Exploring Torque and Deflection Response Characteristics to Evaluate the Ergonomics of Dc Torque Tools Via a Tool Test Rig

Home , Impulse (physics)

EXPLORING TORQUE AND DEFLECTION RESPONSE CHARACTERISTICS TO EVALUATE THE ERGONOMICS OF DC TORQUE TOOLS VIA A TOOL TEST RIG

A Thesis

Presented in Partial Fulfillment of the Requirements for

the Degree Master of Science in the

Graduate School of The Ohio State University

Shritama Mukherji, B.E.

* * * * *

The Ohio State University

2008

Master’s Examination Committee:

Dr. Anthony Luscher, Adviser Approved by

Dr. Carolyn Sommerich

______

Advisor

Graduate Program in Mechanical Engineering

ABSTRACT

Torque tools used in assembly applications generate impulsive reaction forces

during torque build-up that often displace the operator hand and arm, and are associated

with an increased risk of muscle damage and injury. Tools are available in a number of handle shapes, sizes, and output capacities and are operated in various working positions and orientations. These factors affect the dynamic interaction between the tool and the operator and the operator’s ability to react against impulsive forces.

DC torque tools are controlled fastening tools that are instrumented with sensors for

direct measurement of the applied torque and rotation of the threaded fastener during the

assembly process. They have several advantages in terms of torque accuracy, error

detection, and torque verification over other torquing systems. DC torque tools interface

with a tool controller that can be used to set tightening parameters and program different

tightening algorithms, making it highly flexible.

The objective of this thesis is to quantify the ergonomic impact of various DC

torque tool controller settings. This impact was determined by the use of an ergonomic test

rig which will capture the interaction between the physical tool, control software, and a

model of the human arm. The output of the rig is the reaction force and displacement of

ii the tool handle and therefore simulated arm as a function of time. The response curves from the rig were analyzed and a set of metrics were formulated for ergonomic assessment. The work of this thesis will lead to an improved understanding of the interactions between stiffness of the joint to be fastened (joint stiffness), the simulated human arm system, and the tightening algorithms controlling the tool.

iii

DEDICATION

To my parents for their unconditional love and support

ACKNOWLEDGMENTS

With the deepest gratitude, I wish to thank all the people who made this thesis possible.

Firstly, I would like to thank my advisor, Dr. Anthony Luscher for his guidance and support throughout this thesis. I am very grateful to him for all that I have learnt during the course of my graduate studies. I would also like to thank Dr. Carolyn Sommerich for co-advising me on this project and for being a part of my thesis defense committee. Her feedback and encouragement have been invaluable for the successful completion of this thesis.

I would like to sincerely thank Duane Bookshar, Doug Versele and Jim Steverding from Stanley Assembly Technologies. The technical information provided by them has helped me understand DC torque tools. I would also like to thank various individuals in the Department of Mechanical Engineering, namely, Gary Gardner and Neil Gardner for their help in building the ergonomic assessment rig and Joe West for his assistance with the measurement system. Thanks also belong to all the members of the Fastening lab for their ideas and suggestions during this research.

I have been fortunate to have many friends who have supported me and kept me motivated for the last two years. For this, I am very thankful.

Finally I would like to thank my family who made all of this possible. I am grateful to my parents, for having unwavering faith in me and for being a constant source of support and strength. I am also thankful to my sister and brother-in-law, for encouraging me at all times and keeping me in good spirits.

VITA

December 10, 1983……………………. Born – Hyderabad, India

June, 2005……………………………... BE, Visveswariah Technological University

Bangalore, India

January, 2006 - present…………………Graduate Research Associate,

The Ohio State University

FIELDS OF STUDY

Major Field: Mechanical Engineering

Design and Manufacturing

vii

TABLE OF CONTENTS

Page

Abstract...... ii

Acknowledgement...... v

Vita...... vii

List of Tables...... xi

List of Figures...... xii

Nomenclature...... xv

Chapters 1. Introduction ...... 1 1.1 Introduction to bolted joints...... 2 1.2 Torque tool options...... 6 1.3 Motivation for current work...... 10 1.4 Thesis objectives...... 12

2. Literature Review...... 14 2.1 Dynamic models of tool-human operator system...... 14 2.2 Ergonomic injury risk assessment...... 22 2.3 Effect of work station design, operator posture and position...... 26 2.4 Design and application of an instrumented tool handle ...... 29 2.5 Literature summary...... 32

3. Design of ergonomic assessment rig...... 33 3.1 Description of original ergonomic test rig...... 34 3.1.1 Tool and bolted joint assembly...... 35 3.1.2 Human arm model with measurement system...... 37 3.2 Rig improvements...... 39 viii

3.2.1 Improved spring design to represent arm stiffness ...... 39 3.2.2 Design of pneumatic system to drive arm stiffness cylinder...... 43 3.2.3 Improved design of arm mass system ...... 45 3.2.4 Load cell modifications...... 48 3.2.5 Additional modifications...... 50 3.3 Final ergonomic test rig ...... 52 3.4 Repeatability tests ...... 54

4. Experimental method ...... 58 4.1 Description of the factors ...... 59 4.1.1 Tightening algorithm ...... 59 4.1.2 Soft stop feature...... 64 4.1.3 Arm mass and stiffness...... 65 4.1.4 Joint stiffness ...... 67 4.1.5 Summary of factors with their levels...... 68 4.2 Measured response and other dependent variables...... 69 4.3 Design of experiments (DOE)...... 69 4.4 Formulation of ergonomic metrics ...... 73 4.4.1 Torque impulse at different percentages of the target torque ...... 73 4.4.2 Deflection - peaks in positive and negative direction, maximum range.. 76 4.4.3 Reaction torques - peaks in positive and negative direction, maximum range ...... 77 4.4.4 Latency impulse - torque impulse with muscle latency included...... 78

5. Ergonomic assessment of response curves - results ...... 83 5.1 Raw data from screening experiments...... 84 5.2 Assessment of response curves - statistically significant results ...... 90 5.2.1 Torque impulse at different percentages of the target torque ...... 92 5.2.2 Peak deflection negative, peak deflection positive, maximum deflection range ...... 97 5.2.3 Peak torque negative, peak torque positive, maximum torque range.... 101 5.2.4 Latency impulse...... 104 5.3 Comparison of rig curves with curves from human testing ...... 106 5.4 Chapter summary...... 111

6. Discussions ...... 112 6.1 Results of torque impulse...... 112 6.2 Results of deflection peaks and range ...... 115 6.3 Results of torque peaks and range ...... 116 6.4 Results of latency impulse ...... 118 6.5 Chapter summary...... 120

7. Conclusions and future work ...... 121 7.1 Summary of major findings ...... 121 7.1.1 Tightening algorithm...... 122 7.1.2 Joint stiffness ...... 122 7.1.3 Arm mass and stiffness...... 123 7.1.4 Soft stop feature...... 123 7.2 Contributions...... 124 7.3 Recommendations for future work...... 125

List of references...... 128

Appendix A Spring rate analysis of air cylinder ...... 132

Appendix B Design drawings...... 137

Appendix C Data sheets for sensors...... 151

Appendix D Controller program parameter sets...... 166

Appendix E Matlab codes for torque impulse and latency...... 168

LIST OF TABLES

Table Page

3.1 Mean and standard deviation for peak reaction force and deflection from the repeatability tests ...... 57 4.1 Levels of mass and stiffness ...... 67 4.2 Factors with their corresponding levels...... 68 4.3 Orthogonal array ...... 72 5.1 Statistically significant sources for the responses ...... 91 D.1 Explanation of parameter set names...... 166 D.2 Stanley controller parameter values ...... 167

LIST OF FIGURES

Table Page

1.1 Bolted joints classified by external load (a) Tensile joint (b) Shear joint [28]...... 2 1.2 Three phases of bolt tightening operation [35] ...... 3 1.3 Torque distribution of a typical fastener [30]...... 4 1.4 (a) Electric screwdriver [38] (b) Air impact wrench [39] (c) Air angle Nutrunner [40]...... 7 1.5 Cordless tool [41] ...... 8 1.6 (a) Pistol grip (b) Right angle (c) Inline [43]...... 9 1.7 A DC electric nutrunner with a tool controller [44] ...... 10 2.1 Forces acting on a right angle power hand tool [19]...... 15 2.2 Pistol grip tool-operator system represented as a torsional system [12]...... 17 2.3 Average values of human arm parameters for the pistol grip model (a) Torsional stiffness (b) Mass moment of inertia (c) Torsional damping [12]...... 19 2.4 (a) Pistol grip on a vertical surface (b) Inline on a horizontal surface (b) Pistol grip on a horizontal surface (d) Right angle on a horizontal surface [13]...... 20 2.5 “Near” location is 30 cm in front of the ankles, 140 cm above the floor, “Far” location is 60 cm in front of the ankles and 80 cm above the floor [11]...... 22 2.6 Three shut off mechanisms studied by Kihlberg et al [5]...... 24 2.7 Modified Borg’s scales used by Kihlberg et al. [6]...... 25 2.8 “Time-torque” value as defined by Kihlberg et al. [6] ...... 26 2.9 Two postures used in Lindquist’s study (a) Horizontal lower arm (b) Vertical lower arm [15]...... 27 2.10 EMG latency as demonstrated by Oh and Radwin [17]...... 28 2.11 Location of strain gauges on the grip force sensing device [16]...... 30 2.12 Instrumented handles used in Lin’s study (a) Pistol grip tool (b) Right angle tool [9] ...... 31 3.1 Inputs and outputs of the ergonomic test rig ...... 34 3.2 Initial ergonomic test rig design [27] ...... 35 3.3 Tool and bolted joint assembly [27]...... 36 3.4 Bolted joint assembly [27] ...... 36 xii

3.5 Top view of the human arm model with the measurement system [27] ...... 37 3.6 Double acting cylinder (a) Single rod (b) Double rod ...... 41 3.7 Analysis of double acting double rod cylinder ...... 42 3.8 Schematic of pneumatic system...... 44 3.9 Pneumatic control box ...... 45 3.10 Arm mass box to carry arm mass plates ...... 46 3.11 Two sizes of arm mass plates...... 47 3.12 Arm mass box supported on rollers ...... 48 3.13 Device to protect Sensotec model 31 load cell ...... 49 3.14 Arm mass system and load cell assembly...... 50 3.15 LVDT, air cylinder and mass assembly...... 51 3.16 Final ergonomic assessment rig for right angle DC torque tools ...... 53 3.17 Repeatability test plots with Manual Downshift algorithm (a) Deflection (b) Reaction force ...... 55 3.18 Repeatability test plots with ATC algorithm (a) Deflection (b) Reaction force.....56 4.1 Speed and torque control using Manual Downshift [46] ...... 60 4.2 Speed and torque control using Two Stage algorithm [46] ...... 61 4.3 Speed and torque control using ATC [46]...... 62 4.4 Parameters associated two modes of ATC algorithm (a) ATC Automatic mode (b) ATC Custom mode [45]...... 63 4.5 Parameters for the soft stop feature on Stanley controllers [45] ...... 65 4.6 Reaction torque versus time (a) Actual curve (b) Torque impulse 20% (c) Torque impulse 50%...... 75 4.7 Peak deflections positive and negative, maximum deflection range...... 76 4.8 Peak reactions positive and negative, maximum torque range...... 77 4.9 Effect of torque build-up on muscle EMG latency [18]...... 79 4.10 Linear regression between EMG latency and torque build-up time...... 80 4.11 Method used to calculate latency impulse (a) Deflection-time plot (b) Torque-time plot ...... 82 5.1 Response curves with ATC, hard joint, low MK: with soft stop default (a) Reaction torque and (b) Deflection, with no soft stop (c) Reaction torque (d) Deflection ...... 86 5.2 Comparing the three algorithms at medium MK, soft stop default, and hard joint: Reaction torque (a) ATC (b) Manual Downshift (c) Two Stage Control, Deflection (d) ATC (e) Manual Downshift (f) Two Stage Control...... 88 5.3 Reaction torque-time curves with Manual Downshift, medium MK level, soft stop default (a) Hard joint (b) Medium joint (c) Soft joint ...... 89 5.4 Interaction plots for torque impulse at (a) 0 % (b) 20 % (c) 45 % (d) 50 %...... 93 5.5 Interaction plots for torque impulse at (a) 60 % (b) 70 % (c) 75 %...... 94 5.6 Main effect plots at the three controller algorithms (a) Torque impulse 0 % (b) Torque impulse 75 %...... 95 5.7 Main effect plots at the three MK levels (a) Peak deflection negative (b) Peak deflection positive (c) Deflection range ...... 98 5.8 Interaction plots for (a) Peak deflection negative (b) Peak deflection positive

xiii

(c) Deflection range...... 100 5.9 Interaction plots for (a) Peak torque negative (b) Peak torque positive (c) Torque range ...... 103 5.10 Interaction plot for latency impulse...... 104 5.11 Main effect plot for latency impulse at the three controller algorithms...... 105 5.12 Deflection versus time with ATC, hard joint and soft stop default (a) Human operator 1 (b) Rig with medium mass and stiffness...... 108 5.13 Deflection versus time with ATC, hard joint, no soft stop (a) Human operator 1 (b) Rig with medium mass and stiffness ...... 108 5.14 Deflection versus time with Two Stage, soft joint, soft stop default (a) Human operator 1 (b) Rig with medium mass and stiffness ...... 109 A.1 Schematic of double rod cylinder with volume plenums ...... 132 B.1 Device to protect 50 lb load cell from bending loads ...... 138 B.2 Blue tube of load cell protection device ...... 139 B.3 Pink rod of load cell protection device that slides into the blue tube ...... 140 B.4 Orange rod of load cell protection device ...... 141 B.5 Pivot plate of the arm mass box ...... 142 B.6 Arm mass plate that connects to air cylinder ...... 143 B.7 Bottom plate of the arm mass box...... 144 B.8 First slotted plate of arm mass box ...... 145 B.9 Second slotted plate of arm mass box...... 146 B.10 Top plate of the arm mass box...... 147 B.11 Larger size arm mass plate ...... 148 B.12 Smaller size arm mass plate...... 149 B.13 Angle plate at clevis end of the rig ...... 150

xiv

NOMENCLATURE

N Newton mm Millimeters ms Milliseconds lbs Pounds

Nm Newton meter kg Kilogram

MK Mass and stiffness

CHAPTER 1

INTRODUCTION

Fasteners represent complex and critical design elements which are necessary for

the reliability and long service life of machinery and structures. Different types of

fasteners have been developed for specific requirements, such as higher strength, easier

maintenance, greater reliability at different temperatures, lower material and manufacturing costs. The selection and use of a particular fastener is dictated by the design requirements and the conditions under which the fastener will be used. The majority of fasteners used in industry include mechanical fasteners both threaded and non-threaded. Threaded fasteners, mainly bolts, nuts and screws, are used in applications that require components to be disassembled. Other advantages of threaded fasteners include ease of assembly, which generally requires no special equipment and usability on most materials.

1.1 Introduction to bolted joints

Bolted joints can be categorized based on the direction of external loads acting on the joint [28]. The two types of joints are shown in figure 1.1 (a) and (b). If the forces on the joint are parallel to the axis of the bolt, the joint is loaded in tension and is called a tensile joint. If the forces on the joint are perpendicular to the axis of the bolt the joint is loaded in shear and is called a shear joint.

(a) (b)

Figure 1.1: Bolted joints classified by external load

(a) Tensile joint (b) Shear joint [28]

A typical bolt tightening operation occurs in three distinct phases [30] [35]. Figure

1.2 is a plot of torque against fastener rotation showing the three phases of bolt tightening.

Figure 1.2: Three phases of bolt tightening operation [35]

The three phases can be described as follows: i. Rundown: In this phase, prevailing torque which is primarily due to thread friction is

overcome, before the fastener comes into contact with the bearing surface. ii. Alignment: This is the “snugging” zone that occurs when the fastener comes into

contact with the parts being joined. This is a non-linear zone where non-parallel

bearing surfaces may cause the bolt to bend, and coatings, surface roughness and

deforming threads will add to the torque load in an unpredictable manner. iii. Elastic clamping: The actual tightening is done in this phase. A tangent plotted to this

linear portion of the curve and extended back to zero torque locates the “elastic

origin”. The tension in the fastener is directly proportional to the angle of turn in the

region from the elastic origin to the point where torquing was stopped in this zone.

Bolted joints, in effect, behave like two sets of springs [30]. The bolt behaves like a spring in tension as it is tightened and the joint material acts like a spring in

compression while it resists the bolt’s tension. Preload is the tension built-up in the

fastener and is created when torque is applied. During the assembly process the challenge

is to establish the right amount of clamping force between the bolt and the joint members.

There has been no practical and reliable way to determine bolt tension either during the assembly process or afterward. It can be done using special strain gauged bolts or with ultrasonic microphone procedures, but these methods are not practical in a production environment or with very small fasteners. The common methods used to control bolt preload during assembly are [33]: i. Torque control tightening: The most conventional method of controlling the bolt

preload has been to measure the torque applied during the assembly operation [30].

However, most of the tightening torque goes into overcoming friction under the

fastener head and in the threads, as shown in Figure 1.3. In many bolted joints only

10% of the applied torque actually produces the clamp load in the joint.

Figure 1.3: Torque distribution of a typical fastener [30]

In most cases the relationship between torque and preload can be described by the

following equation [36]:

Torque = K X d X F (1.1)

Where K= Nut factor

d = Nominal bolt diameter (in, mm, etc)

F = Bolt preload (lbs., N)

The nut factor, K is a combination of three factors - K1, a geometric factor, K2, a

thread friction factor, K3, an underhead friction related factor. There are published

tables of nut factor values for various combinations of materials, surface finishes,

plating, coatings and lubricants. However for most critical applications it is often

necessary to determine this value experimentally.

ii. Angle control tightening: In this method, the bolt is tightened to a predetermined

angle beyond elastic range. The main disadvantages of angle control tightening lie in

the need for a precise determination of the angle.

iii. Yield control tightening: This method uses wrenches with a control system

instrumented with sensors to measure torque and angle during the tightening process.

The control system is sensitive to the torque gradient of the bolt being tightening and

stops the tightening process when it detects a change in the slope of this gradient. iv. Bolt stretch method: This method involves the measurement of the elastic

deformation that the fastener undergoes as it clamps down. By measuring this stretch

and knowing the physical properties of the bolt and its material composition, the

preload can be calculated.

v. Heat tightening: Thermal expansion characteristics of the bolt are utilized in this

method. When bolt is heated and undergoes expansion, the nut is indexed and the

system is allowed to cool. As the bolt attempts to contract it is constrained

longitudinally by the clamped material and a preload results.

1.2 Torque tool options

Torque tools used for tightening threaded fasteners include hand tools and power

tools. Hand tools such as wrenches and screwdrivers rely on the human operator for

generating force and torque while power tools depend on an external energy source. The

increased use of power tools in numerous assembly applications is due to their ability to

tighten threaded fasteners rapidly, their capacity to generate high torque and their

reliability in achieving target torque levels.

Power torque tools commonly used for securing threaded fasteners are powered

screwdrivers, nutrunners and impact wrenches, shown in figure 1.4. Nutrunners provide tight torque control and are used in precision fastening and assembly applications. They

come in different handle shapes and can provide access in tight quarters. An impact

wrench is a socket wrench power tool designed to deliver high torque output with

minimal exertion by the user. In operation, a rotating mass (the hammer) is accelerated by

the motor, storing energy, and is suddenly connected to the output shaft (the anvil)

creating a high-torque impact. The hammer mechanism is designed such that after

delivering the impact, the hammer is again allowed to spin freely, and does not stay

6 locked [37]. With this design, the operator feels very little torque, even though a very high peak torque is delivered to the socket.

(a)

(b)

(c)

Figure 1.4: (a) Electric screwdriver [38] (b) Air impact wrench [39]

Power tools can be categorized in the following ways:

i. Based on external energy source:

• Pneumatic - Tools driven by compressed air.

• Hydraulic - Tools driven by hydraulic pressure, these are used in the

construction industry for very high torque applications.

• Electric - Tools driven by electricity running through a cable.

• Cordless - Tools that run on rechargeable batteries and can be used in

situations where compressed air or electricity is unavailable or impractical. A

cordless tool is shown in figure 1.5.

Figure 1.5: Cordless tool [41]

ii. Based on type of drive [42]

• Discontinuous drive - Pulse tools are discontinuous drive tools that generate

torque in brief pulses under load. The pulsing allows tool users to benefit from

the advantages of reduced transmission of torque reaction. These tools have

traditionally been hydraulic but there are a few DC torque tools available. 8

• Continuous drive - these tools are gear-driven and continuously supply power

during the entire cycle.

iii. Based on handle shape (figure 1.6)

• Pistol grip

• Right angle

• Inline

(a) (b)

(c)

Figure 1.6: (a) Pistol grip (b) Right angle (c) Inline [43]

Both pneumatic and electric tools can be of different levels of sophistication depending upon the type of application. Less critical applications use torque tools which can be adjusted to different torque levels, but do not have feedback. Critical assemblies use controlled fastening tools, shown in figure 1.7. These tools offer better performance, precision and versatility. Controlled pneumatic and electric tools are instrumented with sensors that allow the direct measurement of both the dynamic applied torque and the angle of rotation of the threaded fastener during the assembly process. These tools interface with a controller which can be programmed to store different fastening 9

parameters and tightening strategies. The controllers display digital torque values

allowing torque verification and also allow data to be collected and recorded. The

controllers can also be connected to a personal computer and be programmed using

proprietary fastening software. Electric tools, of the type described above are called DC

torque tools.

Figure 1.7: A DC electric nutrunner with a tool controller [44]

1.3 Motivation for current work

Power tools have been associated with repetitive and forceful exertions during their use, which often displace the operator hand and arm. Impulsive reaction forces and prolonged exposure to vibration transmitted to the operators of power tools have been related to symptoms of carpal tunnel syndrome (CTS), vibration white finger (VWF) disease, loss of muscle strength, and disorders of the nervous symptom. These disorders belong to a category knows as work related musculoskeletal disorders (WMSD), develop gradually as a result of repeated trauma and are also called cumulative trauma disorders

(CTD). Thus, power tool use, due to its repetitive nature and forceful exertions, has been considered a risk factor for work related musculoskeletal disorders [32].

Various factors affect the dynamic interaction between the tool and the operator and the operator’s ability to react against impulsive forces. Factors that have been studied include tool shape, target torque levels, joint hardness, work location and orientation and operator characteristics [4] [17] [18]. Appropriate selection of the process factors (torque, joint hardness, etc.) and workstation design factors (orientation and distance from operator) is important to maximize performance and quality, while minimizing physical stress.

An important factor in the consideration of DC torque tools is that the tool controller allows enormous flexibility in setting tightening parameters and profiles. For example, the tool speed, torque and angle limits for an acceptable joint, and different tightening strategies, can all be set at the controller. These factors can have a great impact on the dynamic interaction between the tool and the operator and on the operator’s perceptions of exertion and acceptability of the tool. Although there have been several studies on the ergonomics of power tools, there is a limited understanding of the ergonomic impact of tool controller programs and strategies and their interaction with human and joint variables. This project aims to begin to address the above voids.

1.4 Thesis objectives

The purpose of this thesis is to quantify the ergonomic impact of various DC torque

tool controller settings. This impact will be determined by the use of an ergonomic test

rig which will capture the interaction between the physical tool, control software, and a

simulation of a human arm. The rig contains a simulator of human arm mass and

stiffness, and incorporates other input factors such as torque and rotation requirements,

joint hardness, and tightening program parameters. The output of the rig is the reaction

force and displacement of the tool handle and therefore simulated arm as a function of

time. This study involved the following:

i. Modification of an existing ergonomic assessment rig which was designed and

constructed as a part of a student design project [27]. The current study involved an

improved model of the human arm stiffness and mass, the use of a more portable

measurement system, and several mechanical improvements.

ii. Investigation of how three controller algorithms interact with human arm parameters

and joint variables. The DC torque tool and tool controller used in for this thesis was

manufactured by Stanley Assembly Technologies. The three speed management

algorithms that were studied were Manual Downshift, Two Stage Control and

Adaptive Tightening control (ATC). iii. Analyses of the response curves obtained from the rig to estimate the ergonomic

impact on the body for a specific set of arm parameters. A set of metrics were

developed for ergonomic assessment including peak reaction torque values, peak

deflection values, range of handle movement, area under the torque-time curves, etc.

The ergonomic assessment rig used in this thesis incorporates several variables and produces a single curve that combines the effects of all of these. This reduces the number of factors needed for ergonomic assessment and leads to an abstraction of the results.

This thesis does not intend to study the effect of different types of tools, arm positions, or associate population variations. Associates may also be fatigued over an extended period of time, and this factor is not incorporated in the force and deflection curves from the assessment rig. The work of this thesis will lead to an improved understanding of the interactions between stiffness of the joint to be fastened (joint stiffness), the simulated human arm system, and the program algorithms controlling the tool, which could eventually thus help minimize injury associated with the use of these tools.

This thesis is organized in the following manner. Chapter 2 discusses the existing literature on the ergonomics of power tools. Chapter 3 describes the design of the ergonomic assessment rig. The experimental methods used in this study are explained in

Chapter 4, and the ergonomic metrics used for assessment are introduced. Chapter 5 presents the results of the experiments conducted using the ergonomic rig, which are discussed in detail in chapter 6. The conclusions and contributions of this thesis are stated in Chapter 7, with recommendations for future research.

CHAPTER 2

LITERATURE REVIEW

This thesis has benefited from several studies conducted on the ergonomics of

power tools. This chapter gives a description of the literature pertinent to the current

work, and has been divided into four sections. The first section focuses on dynamic

models of the tool-operator system that were developed to quantify human arm

parameters. The second section describes studies on ergonomic injury risk assessment.

The third section talks about the research on the ergonomic effects of work station design

and operator posture. The fourth section describes the design and application of an

instrumented tool handle that allows direct measurement of grip forces and moments

acting on the handle.

2.1 Dynamic models of tool-human operator system

Reaction force acting against the hand was estimated under the conditions of static equilibrium by Radwin et al [23]. Static hand reaction force was given by the spindle

torque divided by the handle length as (figure 2.1):

Tnut FHz = (2.1) LHy

Where FHz = hand reaction force in z direction

Tnut = torque

LHy = length of the tool

If excessive hand movement occurs during tool operation, the hand force estimated by the

static model may be less accurate because of inertial effects. The authors Oh, Radwin and

Fronczak proposed a dynamic model to provide a more accurate estimation of the

reaction force [19]. This model, shown in figure 2.1, was based on physical tool

parameters and can be used to calculate the hand reaction force.

Figure 2.1 Forces acting on a right angle power hand tool [19]

The dynamic hand forces and moments were described by the following equations:

∑MWLzTTyHHyHxHy: + WL− FL =0 (2.2)

2 ∑MTx :− nut+=+α FL Hz Hy( I tool mL H Hy) tool (2.3)

Hand reaction force FHz and tool support force FHx can be solved from equation 2.2 and

2.3:

1 FWLWLHxTT=+(yHHy) (2.4) LHy

1 2 FImLTHz =+α+⎣⎡()tool H Hy tool nut ⎦⎤ (2.5) LHy

Where M = moment, W = weight, I = moment of inertia, m = mass, α = angular

acceleration and subscript T = tool.

The model assumes the hand and lower arm to be a point mass applied at the

handle. The forces calculated using the above equations were compared with the hand

reaction force measured directly using a strain gauge attached to the tool handle. Subjects

used the tool with three target torque levels (25, 40 and 55 Nm) and five different torque

build up times between 35 and 900 ms, so that the effects of target torque and joint

hardness on reaction force could be estimated. Direct force measurements showed that

the dynamic model overestimated the peak hand force by 9 %. The model proposed did

not account for different postures and positions of the operator which could affect the

force components, and this was stated as a plausible reason for the overestimation. Peak

hand force was the least for the hard joint and greatest for the medium hardness joint (150 ms build up time). Peak force increased by 76 % as target torque increased from 25 to 55

Nm. Comparing the results from the static and dynamic equations showed that the static

model overestimated the hand force; the error ranged from 10 % for a soft joint to 40 %

for a hard joint. This was because the static model did not include the inertia of the tool

which played a major role in reducing hand reaction force.

Lindqvist hypothesized that mass-spring-damper mechanical system could be used

to describe the handle response to impulsive reaction forces encountered in nutrunner operation, but did not identify specific parameters for these elements [15]. Lin et al. developed a similar biomechanical model in which a pistol grip tool operator is represented as a torsional system [12]. The hand and arm elements are represented as equivalent mass moment of inertia, rotational stiffness and damper, as shown in figure

2.2.

Figure 2.2: Pistol grip tool - operator system represented as a torsional system [12]

The equation of angular motion describing this system with a torque input of T (t) is:

dd2θθ ()JJsubject+++ tool csubject + kT subject =()t (2.6) dt2 dt

Where: Jsubject = effective mass moment of inertia of the hand and arm.

Jtool = mass moment of inertia of the tool.

csubject = rotational damping for the hand and arm

ksubject = torsional stiffness for the hand and arm

The values of these elements were determined by measuring the free vibration frequency and amplitude decay of a known mechanical system when externally loaded by the human arm. The effect of gender, horizontal distance, and vertical distance from the ankles to the handle was tested. The model was able to predict the hand force and handle displacement as a function of the human arm parameters and the input torque. The model predictions were validated using actual tool operation.

The results shown in figure 2.3 are the average human arm parameters for 25 subjects. The bars represent one standard deviation. The plots demonstrate that the values of human arm parameters vary between operators and are also affected by work place conditions.

(a)

(b)

(c)

Figure 2.3 Average values of human arm parameters for the pistol grip model

(a) Torsional stiffness (b) Mass moment of inertia (c) Torsional damping [12]

The model predictions for handle displacement had a correlation of 0.88 with the actual measurements. The model under predicted the handle displacement by 27%. The experiment to determine stiffness, mass and damping parameters used maximal exertions by the operators, but it was unlikely that the tool operators used their maximum

capability during actual tool operation and this could be a possible reason for the under

prediction.

Lin et al [13] used a mechanical vibrating apparatus similar to that used previously

by the authors [12] to quantify mechanical model parameters for operation with various

tool shapes such as in-line, pistol grip and right angle. The different handle

configurations are shown in figure 2.4.

(a) (b)

Figure 2.4: (a) Pistol grip on a vertical surface (b) In-line on a horizontal surface (c) Pistol grip on a horizontal surface (d) Right angle on a horizontal surface [13]

The tests involved both male and female subjects. Test results showed a decrease

in stiffness as the horizontal distance between the tool and the operator increased. Males

had greater arm parameter values than females. Thus the parameters obtained in [13] and

[12] can be used to model the operator arm response to power tool operation for different hand tool and work place designs.

Previous studies by Lin et al [12] [13] used maximum operator exertion levels to estimate mechanical parameters and thus the model under-estimated the actual handle displacement. A subsequent study by the same authors used normalized forearm flexor muscle group electromyography (EMG) to adjust the mechanical stiffness parameter

[14]. Subjects were asked to exert the maximal force before each experimental condition to determine their maximum voluntary contraction (MVC) using EMG. The muscle was

assumed to maintain constant stiffness 50 ms prior to torque build up, and the EMG from

this phase was normalized to MVC to assess actual exertion during tool operation. This

resulted in an improved correlation (r = 0.98) between the measured and predicted handle

displacement and reduced the model prediction error from 27% [12] to 3%.

Further applications for the mechanical model developed were explored by Lin,

Radwin and Nembhard [11]. A right angle and pistol grip tool were operated by 25

subjects at two discrete work locations described as “near” and far”, shown in figure 2.5.

This model could be used to predict group means and variations of handle displacement

and force for a given tool configuration. For instance, the pistol grip nutrunner used on

horizontal surface at 30 cm in front of the ankles and 140 cm above the floor resulted in a

predicted mean handle displacement of 39 mm for males. The study also describes an interpolation method which can be used to calculate subject parameters at any work location expressed as a linear combination of the work locations used in the study.

Figure 2.5: “Near” location - 30 cm in front of the ankles, 140 cm above the floor,

“Far” location is 60 cm in front of the ankles and 80 cm above the floor [11].

2.2 Ergonomic injury risk assessment

Power tool use has been associated with repetitive and forceful exertions associated with increased risk for musculoskeletal disorders. It is important to control the forces acting against the operator’s hands to reduce the risk of injuries, disorders and muscle fatigue. Several authors have investigated the factors that can influence the reaction force due to power tool operation.

Johnson and Childress [4] investigated the effect of target torque, tool grip diameter, type of tool, tool shape and tool weight on the operator. Electromyography and subjective evaluations of the operators were used to assess operator response. Torque was the most significant factor in the amount of effort and stress associated with a tool and subjects indicated a preference for the tools set at lower torque level. Tool grip diameter

22 was significant only at higher torque levels, with small diameters resulting in higher

EMG levels and lower subject preferences. The effect of tool weight was not significant in the analysis of EMG levels (p > 0.05) though subjects preferred lighter tools.

Another study that used EMG and subjective ratings to determine operator exertion was by Frievalds and Eklund [2]. The factors that were studied included different work orientations, joint stiffnesses, type of tool, tool speed, air pressure levels and handle configurations. Peak reaction torque was measured using a torque transducer and torque impulse was calculated as the area under torque-time curve. The subjective ratings were significantly correlated (p < 0.05) to the peak reaction torques, torque impulse and the EMG levels. The results showed that running electric tools at lower speeds, pneumatic tools at lower pressures and using a soft joint resulted in larger impulses and more stressful operator ratings.

Radwin et al. [23] used EMG to study the reaction force acting against the hand, forearm muscle activity and grip force for operators of right angle air shut off nutrunners.

The independent variables for this test were four tools with increasing torque output capacity ranging between 30 Nm to 100Nm, which were operated at two torque build up times (0.5 and 2 seconds). Peak hand force increased with increasing tool output capacity which is consistent with the results of Johnson and Childress [4]. Average grip force was

50 N greater for the shorter torque build up time.

Kihlberg, Lindbeck and Kjellberg discuss methods to assess torque reaction associated with pneumatic nutrunners in three separate studies. In the first study [5], three nutrunners with the same pre-set spindle torque of 75 Nm, but different shut-off

23 mechanisms - fast, slow and delayed (figure 2.6), were studied. The test setup included a force platform that measured ground reaction forces between subject and the floor. Other performance measures included muscle activity, hand arm motion and discomfort ratings.

Fast shut off nutrunners gave the smallest handle displacement and force, while delayed shut-off gave the largest displacement and force. EMG measurements showed no significant relationships among the three tools. The tool torque impulse according to ISO

6544 [25] is defined as the area under the torque-time curves above a threshold torque equal to 10% of the target torque. Discomfort ratings were weakly correlated with torque impulse values (r = 0.74).

Figure 2.6: Three shut off mechanisms studied by Kihlberg et al. [5]

The second study by Kihlberg et al. [6] tested nine angle nutrunners with three different preset torques (25, 50 and 75 Nm) and different shut off mechanisms tested in

[5]. Reaction force, hand arm displacements were measured and subjective ratings were obtained using a modified Borg’s strength and discomfort scales shown in figure 2.7.

Strength Discomfort

10 Extremely strong 20 Almost unbearable 9 19 8 18 Extremely discomforting 7 Very strong 17 6 16 5 Strong 15 4 14 Very discomforting 3 Moderate 13 2 Weak 12 1 Very weak 11 0.5 Extremely weak 10 0 Nothing at all 9 Rather discomforting 8 7 6 5 4 Somewhat discomforting 3 2 1 Hardly discomforting at all 0 No discomfort at all

Figure 2.7: Modified Borg’s scales used by Kihlberg et al. [6]

The authors also proposed a method to calculate a parameter called “time - torque value” which was the time period during which the torque exceeded 75 % of the present torque, multiplied by the torque. This is the shaded area shown in figure 2.8. Strong correlations between subjective ratings and displacements (r = 0.977) and ratings and vertical ground reaction force (r = 0.987) were found. Subjective ratings had strong correlations with the torque impulse values (r = 0.945) calculated according to ISO 6544 and the time-torque value (r = 0.962).

Figure 2.8: “Time - torque” value as defined by Kihlberg et al [6]

A third study by Kihlberg et al. [7] aimed to test the results of their previous work in an industry setting with experienced workers and to establish acceptability limits for ratings, tool handle displacements and reaction forces. The results indicated that no subject would accept to work a whole workday at a discomfort over 9 on their 20 point scale (figure 2.7). It was also concluded that for a tool to be accepted by 90% of the operators, it should produce handle displacement of less than 30 mm.

2.3 Effect of work station design, operator posture and position

The human arm parameters quantified by the single degree of freedom mechanical model show that the arm mass, stiffness and damping values are affected by the work location. The operator’s ability to resist the forces from power tool use will depend on his posture, position and work station design as investigated by the studies below.

Lindqvist [15] tested two different postures using a right angle nutrunner, one with a horizontal lower arm and other with a vertical lower arm, as shown in figure 2.9. The tests were done on a hard joint and a medium soft joint. The results show a higher

26 displacement for the tests with a vertical lower arm, thus showing the influence of arm posture on the handle displacement during the tightening sequence.

(a) (b)

Figure 2.9: Two postures used in Lindquist’s study

(a) Horizontal lower arm (b) Vertical lower arm [15]

Oh and Radwin’s [17] study on right angle nutrunner operation included two categories of independent variables. The first was the process factors determined by two target torque levels, 25 Nm and 50 Nm and two joint hardnesses characterized by torque build up times of 35 ms (hard joint) and 900 ms (soft joint). The second category was the work station design factors which included orientation (vertical and horizontal) and operator distance (10 cm and 35 cm) from the tool. Handle displacement and velocity, work done on the tool - arm system, and muscle EMG activity were used as dependent variables. One objective of this study was to determine conditions that minimize tool handle instability. Peak handle velocity and peak handle displacement were used as indicators of work done by the operator (positive work) or work done against the operator

(negative work). The handle was most stable when torque was 25 Nm, when vertical

27 workstations were closest and horizontal workstations were farthest. The work done against the operator was lower for the hard joint. Subject EMG measurements show a burst of muscle activity after the onset of torque build up, as seen in figure 2.10. Oh and

Radwin describe a method to determine the start of this event and also define EMG latency as the time difference from the start of torque build up to the onset of the muscle activity burst.

Figure 2.10: EMG latency as demonstrated by Oh and Radwin [17]

Another study by Oh and Radwin [18] uses three levels of target torque (25, 40 and 55 Nm), five torque build up times between 35 ms and 900 ms, horizontal and vertical orientation. The study showed higher handle stability for the horizontal orientation demonstrating that a horizontal work station is preferable for right angle tool

28 use. Higher target torque resulted in greater muscular exertion, similar to the conclusions by Johnson and Childress [4] and Radwin et al [23].

2.4 Design and application of an instrumented tool handle

Direct measurement of the grip forces and moments applied during hand tool use is an important aspect of ergonomic evaluation. Different approaches to the measurement of force at the hand-handle interface have included the use of pressure sensitive materials

[3] and EMG [22]. These methods have been ineffective in capturing real time grip force data and are limited in their use with different handle shapes.

McGorry’s work [16] describes a device that is capable of directly measuring grip forces and moments exerted while using hand tools. The hand tool analysis system consists of a grip force sensing core, which is a symmetrical arrangement of three beams with strain gauges fixed to each end of the beams, as illustrated in figure 2.11. Handles of various shapes and sizes can be mounted to the grip core.

Figure 2.11: Location of strain gauges on the grip force sensing device [16]

The device was configured as a knife and evaluated in a laboratory simulation of a

meat cutting task. The tests indicated that the device had a working range greater than

700 N for grip force and 28 Nm and 16 Nm for the two applied moment axes. The system

had good linearity (r2 = 0.999) with negligible hysteresis and creep and possessed a flexible design.

The work of Lin et al [9] was the first study to used the grip force sensors

described above to investigate how work location and joint hardness effect power tool

operator response. The tools used were equipped with a simulated handle that contained a

grip force sensing core instrumented with strain gauges. The simulated handle positions

for the pistol grip and right angle tool are shown in figure 2.12. The results of this study demonstrate that a tool used on a specific joint results in different handle displacements depending on the working postures. The study provides quantitative measures of handle displacement and grip force at different work locations which can be used to design work

stations and select appropriate tool-task combinations. Another study by Lin, McGorry

and Chang [8] extends the application of the grip force sensor to an inline tool and further

tests the impact of all three handle shapes on different working postures and joint

hardness.

(a) (b)

Figure 2.12: Instrumented handles used in Lin’s study

(a) Pistol grip tool (b) Right angle tool [9]

2.5 Literature summary

Previous studies on the ergonomics of power tools can be summarized in the

following manner. A single degree of freedom model was developed for common power

tool shapes and the human operator was represented as a mass, spring, and damper system. Model parameters were proposed for specific work conditions and the response predicted by the model was validated using actual tool operation. Other studies used several criteria to identify operator discomfort and exertion that results from power tool use such as subjective ratings, ground reaction force, handle displacement, muscle activity from EMG. In addition, different postures and work locations have been explored providing quantitative information that can be used to design work stations and select the appropriate tool for the task.

This thesis is motivated by the existing work on power tool ergonomics and the need to better understand the interaction between the tool and the operator. The ergonomic test rig that will be used in this project is built on the single degree of freedom mechanical model developed by Lin, Radwin et al [11] [12] [13]. The factors that will be studied include target torque, joint hardness and human arm values with a right angle nutrunner. The operator mass and stiffness will be based on the work of Lin and his colleagues for a right angle nutrunner used in a horizontal location [11] [13]. Tool programming strategies will be included as a new independent variable and its effect on the human arm model will be investigated.

CHAPTER 3

DESIGN OF ERGONOMIC ASSESSMENT RIG

This project uses a stationary DC torque tool ergonomic test rig which is based on the dynamic model of the tool-operator system formulated by Lin et al. [11] [12] [13].

The rig contains a model of the human arm in which the hand and arm elements are represented by an equivalent mass and stiffness. It also incorporates other inputs, shown in figure 3.1, which include the target torque, joint hardness and controller program parameters. The output of the rig is the reaction force and displacement of the arm as a

function of time and these response curves will be analyzed to provide an ergonomic assessment. One key advantage of this rig will be its repeatability which will eliminate

the variability associated with human subject testing as seen in some of the papers [12]

[13].

This chapter begins with a description of the initial ergonomic test rig that was

built as an undergraduate student project, the second section talks about the changes

made to the human arm model, measurement system and other components of rig, and the

third section displays the final ergonomic test rig used for this project. The results of the

repeatability tests conducted are presented in the final section.

Figure 3.1: Inputs and outputs of the ergonomic test rig

3.1 Description of the original ergonomic test rig

An ergonomic assessment rig was designed and built during a student design project conducted with Honda to investigate the impact of DC tools on safety and ergonomics by considering factors such as joint hardness and controller program algorithms by different tool manufacturers [27]. This test device, shown in figure 3.2, was modular in construction so as to accommodate different tool lengths, but was designed specifically for assessing right angle tools.

Figure 3.2: Initial ergonomic test rig design [27]

This ergonomic test rig consisted of two major systems - the tool and bolted joint assembly, and the human arm model with the measurement system. The various components of these systems are described below.

3.1.1 Tool and bolted joint assembly

The tool and bolted joint assembly is shown in figures 3.3 and 3.4. The tool used was a Stanley DC right angle torque tool (model number E44LA19-70) rated at 70 Nm.

The right angle head of the tool was supported by a shoulder bolt so that only a rotational degree of freedom remained. The tool socket rested on a drive plate and the fastener head was placed between the socket and the drive plate and was free to rotate. The bolted joint also consisted of a torque plate that moved vertically as the fastener was tightened by the

tool (figure 3.4). The joint hardness could be varied by placing Belleville washers

between the two plates either in series or parallel. The other end of the tool handle was connected to the human arm model.

Shoulder bolt Right angle tool

Drive plate

Figure 3.3: Tool and bolted joint assembly [27]

Belleville washers to adjust joint stiffness Drive plate

Torque plate Fastener

Figure 3.4: Bolted joint assembly [27]

3.1.2 Human arm model with measurement system

The human arm model, along with the measurement system, rested on an aluminum table, as shown in figure 3.5. The tool handle was held by U-bolts to the mass system. The plate attached to the U-bolts was pivoted to allow the handle to rotate during tool operation. The human arm mass consisted of an aluminum box to which steel plates were held by C clamps to model different arm masses. The human arm stiffness was modeled by an air spring whose stiffness could be varied by changing air pressure and volume. The air spring threaded into a load cell which measured tension and compression force. A linear variable differential transformer (LVDT) was connected to the arm model to measure handle displacement. The sensors were connected to an Instron measurement system to obtain force and displacement measurements in the time domain.

Air spring Mass system LVDT

U-bolts

Load cell Tool handle

Figure 3.5: Top view of human arm model with the measurement system [27]

The entire set-up, consisting of the tool and bolted joint assembly and human arm model rested on a steel optical table with a grid of threaded holes on its surface which allowed the components of the rig to be bolted down.

Several improvements to the existing test rig marked the beginning of this project.

These modifications aimed to address some of the drawbacks of the rig which are explained below. i. The model of human arm stiffness had to simulate arm motion in forward and

reverse directions, but the air spring did not function well in tension and would

bottom out if stretched too much [27]. Also, based on previous studies by Lin and

Radwin [11] [13], arm stiffness is assumed to be constant during tool operation. But

the air spring resulted in a non-linear stiffness curve over its displacement range.

Thus, there was a need for an improved arm stiffness system. ii. The present method of varying the arm mass using steel plates and C- clamps was

cumbersome and also limited in the range of arm masses that could be obtained. It

was necessary to design a more convenient method of incrementing arm mass and

also one that would satisfy a wider range of values. iii. The purpose of the load cell was to measure the reaction force on the operator hand

during tool operation. The load cell in the original test rig was located behind the

arm mass and at a considerable distance from the tool handle. As the rig was

originally configured, the load cell was also measuring the inertia of the arm which

is not what is required. Thus, it was necessary to position the load cell on the other

side of the mass and closer to the tool handle. iv. The rig used the Instron system for calibration and data acquisition, which limited

its portability. A more compact and convenient data acquisition system was needed.

3.2 Rig improvements

The following modifications were made to the rig throughout the course of this research and resulted in the final design of the ergonomic test rig.

3.2.1 Improved spring design to represent arm stiffness

Some of the requirements of the new spring design were a larger displacement range and an ability to simulate eccentric and concentric motion of the human arm. A spring whose spring rate could be modified by controlling a limited number of variables was needed to model different human arm capabilities. Due to the assumption of constant arm stiffness, the spring also had to meet conditions of a constant spring rate over its entire range of displacement.

There were a few alternatives for the new arm stiffness system. Using different combinations of mechanical springs in series and parallel to vary the spring rates was one alternative. Another option was to use a pneumatic cylinder as a linear actuator, which can be driven by pressure differential in the cylinder chamber. Since one of the requirements of the arm stiffness system was to produce a large number of spring rates with minimum design changes, a pneumatic cylinder was chosen as a replacement for the

air spring. Combining mechanical springs in different series and parallel configurations

would be more complicated, while an air cylinder of specific bore and stroke dimensions

could be used with varying internal pressures and external volume capacities to produce different stiffness curves. Appropriate selection of these factors can ensure the linearity of the spring rate within the desired range. A double acting cylinder was selected as both sides of the piston can be pressurized to extend and retract the piston rod, thereby modeling the movement of the human arm in forward and reverse directions.

An initial analysis of the piston movement and the resulting force - displacement curves was done using a single rod double acting cylinder. The piston was positioned at the midpoint of its stroke initially so that the maximum displacement of the rod in both directions is equal. Both sides of the piston are pressurized equally to ensure equal spring rates in forward and reverse strokes. However in a single rod cylinder, the effective working area of the rod side of the piston is lesser than that of the other side (figure 3.6

(a)). Thus, the forces on the two sides are unequal and the resultant force on the piston is

not zero in its initial position. The only way to have the piston balanced at the midpoint is

to either have different pressures on the two sides or use a double rod double acting

cylinder, as shown in figure 3.6 (b). A double rod cylinder was chosen as this would

eliminate additional pressure calculations to keep the piston balanced.

Area 1 Area 2

(a)

Area 1 ≠ Area 2

Area 1 Area 2

(b)

Area 1 = Area 2

Figure 3.6: Double acting cylinder (a) Single rod (b) Double rod

The dimensions of the air cylinder were decided by calculating the spring rate from the force-displacement curves for different bore and stroke specifications. This analysis was based on the ideal gas law according to which pressure, volume and temperature are related by the equation:

PV = n R T (3.1)

Where P = absolute pressure (Pa)

V = volume (m3)

n = number of moles of the gas

R = Universal gas constant (8.3143 m3 Pa/mol -K)

T = absolute temperature (K)

The basis of calculations for the spring rate analysis is explained below with help of

figure 3.7.

Piston at mid-point

Piston rod of stroke

Pin Pin

F1 F2 Side 1 Side 2

Figure 3.7: Analysis of double acting double rod cylinder

Initially the pressure on both sides of the piston is equal to the inlet pressure Pin and the piston is at the mid-point of its stroke. Since the pressures and volumes are the same on both sides, the piston is balanced. The number of moles of air on each side of the piston can be calculated by the ideal gas equation as:

n = Pin Vin/RT (3.2)

where T is equal to room temperature (298 K). It is assumed that the piston is perfectly

sealing and gas does not leak from one side of the piston to the other so that the number

of moles remains constant at all times. Now suppose the piston moves a certain distance x

towards side 2. Then the volumes and pressures on both sides are no longer equal. The

volume on side 2 is lesser than the volume on side 1, and so pressure on side 2 is greater

than the pressure on side 1 .This produces an unbalanced force on the piston (F2 − F1) which is the difference in pressure multiplied by the effective area. This resultant force is plotted against the displacement of the piston and the spring rate is calculated from the force-displacement curve.

The complete analysis of the air cylinder and algorithm developed to relate inlet line pressure to spring rate is described in Appendix A. The final specifications for the arm stiffness system consisting of the air cylinder and volume plenums are as follows:

• Air cylinder bore equals one and one - half inches, stroke equals four inches.

• Two volume plenums of eight cubic inches capacity connected to either side of

the piston.

The pneumatic cylinder and volume chambers were manufactured by Clippard

Minimatic (models SDD-24-4 and AVT-24-8 respectively). This arm stiffness system is capable of producing spring rates from 1500 N/m to 7900 N/m which would cover Lin’s stiffness values ranging from 1200 N/m to 3200 N/m [11].

3.2.2 Design of pneumatic system to drive arm stiffness cylinder

The completion of the arm stiffness design was followed by a lay-out of the pneumatic system that would drive the cylinder. The air cylinder connected to two volume chambers, one on either side of the piston, constitutes the arm stiffness system.

At the start of each run, the piston is at the mid-point of its stroke to allow equal displacement in forward and reverse directions. Each run begins with equal pressure on

43 both sides of the piston, so that the spring rates in the forward and reverse strokes are the same.

Figure 3.8: Schematic of pneumatic system

The schematic of the pneumatic system and the plumbing connections is shown in figure 3.8. There are three pressure gauges in total; two gauges are connected to the two sides of the air cylinder through the volume plenums, and the third gauge is connected to a pressure regulator through the shut-off valve denoted as valvecenter. The pressure regulator is adjusted manually to a pressure corresponding to a specific spring rate, and this pressure is read off the center gauge. Compressed air from the inlet line flows through the regulator into the system when valvecenter is open. The air then flows through valve1 and valve2 each connected to one side of the piston. To pressurize the cylinder all 44

three valves are open initially, and the three gauges show the same read out. Before each

run the arm stiffness system (cylinder and volume chambers) is isolated from the inlet

lines by closing valve1 and valve2.

A pneumatic control box was built to enclose all the fittings and tubing. As seen in

figure 3.9 the pressure gauges were mounted on the top plate and a sheet metal front

cover helped in concealing all the plumbing connections.

To air cylinder To air cylinder

Figure 3.9: Pneumatic control box

3.2.3 Improved design of arm mass system

The original test rig used steel plates and C clamps to add and vary the mass of the human arm model. Although this method worked satisfactorily, it restricted the range of masses that could be obtained and also was limited in its usability. A new mass system was required which would allow mass values to be varied conveniently during

45 experimentation. One alternative for the mass system was using a threaded rod and adding hollow discs of different dimensions to it. Another option was to use a slotted box and inserting steel plates of different dimensions. The design calculations showed that using the hollow discs to add mass limited the range of values that could be attained.

Thus, the slotted aluminum box design was chosen. The new mass system used an aluminum box with a partially open top as shown in figure 3.10. This box contained slots into which steel plates could be inserted and bolted. A combination of steel plates of different dimensions would add up to the desired mass. The final design of the arm mass system had seven slots and the steel plates used could vary the mass from 3.3 kilograms to 7.5 kilograms. Two sizes of arm mass plates were used which are shown figure 3.11.

The drawings for the components of the arm mass system are included in Appendix B.

Figure 3.10: Arm mass box to carry mass plates

4” X 3.75” X 3/8” 2.5” X 3.75” X 3/8”

Figure 3.11: Two sizes of arm mass plates

The arm mass box was located next to the load cell and was supported on three

rollers so as to avoid imposing bending moment on the sensor (figure 3.12). The plate on the side of the load cell was pivoted at the top and bottom to allow the rotary motion of the tool handle. The mass system rested on a plate whose height could be adjusted by jacking screws threaded into the main aluminum table that supported the human arm model.

Pivot

Roller Adjustment plate

Figure 3.12: Arm mass box supported on rollers

3.2.4 Load cell modifications

A Sensotec model 45A fatigue rated pancake load cell with a range of +/- 500lbs was used for force measurement in the initial rig. The location of this load cell was a cause of concern since it was positioned behind the arm mass system at a considerable distance from the tool handle. The response that needed to be measured was the reaction force between the tool handle and the human arm. The load cell in its existing location was also measuring the inertia of the arm which was not required, and so it had to be moved closer to the tool handle on the other side of the mass system. The 500lb load cell was replaced by a model 31 miniature load cell also from Sensotec; the range was

lowered to +/- 50 lb (+/- 222 N) to increase the resolution as a percentage of full scale

output.

Moving the load cell closer to the tool handle increased its proximity to the arm mass system and made it susceptible to bending loads. The model 31 load cell can withstand a maximum bending moment of 8 in-lbs (0.9 Nm) without permanently damaging it, and the bending moment from the arm mass system was several times this limit. Due to this concern a device was designed to isolate the load cell so that it measures only axial loads and is protected from the bending moment. Its major components were an inner round rod and an outer hollow tube which react out bending moment as a couple. The load cell is threaded to these parts as shown in figure 3.13. The drawings for the various parts of this device are shown in Appendix B.

To tool handle To mass box

Load cell

Figure 3.13: Device to protect Sensotec model 31 load cell

The load cell protection device was located between the tool handle and the arm mass box and is shown in figure 3.14. It is threaded to the plate that holds the tool handle on one end. The other end is bolted to the pivoted plate of the arm mass box. 49

Figure 3.14: Arm mass system and load cell assembly

3.2.5 Additional modifications

i. The LVDT was mounted on top of the air cylinder since this would measure the

movement of the piston rod more accurately. The original location of the LVDT

also caused the core rod to bend due to its movement, while locating it above the

cylinder would eliminate this problem. The LVDT and the air cylinder threaded

into one of the plates of the arm mass box as shown in figure 3.15. ii. The other end of the air cylinder is threaded into a hollow connecting piece which is

connected to a clevis (figure 3.15). This allows the cylinder to move about the

clevis axis when the tool is run. The clevis is bolted to an angle plate which is

fastened to the aluminum table supporting the entire human arm system.

Clevis LVDT Air cylinder

Figure 3.15: LVDT, air cylinder and mass assembly

iii. The Instron controller was replaced by a portable National Instruments data

acquisition and signal-conditioning system. This system has a NI SCXI-1520

module consisting of eight channels capable of providing 0-10V excitation for each

channel independently. The SCXI-1600 data acquisition module is used with the

SCXI-1520 to provide data acquisition and control capabilities. The SCXI 1314

terminal block mounts to the front of the module and provides connections to

sensors at the screw terminals located within a fully shielded enclosure. The

National Instruments system communicates with a computer via a USB connection

and is interfaced with LabView software which displays the voltage reading from

the sensors and also records the data as a text file.

3.3 Final ergonomic test rig

The fully assembled ergonomic assessment rig is shown in figure 3.16.

Arm stiffness Arm mass LVDT cylinder system Load cell

53 Test joint

Stanley DC torque tool

NI Data acquisition Pneumatic control box system

Figure 3.16: Final ergonomic assessment rig for right angle DC torque tools

3.4 Repeatability tests

Following the completing of the ergonomic assessment rig, it was important to test

its repeatability. The protocol for the repeatability tests is given below, the speed control

algorithms and soft stop feature are described in detail in chapter 4.

i. A medium-hard joint, requiring around 120° of rotation from snug to tight was

used, where snug torque was defined as 10 % of the target torque.

ii. The target torque was set to 60 Nm for all runs. The entire set of controller

parameters can be found in Appendix C. iii. The tests were conducted at two tightening algorithms, Manual Downshift and

Adaptive Tightening Control (ATC). The tightening algorithms used in this thesis

are described in section 4.1.1. iv. The soft stop feature was used with the following timer values, which are Stanley’s

recommended values:

- Current off = 0.001 seconds.

- Current hold = 0.025 seconds.

- Current ramp = 0.075 seconds.

A detailed description of the soft stop feature is provided in section 4.1.2.

v. For the human arm model, the arm mass was set at 4.46 kilograms, and the

stiffness at 4150 N/m, which were chosen to represent a medium level of arm mass

and stiffness. vi. Ten replicates were done at each control algorithm and the runs were randomized.

The force-time and deflection-time plots from the repeatability tests are shown below. Figure 3.17 shows the plots with Manual Downshift and figure 3.18 are for the runs with ATC.

(a)

(b)

Figure 3.17: Repeatability test plots with Manual Downshift algorithm (a) Deflection (b) Reaction force 55

(a)

(b)

Figure 3.18: Repeatability test plots with ATC algorithm (a) Deflection (b) Reaction force

It can be seen from these figures that the reaction force and deflection plots are

highly repeatable in their shape and the magnitudes of the peaks. For both Manual

Downshift and ATC algorithms, the reaction force plots contain one large peak at around

-100 N and a second peak around -70 N. The reaction force rebounds to about 10 N. The deflection plots in figure 3.17 (a) have peak values between -14 and -16 millimeters and the rebound deflections are around four millimeters. The deflection peak values with

ATC are also repeatable and range from -14 to -16 mm. The means and standard

deviations for the peak force and deflections for the runs with the two algorithms are displayed in table 3.1. The low coefficients of variability values validate the repeatability of the ergonomic assessment rig.

Reaction force (N) Deflection (mm) Algorithm Mean Standard Coeff. Of Mean Standard Coeff. Of deviation variability deviation variability Manual Downshift −102.6 2.8 2.7% −15.487 0.842 5.4%

ATC −102.57 1.93 1.8% −15.65 0.747 4.7%

Table 3.1: Mean and standard deviation for peak reaction force and deflection from

the repeatability tests

CHAPTER 4

EXPERIMENTAL METHOD

This chapter describes the experimental efforts undertaken to study the interaction between the tool controller settings, joint stiffness and the human arm model using the ergonomic rig described in the previous chapter. The purpose of these experiments is to investigate the response of the model of the operator arm under combinations of factors that act as inputs during torque tool operation. The first section in this chapter describes the various input factors that were investigated, and the second section talks about the responses that were measured. The third section describes the design of experiments approach used to develop the orthogonal array and the final section discusses the various ergonomic metrics that were developed and used to compare the response curves.

4.1 Description of the factors

A factor is a controlled independent variable whose levels are set by the experimenter. This project investigated the effect of four factors which are stated below:

1. Tightening algorithm (tool controller setting)

2. Soft stop feature (tool controller setting)

3. Arm mass and stiffness

4. Joint stiffness

Each of the factors is explained in detail in the following subsections.

4.1.1 Tightening algorithm

The speed management algorithms are various approaches used to manage the speed and torque of the motor during a threaded fastening cycle using a DC nutrunner. Three different speed management approaches can be programmed at the Stanley tool controller and these are explained below [46].

Manual Downshift

Running the tool at a fixed speed from start until the final target can result in significant overshoot of the target torque if the speed is high while running at very low speed would increase the cycle time and result in overheating of the tool. Therefore, the traditional approach for DC nutrunners has been to use downshifting. In the Manual

Downshift technique the controller runs the tool at a higher initial speed for increased productivity and reduced heat. Once it senses a preliminary torque target, the speed is

reduced and the tool runs at a lower speed until achieving the final target torque, for improved accuracy (figure 4.1). The initial speed, the downshift torque level, and the final speed are the important parameters that are required to program this algorithm. It is

important to set an appropriate downshift torque, since downshifting too late may result

in torque overshoot with a hard joint, as seen in figure 4.1.

Torque overshoot

Figure 4.1: Speed and torque control using Manual Downshift [46]

Two Stage Control

The Two Stage Control algorithm, as shown in figure 4.2, runs the tool at maximum speed to a preliminary torque target, then shuts off the tool for 50 milliseconds, and then accelerates continuously to the final torque target. In the two stage approach it is important to find a balance between the initial speed, first shut off target and the final speed making this algorithm more complicated to use on different joint stiffnesses.

Figure 4.2: Speed and torque control using Two Stage algorithm [46]

Adaptive Tightening Control (ATC)

Stanley Tools has a patented algorithm called Adaptive Tightening Control (ATC).

This algorithm senses the joint rate and dynamically changes the speed every millisecond to achieve the best possible capability at a given starting speed. The speed and torque control for ATC is shown in figure 4.3. This algorithm has a number of advantages, including no requirement to test or learn a joint, and has been proven to decrease cycle time on applications with a mix of hard and soft joints. This algorithm also increases tool life by gradually loading the gears.

Figure 4.3: Speed and torque control using ATC [46]

The ATC algorithm can be used in two modes, the ATC automatic mode and the

ATC custom mode. These two modes are shown in figures 4.4 (a) and (b) with the parameters associated with them and the codes required to program them on the controller. The custom mode allows the adjustment of four parameters that help shape the

ATC deceleration ramp. These are start torque, end torque, free speed and end speed. In the automatic mode, the speed and torque parameters take on their default values as shown in figure 4.4 (a). The ATC custom mode is especially used for extremely hard joints and high prevailing torque applications. This project used the ATC automatic mode with its default speed and torque settings.

Figure 4.4:Parametersassociated Speed Torque Speed Torque (a)ATCAutomaticmode(b)Custom [45] P214 P214 (b) (a) reSed4 100% (>P213) Free Speed 40 – Free Speed 100% ofrated 0–75%(

0–100% (>P211) – 50 two modesofATCalgorithm End Torque End P212 75% oftarget End Torque End P213 P212 P213 n pe 0–30% (

4.1.2 Soft stop feature

While the tightening algorithms discussed in the previous subsection are related to the process of building the load to the final target, the process of releasing the load is also important from an ergonomic perspective [46]. The primary purpose of the “soft stop” feature is to improve the ergonomics of torque tools by reducing the jerk on the operator arm caused by releasing the load too quickly at the end of the run. The various controller parameters associated with the soft stop are displayed in figure 4.5. The way soft stop works is as follows: upon sensing the final target torque the controller shuts off the tool and pauses for a time described by a parameter called the “current off time”. After the current off time has elapsed the current flow is restored to a level slightly below that at the shut off point and held at that level for the “current hold time.”After the hold time has elapsed the current is ramped down linearly to zero over the course of the “current ramp time.” For a rundown without the soft-stop feature the current off, hold and ramp times will be equal to zero. Figure 4.5 shows the default values for the current off, hold and ramp times in milliseconds used on the Stanley controller and the maximum recommended value for the ramp time which is 250 milliseconds. The figure also shows codes for the parameters which are required to program the soft stop feature on the tool controller.

Figure 4.5: Parameters for the soft stop feature on Stanley controllers [45]

The soft stop factor was set at two levels for this project. For the first level, the current off, hold and ramp times were set at zero which meant that the soft stop feature was not active. This is denoted as the none condition. The second level used the default soft stop time values which were 0.001, 0.025 and 0.075 seconds for current off, hold and ramp times respectively (as shown in figure 4.5). This level is referred to as “default”.

4.1.3 Arm mass and stiffness

The arm mass and stiffness were combined as a single factor since both these are related to the muscle’s capacity to resist reaction forces. The effective mass is thought to reflect the quantity of muscle that is involved in the net muscle contraction and the stiffness is the inherent spring like property of the muscle that is important in the control of movement and maintenance of posture.

The ergonomic assessment rig described in the previous chapter contains a model

of the arm mass and stiffness represented by a mass box and an air cylinder respectively.

The values for the arm mass and stiffness levels were decided based on the papers by Lin et al. [11] and also the capabilities of the test rig used in this project. Lin and Radwin’s papers describe arm properties for specific test conditions (horizontal and vertical distances from the tool) using a right angle tool of 9 Nm torque output. The arm mass and

stiffness factor was set at three levels so that a wide portion of human population could

be represented.

The paper by Lin et al [11] lists the human arm parameters for an operator using a

right angle tool on a horizontal surface. The arm mass values range from a minimum of

0.51 kg at horizontal and vertical distances of 60 cm and 110 cm respectively to a

maximum of 7.67 kg at a horizontal distance of 30 cm and vertical distance of 80 cm.

Although the rig in this thesis was built to be an accurate representation of Lin’s model,

the minimum arm mass that could be obtained on the rig was 3.371 kilograms due to the

combined mass of all the moving parts. These included the mass box (without any mass

plates in it), the piston rod, the load cell with the protection device and the tool support

plate. The plates and the mass box were designed to attain a maximum mass of 7.5 kg to

represent the maximum value observed by Lin et al [11]. The second level of mass was

set half way between at about 5.43 kilograms. The stiffness values in Lin’s paper ranged

from 1289 N/m (at a horizontal distance 90 cm, vertical distance 80 cm) to a maximum of

3117 N/m (at a horizontal distance 90 cm, vertical distance 110 cm) [11]. Pilot tests

conducted at these stiffness values resulted in very high deflections. A possible reason for

this could have been the higher torque rating of the Stanley tool (maximum torque output

of 70N/m) as compared to the tool used by Lin which was rated at 9 N/m. Thus, it was

concluded that higher values have to be used for this rig. The value of the highest

stiffness level was set at 7000 N/m, more than twice the maximum stiffness observed by

Lin. The remaining stiffness levels were calculated by using the ratios of the different

mass levels. Table 4.1 shows the three levels of the mass and stiffness factor. The three

levels of the arm mass and stiffness factor are denoted by the factor called MK (kg N/m)

obtained by multiplying the corresponding levels of mass and stiffness.

Levels Mass (kg) Stiffness (N/m) MK (kg N/m) Level 1 3.371 3153 10629

Level 2 5.43 5076 27562

Level 3 7.5 7000 52500

Table 4.1: Levels of mass and stiffness

4.1.4 Joint stiffness

Joint stiffness is characterized by the fastener rotation required to go from the snug

torque until the target torque. The snug torque is the torque required to initiate contact

between the joint members and is expressed as 10% of the target torque. According to the

ISO 5393, a hard joint is one in which the degrees of fastener rotation required to go from the 10 % to 100 % of target torque is 27° or less, and a soft joint is one requiring greater than 720° of rotation [26].

Three levels of joint stiffness were studied in this project. A joint requiring about

120° of rotation was set as the first level, since the ATC program did not work very

effectively with very hard joints. The second level required 240° of rotation and the third

level required 360° of rotation. According to the ISO 5393 definition [26], all the joints in

this thesis belong to the “medium” joint category. But the levels will be referred to as the

“hard”, “medium” and “soft” for comparison purposes. The joint hardness was

manipulated using different configurations of Belleville spring washers.

4.1.5 Summary of the factors with their levels

The table 4.2 lists all the independent variables (factors) used for this project with their corresponding levels. The target torque was set at 60 Nm for all conditions. The complete set of controller parameters is included in Appendix D.

Factors Levels

Speed management Adaptive tightening Manual Downshift Two Stage Control algorithm control (automatic)

Arm mass and 10629 kg N/m 27562.7 kg N/m 52500 kg N/m stiffness (MK)

Joint hardness Hard Medium Soft

Soft stop feature None Default

Table 4.2: Factors with their corresponding levels

4.2 Measured response and other dependent variables

The dependent variables in an experiment refer to those that are observed to change

in response to the factors. The experiments conducted for this thesis assessed two

categories of dependent variables. The first was the response directly measured by the

load cell and LVDT which are part of the ergonomic test rig. The outputs of these sensors

are the reaction force at the arm model and deflection of the tool handle as a function of

time. The second kind of dependent variables were obtained by processing the reaction

force and deflection data using Matlab. Different ergonomic metrics were created based

on signal analysis of the curve. These included calculating area under the curve above a

certain threshold to calculate the impulse, finding peak values, ranges and incorporating

the effect of muscle latency on the curves. A complete description of these ergonomic

metrics is given in section 4.4.

4.3 Design of Experiments (DOE) and statistical analysis

Design of experiments (DOE) is a statistical method used to determine the

relationship between different factors affecting a process and the output of the process

[29]. This method involves designing a set of experiments in which the factors are varied

systematically. The first step in using DOE is to define the input variables of the experiment and their corresponding levels. The next step is to identify the response that

will be measured to describe the outcome of the experiment. Following this an

experimental design needs to be selected from several standard designs depending on the

objective of the experiment, the number of factors and the number of experimental runs

that can be conducted. An experimental array is then produced which specifies the levels

of the factors for each run. The response is measured for each run and is analyzed to find

differences between the responses for different groups of the input changes. The change in the response that resulted only from the change in an input factor is termed as a main effect. An interaction effect is said to occur when a change in the response due to a variation in one input factor depends on the level of the another input factor.

Two choices of designs were available for the experiments in this project, which were a full factorial design and a fractional factorial design. A full factorial design is one in which an experimental run is performed at every combination of the factor levels. The sample size is the product of the numbers of levels of the factors. A full factorial is the most conservative design approach and also the most time consuming because of the large number of runs. However a full factorial is capable of providing information about every main effect and every interaction effect. A fractional factorial design includes selected combinations of factors and levels which are a representative subset of a full factorial design. The disadvantage of using a fractional factorial approach is that, depending on the resolution of the design, some higher-order interactions are confounded with main effects or lower-order interactions.

A full factorial design was chosen since the interactions between the different factors were important in order to determine the most optimum factor conditions. The sample size was 3 X 2 X 3 X 3 equaling 54 runs. It was also decided to perform three replicates. In statistics, replication is the repetition of an experiment under the same

conditions so that the variability associated with the system can be estimated. Thus the

total number of runs was 162. The experimental array specifying the levels of the factors

for each run was generated and one complete set of 54 runs is shown in table 4.3. The

runs were not randomized since the effect of external conditions or the environment was

assumed to be insignificant on the response.

The model tested in the statistical analysis is: Dependent variable = Mass and

stiffness (MK) Joint hardness (J) Algorithm (A) Soft stop (S), MK X J, MK X A, MK X

S, J X A, J X S, A X S. The dependent variables are described in section 4.4. A repeated

measure ANOVA was used to test the statistical significance of each factor on the

response and each factor was treated as a fixed effect. The Tukey multiple comparison test was performed for selected significant interactions. A 95% confidence interval was used to denote statistical significance.

Run MK Run MK Joint hardness Speed algorithm Soft stop Joint hardness Speed algorithm Soft stop number (kgN/m) number (kgN/m) 1 10629 120 degrees ATC None 28 27562.7 240 degrees Manual Downshift Default 2 10629 120 degrees ATC Default 29 27562.7 240 degrees Two Stage Control None 3 10629 120 degrees Manual Downshift None 30 27562.7 240 degrees Two Stage Control Default 4 10629 120 degrees Manual Downshift Default 31 27562.7 360 degrees ATC None 5 10629 120 degrees Two Stage Control None 32 27562.7 360 degrees ATC Default 6 10629 120 degrees Two Stage Control Default 33 27562.7 360 degrees Manual Downshift None 7 10629 240 degrees ATC None 34 27562.7 360 degrees Manual Downshift Default 8 10629 240 degrees ATC Default 35 27562.7 360 degrees Two Stage Control None 9 10629 240 degrees Manual Downshift None 36 27562.7 360 degrees Two Stage Control Default 10 10629 240 degrees Manual Downshift Default 37 52500 120 degrees ATC None 11 10629 240 degrees Two Stage Control None 38 52500 120 degrees ATC Default 12 10629 240 degrees Two Stage Control Default 39 52500 120 degrees Manual Downshift None 13 10629 360 degrees ATC None 40 52500 120 degrees Manual Downshift Default 72 14 10629 360 degrees ATC Default 41 52500 120 degrees Two Stage Control None 15 10629 360 degrees Manual Downshift None 42 52500 120 degrees Two Stage Control Default 16 10629 360 degrees Manual Downshift Default 43 52500 240 degrees ATC None 17 10629 360 degrees Two Stage Control None 44 52500 240 degrees ATC Default 18 10629 360 degrees Two Stage Control Default 45 52500 240 degrees Manual Downshift None 19 27562.7 120 degrees ATC None 46 52500 240 degrees Manual Downshift Default 20 27562.7 120 degrees ATC Default 47 52500 240 degrees Two Stage Control None 21 27562.7 120 degrees Manual Downshift None 48 52500 240 degrees Two Stage Control Default 22 27562.7 120 degrees Manual Downshift Default 49 52500 360 degrees ATC None 23 27562.7 120 degrees Two Stage Control None 50 52500 360 degrees ATC Default 24 27562.7 120 degrees Two Stage Control Default 51 52500 360 degrees Manual Downshift None 25 27562.7 240 degrees ATC None 52 52500 360 degrees Manual Downshift Default 26 27562.7 240 degrees ATC Default 53 52500 360 degrees Two Stage Control None 27 27562.7 240 degrees Manual Downshift None 54 52500 360 degrees Two Stage Control Default

72 Table 4.3: Orthogonal array 4.4 Formulation of ergonomic metrics

A set of ergonomic metrics were developed for the purpose of this thesis by synthesizing published research results with some new ideas. The reaction force and deflection data that were obtained from the sensors were processed using different Matlab scripts developed to isolate desired portions of the curve or identify peak values, ranges etc. Four types of ergonomic metrics were formulated and they are explained in detail in the following subsections.

4.4.1 Torque impulse at different percentages of the target torque

The reaction torque impulse was defined in ISO 6544 [25] as the area under the reaction torque-time curve above a certain percentage of the target torque. It serves as a suitable measure of the ergonomic impact since it incorporates the magnitudes of the torque and also the duration of the reaction. The threshold level in ISO 6544 was set at

10% of the target torque. Kihlberg et al. [5] created a criterion called t90% which was defined as the time for which the tool torque exceeded 90% of the target torque. This value was found to be highly correlated with the discomfort ratings (r = 0.94). Another study by Kihlberg et al. [6] defined a metric called “time torque value” which was defined as the product of the target torque and the time for which tool torque exceeded

75% of the target. The authors found the discomfort ratings to be highly correlated to the time-torque value (r = 0.962).

Based on the above studies, a few new ways of calculating the reaction torque impulse were put forward in this thesis. The reaction force curve from the load cell was

73 converted to reaction torque, expressed in Newton-meters, by multiplying the reaction force by the tool’s handle length (equal to 18.5 inches or 0.47 m). The reaction torque impulse was calculated at seven different percentages of the target torque of 60 Nm.

These were 0, 20, 45, 50, 60, 70 and 75 percent. It was expected that this method would be useful in identifying factors that increase the duration of the rundown, which would be perceived by a tool operator as conditions requiring more effort. For example, the torque build up is slow for a soft joint and the impulse should therefore be higher for a soft joint than for a hard joint. The different thresholds can help in identifying whether the effect of a particular factor, say joint hardness, is significant at the higher torques or the lower torques.

A Matlab script, included in Appendix E, was used to isolate the portions of the curve at the seven thresholds and the area under the curve was calculated using the trapezoidal method. Figure 4.6 (a) shows a reaction torque time curve with the Two Stage

Control algorithm, hard joint, at low MK and with soft stop default. The reaction occurs in the negative direction but the curve has been inverted (torque values multiplied by negative one). Figures 4.6 (b) and (c) display the areas of the curve that were used to calculate the torque impulse at 20% and 50% respectively.

Threshold torque Threshold torque 12 Nm (20% of 30 Nm (50% of target) target)

Figure 4.6: Reaction torque versus time (a) Actual curve (b) Torque impulse 20% (c) Torque impulse 50% 75

4.4.2 Deflection - peaks in positive and negative direction, maximum range

Tool handle deflection has been used as a direct measurement of the effect of

“jerks” produced during power tool operation. Several studies have found strong correlations between handle deflections and subjective discomfort ratings [5] [6] [7].

During the repeatability tests conducted on the rig, the deflection time curves from the

LVDT showed peaks in the negative direction and also rebound deflections on the positive side. Thus three deflection metrics, peak deflection negative, peak deflection positive and maximum deflection range were developed, as shown in figure 4.7. These metrics can be used to identify factor conditions that cause maximum forward and reverse movement of the handle, and the range of movement while operating the tool.

Peak deflection positive

Max. deflection range

Peak deflection negative

Figure 4.7: Peak deflections positive and negative, maximum deflection range

4.4.3 Reaction torques - peaks in positive and negative direction, maximum range

The reaction force transmitted to the operator during tool operation has been stated as one of the risk factors for cumulative trauma disorders [6]. In this thesis, the reaction torque obtained from the load cell data has been used to evaluate the ergonomic impact of the input factors. These quantities are an indication of the maximum reaction force on the operator arm during eccentric and concentric exertions. The peak reaction torque in the negative and positive directions and maximum torque range were compared to determine factor conditions which result in lower values. These metrics are explained with the help of figure 4.8.

Peak reaction torque positive

Max. torque range Peak reaction torque negative

Figure 4.8: Peak reactions torque positive and negative, maximum torque range

4.4.4 Latency impulse - torque impulse with muscle latency included

Latency impulse was a metric introduced in this thesis and has its basis on two studies by Oh and Radwin. A brief description of their findings is important to fully understand how the latency impulse was calculated for the response curves.

Oh and Radwin [17] studied the effect of two types of joint hardnesses (35 millisecond and 900 millisecond build-up times) on muscle activity. From the EMG data, the authors observed a burst of muscle activity after the onset of torque build-up, shown in figure 2.10 in chapter 2. This time difference between the onset of torque build- up and the onset of the muscle activity burst was called EMG latency. The authors found the EMG latencies increased significantly (p < 0.05) as the build up time increased.

In another study, the authors Oh and Radwin tested the effect of five joint hardnesses on the EMG activity of finger flexors, biceps and triceps [18]. The torque build-up times corresponding to the five joints were 35, 150, 300, 500 and 900 milliseconds respectively. From all the trials analyzed in this study, the EMG burst was observed 87% of the time for finger flexors, 88% of the time for biceps and 94% of the time from triceps. The results showed that EMG latency was significantly influenced by the type of joint, and increased with increasing build-up time. The plot of average EMG latencies for the five joints from this study is shown in figure 4.9. In this paper, the subject perceived exertion was less and task acceptance rates were higher for the 35 millisecond build-up time than for longer build-up times. The EMG latency for this joint was on an average 40 milliseconds after the onset of torque build up. This implied that the muscles were not activated until after target was achieved. Thus, it was concluded

that reaction forces that occur too quickly do not result in muscular contractions and are

not felt by the operator, resulting in lower exertion.

The latency times for the joints used in this thesis were derived by fitting a linear

regression model to the average values from Oh and Radwin’s study, as shown in figure

4.10. The regression coefficient (r2) is 0.99, and p value is 0.00 showing a highly linear relationship between EMG latency and build-up time. The torque build up times for the joints in this research, according to the ISO 6544 definition of joint hardness used in Oh and Radwin’s study (50 % to 100 % of target torque) were 27 milliseconds, 33 milliseconds, and 60 milliseconds.

The latencies calculated using the regression equation were 44 milliseconds for the hard joint, 46 milliseconds for the medium and 55 milliseconds for the soft joint.

Figure 4.9: Effect of torque build-up times on muscle EMG latency [18]

Fitted Line Plot latency (ms) = 35.10 + 0.3286 Build up time (ms)

350 S 12.9930 R-Sq 99.0% 300 R-Sq(adj) 98.7% R2= 0.99, p=0.000 250

200

150

EMG latencyEMG (ms) 100

0 0 900800700600500400300200100 Torque build-up time (ms)

Figure 4.10: Linear regression between EMG latency and torque build-up time

The “latency impulse” was defined as the area under the reaction torque-time curve after excluding a part of the curve whose duration was equal to the EMG latency for that joint. The Matlab script used to calculate the latency impulse in included in Appendix E.

The method used to calculate latency impulse is described with the help of figure 4.11:

i. The time corresponding to the start of deflection (below zero) was determined.

This is the point when the muscle tendon begins to extend and this is when a

person using the tool would start feeling the pull on his arm. The corresponding

time on the reaction torque curve was found out and the EMG latency period was

counted starting from this point denoted as “latency start point”.

ii. The curve portion lasting for the EMG latency time is excluded since it is

assumed that the response of the arm during this time results from stretch reflex,

and not from voluntary muscle contraction, and is thus perceived as requiring less

exertion by the operator. iii. The area under the torque time curve, above 0 Nm is calculated. This area

(indicated by the red hatched lines) is called the latency impulse.

Latency start point

(a)

Excluded portion of the curve

Curve portion used to calculate latency impulse

Latency time for the joint (b)

Figure 4.11: Method used to calculate latency impulse

(a) Deflection-time plot (b) Torque-time plot

CHAPTER 5

ERGONOMIC ASSESSMENT OF RESPONSE CURVES -

RESULTS

This chapter presents the results of the experiments conducted for this thesis and

demonstrates the ergonomic impact of the four factors that were studied. In the first

section of this chapter, the raw data from a few screening experiments will be presented.

In the second section the response curves are evaluated based on the ergonomic metrics

developed in chapter 4 and the statistically significant effects of the factors are discussed.

In the third section, the curves from the rig are compared with those obtained from a few pilot tests with human subjects and possible reasons for differences between them are stated. The chapter is summarized in the final section.

5.1 Raw data from screening experiments

A one-factor-at-a-time screening experiment was conducted before executing the

162 runs of the DOE array, to get qualitative information about the effect of the factors on the response curves. The output of the ergonomic assessment rig is the reaction force and deflection of the human arm model as a function of time. The reaction force curve was converted to reaction torque, expressed in Newton-meters, by multiplying the reaction force with the handle length of the tool (equal to 18.5 inches or 0.47 m), since these units were used in the papers by Kihlberg et al. [5] [6]. In this section, the shapes of the torque and deflection plots will be compared to each other to understand their similarities and differences due to the different factor levels. The results of the statistical analysis from the DOE array will be stated in section 5.2.

A reaction torque-time curve using the ATC algorithm with a hard joint, at the lowest mass and stiffness level, and with soft stop is shown in figure 5.1 (a). The oscillations at the beginning of the curve are due to the free running of the nut after the trigger is pressed. The largest peak in the plot occurs when the tool reaches the target torque. The effect of soft stop can be seen clearly in this plot. During the tightening process, the entire drive train, including the gears between the motor itself and the fastener, is torsionally deflected. During the “off time” of the soft stop there is no current available to the stator and the gear train begins to relax to its neutral state. During the

“hold time” the current is restored in the stator which then attempts to hold the rotor in some interim position between fully deflected and fully relaxed. Finally, during the

“ramp time” the motor current is gradually ramped down to zero thereby allowing the

84 rotor to gradually relax to its neutral state.

The reaction torque - time plot using ATC with a hard joint, at the lowest MK level and without the soft stop is shown in figure 5.1 (c). When this curve is compared to figure 5.1 (a), the absence of the second peak at around -30 N can be seen. However there is a small bump towards the end of the run down which can be explained as follows.

After reaching the final target, the load on the tool is released to bring the tool to a stop.

Before the gears and other elements of the drive train come to a complete stop, they rotate in the reverse direction for a brief period due to the inertia of the elements. As the gear train is reversed at some point the backlash is overcome, and this is seen as a change in the sign of the torque from the tool. This effect causes the small bump in the reaction torque plots from the rig.

The purpose of the soft stop parameters in reducing oscillations at the end of the run is illustrated through two deflection plots in figure 5.1 (b) and (d). Both these runs use ATC algorithm, low mass and stiffness and a hard joint. The run in figure 5.1 (b) uses the soft stop default settings and it can be seen that the oscillations are damped out at the end of the run. The run in figure 5.1 (d) is without the soft stop feature, and shows that setting the soft stop timers to zero causes more oscillations at the end of the run.

Soft stop feature damps out This area denotes the oscillations the use of soft stop (a) (b)

Caused when gears overcome the backlash

Figure 5.1 Response curves with86 ATC, hard joint, low MK: with soft stop default (a) Reaction

torque and (b) Deflection, with no soft stop (c) Reaction torque (d) Deflection The plots in figure 5.2 compare the reaction torque and deflection curves as a

function of time for the three algorithms at the hard joint, with medium mass and stiffness

and soft stop default. Comparing traces 5.2 (a) and 5.2 (b) shows that the shapes of the

reaction torque curves with ATC and Manual downshift algorithms are quite similar if

other input factors are kept the same. The same thing can be said for the deflection plots in 5.2 (d) and (e). For both these algorithms, the tool runs continuously before reaching the target torque, unlike the Two Stage algorithm. The difference is that for Manual

Downshift, the speed is reduced abruptly at a specified downshift torque while in ATC the speed is ramped down between specified torque levels. Although this is expected to increases the duration for ATC, the difference is not very noticeable in the above plots. If at all thee exists a difference, it can be confirmed from the quantitative torque impulse results in section 4.1.1. Figures 5.2 (c) and (f) show the reaction torque and deflection curves for the Two Stage Control algorithm. It is evident that the curves for the Two

Stage Control algorithm are more complex in their shapes than for ATC and Manual

Downshift. The first peak at -30 Nm shows the first stage of the algorithm after which the tool stops for 50 milliseconds and then continues to the final target. The peak after the target is due to the soft stop settings. The deflection plot also has two peaks in the negative direction corresponding to the two stages.

(a) (d)

(b) (e)

Soft stop First stage

Figure 5.2: Comparing the three algorithms at medium MK, soft stop default, and hard joint: Reaction torque (a) ATC (b) Manual Downshift (c) Two Stage Control,

Deflection (d) ATC (e) Manual Downshift (f) Two Stage Control 88

(a) (b)

Effect of joint (c) stiffness

Figure 5.3: Reaction torque-time curves with Manual Downshift, medium MK level,

soft stop default (a) Hard joint (b) Medium joint (c) Soft joint

The effect of joint stiffness on the torque-time traces is shown in figures 5.3 (a), (b)

and (c). All the curves are with Manual Downshift, medium mass and stiffness, and soft

stop default. During the torque build up, it is evident from figures that the slope of the

torque curves for a medium and soft joint (figures 5.3 (b) and (c)) is lesser than for the

hard joint (figures 5.3 (a)). Since a softer joint takes more fastener rotations to go from snug torque to target torque, the torque build up time is longer. In contrast a hard joint

takes less rotation and this makes the curve steeper. The effect of joint stiffness was not very noticeable in the deflection-time curves.

5.2 Assessment of response curves - statistically significant results

The response curves obtained from the test rig were evaluated based on the metrics defined and explained in section 4.4. The results of the statistical analysis are presented in this section and effects of the factors and their interactions are described.

Table 5.1 shows the p values for all the factors and two way interactions, and the statistically significant sources are highlighted. The significant factors and interactions are discussed for each of the evaluation metrics in the following subsections.

For all plots in this section the three controller algorithms are abbreviated as ATC for Adaptive Tightening Control, MDS for Manual Downshift and TSC for Two Stage

Control. The three levels of joint hardness are denoted by 120, 240 and 360, referring to the degrees of rotation from snug torque (10% of target torque) to the target torque. The three levels of mass and stiffness are referred to as 1-Low, 2-Medium and 3-High. The two soft stop levels are indicated as default and none.

Torque Impulse Deflection Torque Latency Source Peak Peak Peak Peak 0% 20% 45% 50% 60% 70% 75% Range Range Impulse negative positive negative positive Joint 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 hardness

Mass and 0.000 0.000 0.003 0.000 0.000 0.001 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 stiffness

Algorithm 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Soft Stop 0.000 0.000 0.000 0.000 0.016 0.783 0.673 0.000 0.000 0.000 0.421 0.000 0.000 0.000

Joint hardness 0.304 0.012 0.068 0.042 0.182 0.055 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.009 X MK Joint 91 hardness 0.000 0.000 0.000 0.000 0.000 0.000 0.024 0.000 0.000 0.000 0.000 0.000 0.000 0.000 X Algorithm Joint hardness 0.896 0.658 0.006 0.044 0.019 0.060 0.361 0.000 0.000 0.000 0.172 0.000 0.000 0.607 X Soft stop MK X 0.015 0.014 0.067 0.007 0.000 0.001 0.000 0.000 0.002 0.000 0.000 0.000 0.057 0.001 Algorithm

MK X 0.178 0.116 0.002 0.000 0.001 0.544 0.820 0.000 0.000 0.000 0.001 0.000 0.000 0.316 Soft Stop

Algorithm X Soft 0.009 0.002 0.000 0.000 0.003 0.037 0.358 0.000 0.000 0.000 0.001 0.000 0.000 0.001 Stop Note: Highlighted areas are significant.

Table91 5.1: Statistically significant sources for the responses

5.2.1 Torque impulse at different percentages of the target torque

The torque impulse was calculated at seven percentages of the target torque, as explained in section 4.4.1. From table 5.1, it can be seen the joint hardness, mass and stiffness levels and algorithm were statistically significant at all torque impulses, while the soft stop factor was significant for all impulses except at 70 % and 75 %. Many of the two-way interactions were found to be statistically significant for the seven torque impulses. Hence, in this subsection, the significant sources of interactions will be stated, and following that, the main effects that are consistently observed, will be indicated. A few interactions were further tested using a Tukey pairwise comparison test, and the major findings from these will be stated.

The interaction plots are shown in figures 5.4 and 5.5. Statistically significant interactions from table 5.1 are:

• Interaction between MK and joint hardness at torque impulse 20 %, 50% and 75 %.

• The interaction between the tightening algorithm and joint hardness at all the

torque impulses.

• Interaction between joint hardness and soft stop at torque impulse 45 %, 50 % and

60 %.

• Interaction between mass and stiffness and algorithm at all torque impulses except

at 50 %.

• Interaction between mass and stiffness and soft stop at torque impulse 45 %, 50 %

and 60 %.

• The interaction between algorithm and soft stop at all torque impulses except at

75 %. 92

(a) Interaction Plot for Torque Impulse 0% (Nm-s) (b) Interaction Plot for Torque Impulse 20% (Nm-s) Data Means Data Means

ATC MDS TSC 120 240 360 default none ATC MDS TSC 120 240 360 default none 12 12 Mass and Mass and 8 Stiffness 8 Stiffness Mass and Stiffness 1-Low Mass and Stiffness 1-Low 4 2-Medium 4 2-Medium 3-High 3-High 12 12 MassController and MassController and Stiffnessalgorithm 8 Stiffnessalgorithm 8 ATC Controller algorithm 1-LowATC Controller algorithm 1-Low 4 MDS 4 2-MediumMDS 2-Medium 3-HighTSC 3-HighTSC 12 12 ControllerMassJoint and MassController Jointand 8 algorithmStiffnesshardness 8 Stiffnessalgorithmhardness Joint hardness 1-LowATC 120 Joint hardness 1-LowATC 120 4 2-MediumMDS 240 4 2-MediumMDS 240 TSC3-High360 TSC3-High360

Soft stop Soft stop 93 (c) Interaction Plot for Torque Impulse 45% (Nm-s) (d) Interaction Plot for Torque Impulse 50% (Nm-s) Data Means Data Means

ATC MDS TSC 120 240 360 default none ATC MDS TSC 360240120 default none 12 12 Mass and Mass and 8 Stiffness 8 Stiffness Mass and Stiffness Mass and Stiffness 1-Low 1-Low 4 2-Medium 4 2-Medium 3-High 3-High 12 12 MassController and MassController and 8 Stiffnessalgorithm 8 Stiffnessalgorithm Controller algorithm Controller algorithm 1-LowATC 1-LowATC 4 2-MediumMDS 4 2-MediumMDS 3-HighTSC TSC3-High 12 12 ControllerMassJoint and ControllerMassJoint and 8 algorithmStiffnesshardness 8 algorithmStiffnesshardness Joint hardness Joint hardness 1-LowATC 120 1-LowATC 120 4 2-MediumMDS 240 4 2-MediumMDS 240 TSC3-High360 TSC3-High360

Soft stop Soft stop

Figure 5.4: Interaction plots for torque impulse at (a) 0 % (b) 20 % (c) 45 % (d) 50 % 93

(b) Interaction Plot for Torque Impulse 70% (Nm-s) (a) Interaction Plot for Torque Impulse 60% (Nm-s) Data Means Data Means ATC MDS TSC 120 240 360 default none ATC MDS TSC 120 240 360 default none 12 12 Mass and Mass and 8 Stiffness 8 Stiffness Mass and Stiffness 1-Low Mass and Stiffness 1-Low 4 2-Medium 4 2-Medium 3-High 3-High 12 12 MassController and MassC ontroller and 8 Stiffnessalgorithm 8 Stiffnessalgorithm Controller algorithm ATC Controller algorithm 1-LowATC 1-Low 4 4 2-MediumMDS 2-MediumMDS TSC3-High TSC3-High 12 12 CMass ontrollerJoint and ControllerMassJoint and 8 algorithmStiffnesshardness 8 algorithmStiffnesshardness Joint hardness Joint hardness 1-LowATC 120 1-LowATC 120 4 2-MediumMDS 240 4 2-MediumMDS 240 TSC3-High360 TSC3-High360

Soft stop Soft stop

94 (c) Interaction Plot for Torque Impulse 75% (Nm-s) Data Means

ATC MDS TSC 120 240 360 default none 12 Mass and 8 Stiffness Mass and Stiffness 1-Low 4 2-Medium 3-High 12 MassController and 8 Stiffnessalgorithm Controller algorithm 1-LowATC 4 2-MediumMDS TSC3-High 12 MassControllerJoint and 8 Stiffnessalgorithmhardness Joint hardness 1-LowATC 120 4 2-MediumMDS 240 TSC3-High360

Soft stop

Figure 5.5: Interaction plots for torque impulse at (a) 60 % (b) 70 % (c) 75 % 94

One main effect that was observed consistently in all the interaction plots was the

effect of algorithm on the torque impulse. Two Stage Control resulted in higher torque impulses at all mass and stiffness levels, joint hardnesses, and soft stop conditions. The main effects plots for torque impulse at 0 % and 75 % are shown in figure 5.6 (a) and (b) respectively, for the three controller algorithms.

Main Effects Plot for Torque Impulse 75% (Nm-s) Main Effects Plot for Torque Impulse 0% (Nm-s) Data Means Data Means 12 12

8 8

4 4

ATC MDS TSC ATC MDS TSC Controller algorithm Controller algorithm (a) (b)

Figure 5.6: Main effect plots at the three controller algorithms

(a) Torque impulse 0 % (b) Torque impulse 75 %

The Tukey tests for the controller algorithm and joint hardness interaction showed that the response at a particular joint hardness depends upon the algorithm. The important findings were:

• For torque impulse 0 %, the response with ATC and Manual Downshift are the

same at all three joints. Hard and medium joints result in equal impulses, and soft

joints result in the highest. With the Two Stage Control algorithm, the impulse is

the least with a hard joint, and equal at medium and soft joints.

• At torque impulse 20 %, 45 % and 50 %, the response with ATC and Manual

Downshift are the same as that at 0 %. But with the Two Stage Control, the

impulses at hard and soft joints are equal and lesser than at the medium joint.

• At torque impulse 60 %, there is no difference between the impulses with Manual

Downshift and ATC at all three joint hardnesses. The response with Two Stage

Control is the same as at torque impulse 20 %, 45 % and 50 %.

• At torque impulse 70 % and 75 %, the response with Manual Downshift and ATC

are equal at all three joints hardnesses, and there is no difference between the

values at the three joints. There is also no difference between the impulse at the

three joints with Two Stage Control algorithm, but the response with Two Stage

Control was higher than the responses at ATC and Manual Downshift, at all joints.

From the Tukey pairwise comparison tests on the controller algorithm and soft stop interaction, it was found that:

• At torque impulse 0 % and 20 %, soft stop default resulted in higher values than

none for all three algorithms. However, there was no difference between the values

of ATC and Manual Downshift at the corresponding soft stop levels. Two stage

control values were higher than for ATC and Manual Downshift.

• At torque impulse 45 % and 50 %, soft stop default resulted in higher values with

ATC and Two Stage Control. The impulses with Manual Downshift were equal at

both levels. Also, the difference between ATC and Manual Downshift was

significant only at the soft stop default level, with ATC resulting in higher impulse.

• At torque impulse 60 %, there was no difference in the impulse at the two soft stop

levels, for all three algorithms. However, the values with Two Stage Control were

higher than with ATC and Manual Downshift.

5.2.2 Peak deflection negative, peak deflection positive and maximum range

The three deflection metrics were explained in section 4.4.2. From table 5.1, it can be seen that all factors and two way interactions were found to be statistically significant for the deflection metrics. The main effects plots for the peak deflections positive and negative and the range for the MK factor are shown in figure 5.7 (a), (b) and (c). It can be seen from the interaction plots, in figure 5.8, that the effect of mass and stiffness is consistent for all the three deflection metrics, with the highest values resulting at the lowest MK level.

Main Effects Plot for Peak deflection negative (mm) Main Effects Plot for Peak deflection positive (mm) Data Means Data Means

24 9

22 8

20 7 18

16 6

14 5

12 4 10 1-Low 2-Medium 3-High 1-Low 2-Medium 3-High Mass and Stiffness Mass and Stiffness (a) (b)

Main Effects Plot for Deflection Range (mm) Data Means

1-Low 2-Medium 3-High Mass and Stiffness (c)

Figure 5.7: Main effect plots at the three MK levels (a) Peak deflection negative

(b) Peak deflection positive (c) Deflection range

The interactions between controller algorithm and joint hardness were further evaluated using the Tukey pairwise comparison test. The important findings from the

Tukey tests were:

• For the peak deflection negative, there was no difference between ATC and Manual

Downshift algorithms responses. The responses at hard and medium joints were

equal and lesser than the response at the soft joint. The Two Stage Control response

was the same at all three joints, and equal to the soft joint response with ATC and

Manual Downshift. 98

• For the peak deflection positive, ATC and Manual Downshift response trends were

the same as that for peak deflection negative. However with Two Stage Control, the

deflection at the medium joint was the least. With Two Stage Control, hard and soft

joints resulted in equal positive deflections, which were found to be equal to the

deflection with ATC and Manual downshift with a soft joint.

• For the deflection range, ATC and Manual Downshift responses had the same trend

as that for the other two deflection metrics. The deflection range with Two Stage

Control was the same across all the three joints, and equal to the ATC and Manual

Downshift range at the soft joint.

The Tukey pairwise comparison test was also used to assess the controller algorithm and soft stop interactions: The major findings were:

• For the peak deflection negative, there was no effect of the two soft stop levels on

Manual Downshift and Two Stage Control. The value at ATC and default soft stop

was higher than at none. The values at Two Stage Control were equal to that at

ATC and default soft stop.

• For the peak deflection positive, the effect of soft stop was seen only with the Two

Stage Control algorithm. The default soft stop resulted in lower positive deflection.

The values at ATC and Manual Downshift were equal at both soft stop levels.

• The observations for the deflection range were similar to that at peak deflection

positive.

(a) Interaction Plot for Peak deflection negative (mm) (b) Interaction Plot for Peak deflection positive (mm) Data Means Data Means

ATC MDS TSC 120 240 360 default none ATC MDS TSC 120 240 360 default none 30 Mass and 10.0 Mass and 20 Stiffness Stiffness 1-Low 7.5 Mass and Stiffness Mass and Stiffness 1-Low 10 2-Medium 5.0 2-Medium 3-High 3-High 30 MassC ontroller and 10.0 MassController and 20 Stiffnessalgorithm Stiffnessalgorithm ATC 7.5 Controller algorithm 1-Low Controller algorithm 1-LowATC 10 2-MediumMDS 5.0 2-MediumMDS TSC3-High TSC3-High 30 MassC ontrollerJoint and 10.0 MassControllerJoint and 20 Stiffnessalgorithmhardness Stiffnessalgorithmhardness 7.5 Joint hardness 1-LowATC 120 Joint hardness 1-LowATC 120 10 2-MediumMDS 240 5.0 2-MediumMDS 240 TSC3-High360 TSC3-High360

Soft stop Soft stop 100 (c) Interaction Plot for Deflection Range (mm) Data Means

ATC MDS TSC 120 240 360 default none

Mass and 30 Stiffness Mass and Stiffness 20 1-Low 10 2-Medium 3-High MassController and 30 Stiffnessalgorithm Controller algorithm 20 1-LowATC 10 2-MediumMDS 3-HighTSC MassControllerJoint and 30 Stiffnessalgorithmhardness Joint hardness 20 1-LowATC 120 10 2-MediumMDS 240 TSC3-High360

Soft stop

Figure 5.8: Interaction plots for (a) Peak deflection negative (b) Peak deflection positive (c) Deflection range 100

5.2.3 Peak torque negative, peak torque positive and torque range

The three torque metrics, peak deflection negative, positive and the torque range

were described in section 4.4.3. From table 5.1, it can be seen that all factors and interactions were found to be statistically significant for the torque metrics, except for the soft stop factor and the interaction between soft stop and joint hardness for peak torque negative.

The interaction plots for the torque metrics are shown in figure 5.9. None of the main effects were consistent for all factor conditions. Hence, the Tukey pairwise comparison test was used to assess selected interactions.

The major findings from the Tukey test performed on the controller algorithm and joint hardness interactions are:

• For peak torque negative and torque range, there is no difference between the

values with ATC and Manual Downshift. The peak torque values with the hard and

medium joints are equal and lesser than that at the soft joint. For the Two Stage

Control algorithm, the peak torque negative and range is equal at the hard and soft

joint, and this is greater than the response at the medium joint.

• For the peak torque positive, there was no difference between the values of ATC

and Manual Downshift at all three joints. The trend with the Two Stage Control

algorithm was the same as that for peak torque negative.

The major findings from the Tukey test performed on the controller algorithm and

soft stop interactions are:

• For the peak torque negative and torque range, the difference in the response at the

two soft stop levels for seen only with Manual Downshift, with default values 101

reducing the response. The highest negative peak and range values at all soft stop

conditions was seen with the Two Stage Control algorithm. The only difference

between ATC and Manual Downshift was seen at the default soft stop, for the peak

torque negative. ATC resulted in higher negative peak than Manual Downshift.

• The soft stop default settings were found to reduce the peak torque positive at all

the algorithms. The peak torque positive with ATC and Manual Downshift were

equal at the corresponding soft stop levels (ATC with default = Manual Downshift

with default, ATC with none = Manual Downshift with none). The highest peak

torque positive occurred with Two Stage Control at the no soft stop condition.

For the peak torque negative, the interaction between mass and stiffness and controller algorithm show some trends that are not consistent with the other torque metrics. So the Tukey pairwise comparison test was performed to evaluate the differences at the different algorithms and MK levels. The major findings were:

• The peak torque negative with ATC and Manual Downshift algorithms were the

same at the corresponding MK levels. The peaks at the low and medium MK levels

were equal and lesser than the peak at the high MK level.

• With Two Stage Control, the peak torque negative was the same at all three mass

and stiffness levels.

102

Interaction Plot for Peak torque negative (Nm) (a) (b) Interaction Plot for Peak torque positive (Nm) Data Means Data Means ATC MDS TSC 120 240 360 default none ATC MDS TSC 120 240 360 default none 56 Mass and Mass and Stiffness 12 52 Stiffness Mass and Stiffness 1-Low Mass and Stiffness 1-Low 2-Medium 8 48 2-Medium 3-High 3-High 56 4 MassController and 12 MassController and Stiffnessalgorithm algorithm 52 Stiffness Controller algorithm 1-LowATC Controller algorithm ATC 8 1-Low 2-MediumMDS MDS 48 2-Medium TSC3-High TSC 4 3-High 56 ControllerMassJoint and 12 MassControllerJoint and algorithmStiffnesshardness Stiffnessalgorithm 52 hardness Joint hardness Joint hardness 1-LowATC 120 8 1-LowATC 120 2-MediumMDS 240 2-MediumMDS 48 240 TSC3-High360 4 3-HighTSC 360

Soft stop Soft stop

(c) Interaction Plot for Torque Range (Nm) Data Means 103 ATC MDS TSC 120 240 360 default none Mass and 65 Mass and Stiffness Stiffness 60 1-Low 2-Medium 55 3-High Controller 65 Mass and Controller algorithm Stiffnessalgorithm 60 1-LowATC MDS2-Medium 55 TSC3-High Controller 65 Mass Jointand Joint hardness Stiffnessalgorithmhardness 60 1-LowATC 120 MDS2-Medium 55 240 TSC3-High360

Soft stop

Figure 5.9: Interaction plots for (a) Peak torque negative (b) Peak torque positive (c) Torque range

103

5.2.4 Latency impulse

The latency impulse was a metric introduced in this thesis and was explained in

section 4.4.4. From table 5.1, it can be seen that all factors and two-way interactions,

excluding the interaction between MK and soft stop, and joint hardness and soft stop,

were found to be statistically significant for the latency impulse. The interaction plot for

latency impulse is shown in figure 5.10.

Interaction Plot for Latency Impulse (Nm-s) Data Means

ATC MDS TSC 120 240 360 default none 12 Mass and Stiffness 8 Mass and Stiffness 1-Low 2-Medium 4 3-High 12 MassController and Stiffnessalgorithm 8 Controller algorithm 1-LowATC 2-MediumMDS 4 3-HighTSC 12 ControllerMassJoint and algorithmStiffnesshardness 8 Joint hardness 1-LowATC 120 2-MediumMDS 4 240 TSC3-High360

Soft stop

Figure 5.10: Interaction plot for latency impulse

The Two Stage Control algorithm resulted in the maximum latency impulse at all the MK levels, joint hardnesses and soft stop conditions. This main effect was consistent at all factor conditions and is shown in figure 5.11.

104 Main Effects Plot for Latency Impulse (Nm-s) Data Means

3 ATC MDS TSC Controller algorithm

Figure 5.11: Main effect plot for latency impulse at the three controller algorithms

The Tukey pairwise comparison test was performed on the controller algorithm and joint hardness interactions. The major findings were:

• The latency impulse at the corresponding joint hardnesses was equal for ATC and

Manual Downshift algorithms. The latency impulses with the hard and medium

joints were equal, and lesser than the impulse with the soft joint.

• The latency impulses with the Two Stage Control were different at all three joint

hardnesses. The least impulse resulted with the hard joint, and the maximum was

in case of the medium joint.

The major findings from the Tukey tests on the controller algorithm and soft stop interactions were:

• The soft stop default resulted in higher latency impulse than the none condition at

105 all three algorithms.

• The latency impulse with ATC and Manual Downshift were equal at the

corresponding soft stop conditions (ATC with default = Manual Downshift with

default, ATC with none = Manual Downshift with none).

• The latency impulse with Two Stage Control was greater than with ATC and Manual

Downshift. The maximum impulse resulted when the Two Stage Control algorithm

was used with default soft stop.

5.3 Comparison of rig curves with curves from human testing

A few pilot tests were conducted to compare the human response with that of the ergonomic rig. The pilot test method was as follows:

• Two colleagues were tested with the same three joint stiffnesses as used for the rig.

• The tool was run with the same three controller programs that were used for the

experiments with the rig. All program parameters including the target torque, snug

torque, torque and angle limits were also the same. The three algorithms were used

with and without the soft stop feature resulting in six controller settings totally.

• The pilot test operators were allowed to conduct few trial runs to get used to the tool

and the actual runs were conducted once they were ready, in random order.

• The deflection of the handle was measured using a rate gyro from Analog Devices

with a range of +/- 300°/sec mounted on the tool handle. The data sheet for the rate

gyro is included in Appendix C. The angular velocity was integrated to find the

angular displacement, which was then converted to linear displacement.

106 The main purpose of these pilot tests was to compare the shapes of the deflection

curves obtained with human operators to the rig deflection curves. The pilot tests would also demonstrate whether the deflection values measured by the rig were comparable to those experienced by people. However, there are several other factors that affect the human response. For instance, operators who have a greater experience in the use of power tools would find it easier to counter the reaction force. There is also a learning component involved with the use of these tools. It may be easier to respond to the torque reaction if you are familiar with the run down and know what to expect, especially in the

case of the Two stage algorithm.

A few samples plots compare the rig and human response curves. Note that the y-

axis scale on the human and rig plots are different. All plots from the human pilot tests

were conducted by operator 1. Figures 5.12 (a) and (b) show the deflection-time plots

with ATC algorithm used on a hard joint, with the soft stop feature on. The plots in figure

5.13 are for runs with ATC on a hard joint, but with the soft stop feature turned off. The

figures 5.14 (a) and (b) correspond to curves with Two stage control on a soft joint with

the soft stop feature. A few key observations from the above plots are:

• The peak deflection magnitudes are much higher for the human responses curves

than for the rig. The actual human arm deflections with the Two stage algorithm

are significantly greater than that seen on the rig.

• The shapes of the curves are similar till the peak deflection occurs. But the

oscillations that were observed towards the end of the run with the rig are absent in

the human curves.

107 (a) (b)

Figure 5.12: Deflection versus time with ATC, hard joint, soft stop default (a) Human operator 1 (b) Rig with medium mass and stiffness

(a) (b)

Figure 5.13: Deflection versus time with ATC, hard joint, no soft stop (a) Human operator 1 (b) Rig with medium mass and stiffness

108 (a) (b)

Figure 5.14: Deflection versus time with Two Stage, soft joint, soft stop default a) Human operator 1 b) Rig with medium mass and stiffness

There could be several reasons for the observed differences, but they are only suppositions without further testing. Some possible reasons are given below.

• The ergonomic test rig does not contain a damping element and this could be a

reason for the oscillations seen in the rig curves.

• One important observation during the pilot tests with human subjects was the

whole body movement of a person to overcome the torque reaction from the tool.

It was seen that not only the arm, but the head and shoulders were also used

occasionally to resist the kickback. Since the rig contains only a model of the

human arm, it does not take into account the effect of other body parts that support

the arm.

• Another reason for the differences could be the assumption of a constant arm mass

and stiffness during a run. The effective stiffness of the muscles will change during

a run, if different muscle groups are activated. The effective mass depends on the

109 different parts of the body that assist in resisting the reaction and this will not be

constant during a rundown. The constant mass and stiffness assumption does not

account for these changes.

• Also, the mass and stiffness values used in the rig were higher than the values

determined by Lin et al. [11]. This was done to account for higher torque rating of

the Stanley tool.

• For the experiments conducted with the rig, the arm mass and stiffness were

combined as a single factor. How well a person counters the torque reaction

depends on how fit he/she is. The fitness of a person is a function of both his mass

and stiffness. For instance a person with low arm mass but better toned muscles

(more stiffness) is fitter than someone with high mass but less toned muscles (low

stiffness). Thus it more appropriate to separate the mass and stiffness levels as two

different factors and investigate different levels of these in combinations (low

mass, high stiffness versus high mass low, stiffness).

The rig designed and built in this project was based on the dynamic model of the human arm developed by Lin and Radwin [11] [12] [13] which assumes the mass and

stiffness elements to be constant during a run. This rig is a highly repeatable physical

representation of Radwin’s model. It is not a perfect replication of the human arm and

developing such a system requires further testing of human subjects and comparing the

response with that of the rig. For this it is also important to determine how the mass,

stiffness and damping elements vary during a run and develop a model that will simulate

this variation. The results presented in section 5.2 are pertinent to the response curves

110 from the rig, but the ergonomic metrics that were developed as a part of this research can be used with data from human subjects, provided the reaction torque at the subject’s hand can be measured.

5.4 Chapter summary

This chapter displayed the raw data obtained from screening experiments on the rig and the shapes of the curves for different input conditions were compared. The results of the statistical analysis conducted on the rig responses were presented and statistically significant sources were discussed. The curves from pilot tests with human operators were compared with the rig response curves and possible reasons for the differences were stated. The results of the statistical analysis conducted on all the evaluation metrics will be discussed in chapter 6 and possible reasons for the observed effects will be stated.

111

CHAPTER 6

DISCUSSIONS

The analytical results and statistically significant sources for all the evaluation metrics were presented in chapter 5. In this chapter, the effects of the factors and interactions on the four assessment criteria i.e. torque impulse, deflection, reaction torque and latency impulse, will be discussed and the results will be related to previous ergonomic studies.

6.1 Results of torque impulse

The torque impulse for this thesis was calculated at seven different percentages of the target torque. The reaction torque data was obtained from the reaction force measured at the tool handle by the load cell. Studies by Kihlberg et al. [5] [6] had correlated the torque impulse, as defined in ISO 6544 [25], with discomfort ratings. Frievalds and

Eklund had studied the effect of joint stiffness on torque impulse [2]. However in these studies the torque from the tool was used to calculate the impulse. As stated in ISO 6544,

112 the installation torque (torque transmitted to the fastener) may be higher or lower than the

reaction torque (torque transmitted to the operator) depending upon the inertia of the tool,

operator characteristics and work orientation [25]. The torque impulse calculations for

this thesis used the reaction force measurements at the tool handle, which were

considered to be a more accurate measurement of the reaction transmitted to the model of

the human arm.

Among the four input factors investigated in this thesis, the effect of three of these i.e., mass and stiffness, controller algorithm and soft stop feature, have not been studied in relation to torque impulse. Frievalds and Eklund [2] included joint stiffness as a factor and studied its effect on torque impulse.

The three levels of mass and stiffness were statistically significant at all the torque impulses (table 5.1). However, there was not much of a difference between the values obtained at the three MK levels, when used at the different algorithms, joint hardnesses or soft stop conditions. A possible reason for this could be that the range of mass and stiffness levels that were investigated was not wide enough, and there may not have been enough separation between the three levels. Also the values chosen might have been too high, and this may have masked the effects that might have occurred, had the MK values been lower.

The effect of the three algorithms was consistent across all the MK levels, joint hardnesses and soft stop conditions. A much higher torque impulse was seen with the

Two Stage Control algorithm at all the cut off percentages. This was not surprising since torque impulse calculations take into account the duration of reaction. This program takes

113 longer than ATC and Manual Downshift, because of the run occurring in two stages and also the tool being stopped for 50 milliseconds at the end of the first stage, and thus, would result in a higher area under the curve. The results of the Tukey pairwise comparison tests showed that there was no significant difference between the torque impulse with ATC and Manual Downshift at all joint hardnesses and soft stop levels.

The response at a particular joint hardness depended upon the algorithm that was used. The impulse with ATC and Manual Downshift algorithms was the highest with the soft joint at 0 %, 20 %, 45 % and 50 %. This was consistent with the finding of Frievalds and Eklund who found that a softer joint resulted in a higher impulse [2]. With the Two

Stage Control algorithm, the highest torque impulse occurred with the medium joint at all cut off percentages except at 70 % and 75 %. In the study by Oh, Radwin and Fronczak

[19], the effect of five joint hardnesses (35, 150, 300, 500 and 900 milliseconds build up times) on the hand force was investigated. The greatest hand force occurred at the 150 millisecond joint and the least occurred at the 35 millisecond joint. This was similar to the observation with the Two Stage Control algorithm. However, only three levels of joint hardnesses were studied in this thesis, so it would be necessary to test the effect of the algorithms at other levels of joints (such as very hard and very soft). This would help generate more information, and the effect of joint stiffness can be understood better.

The two soft stop conditions had a significant effect on the torque impulse at lower thresholds (0 %, 20 %, 45 % and 50 %). The effect of soft stop was not seen at the higher cut off percentages (70 % and 75 %), since the “bump” caused by the default soft stop is usually at 20 Nm to 35 Nm, while the threshold torque at 70 % and 75 % were 42 Nm (70

114 % of target 60 Nm) and 45 Nm (75 % of target 60 Nm) respectively. The higher impulse

values with soft stop can be explained by the fact that enabling the soft stop timers causes

the tool to stop after reaching the target torque, the speed is then held at a certain torque

level and finally ramped down to zero. This increases the total duration of the run and

therefore results in a higher impulse.

6.2 Results of deflection peaks and range

Three deflection metrics, explained in section 4.4.2, peak deflection negative, peak

deflection positive and deflection range. Studies by Kihlberg [5] [6] correlated the hand

arm displacement to discomfort ratings and also suggested a displacement limit for

acceptability [7]. Oh and Radwin also studied the effect two levels of joint hardness (35

and 900 milliseconds) on the peak handle displacement [17].

Four input factors i.e., mass and stiffness, controller algorithm, joint hardness and the soft stop feature, were incorporated in this thesis and their effects on the deflection response is discussed below.

For the mass and stiffness factor, it was observed that the highest peak deflection negative, positive and range occurred at the lowest MK level, at all algorithms, joint hardnesses and soft stop levels. This is in agreement with what is expected, that is a weaker arm would deflect more than a stronger arm. There was not much of a difference between the medium and high MK levels.

The effect of tightening algorithms was also significant for the deflection metrics.

The Two Stage Control algorithm resulted in higher deflection positive, negative and

115 range at the lowest MK level and at the hard joint, as compared to ATC and Manual

Downshift. The negative peak and range with the Two Stage Control algorithm was not affected by joint hardness. With the ATC and Manual Downshift algorithms the response was the highest with the soft joint at all three deflection metrics. This was consistent with the results from Oh and Radwin’s study [17] where the peak handle displacement was the greatest with the soft joint (900 milliseconds build up time).

The effect of the soft stop was seen at the peak deflection positive and deflection range values when Two Stage Control was used. The rebound deflection was significantly lower with the default soft stop. This result is consistent with the purpose of the soft stop feature which is to gradually release the load on the tool which also decreases the rebound (positive) deflection. There was no effect of soft stop on Manual

Downshift and ATC.

An important observation from the experiments conducted was that the maximum deflection value obtained on the rig was about three centimeters which was much lower than the peak handle deflection seen with the pilot human tests. Peak handle displacements seen in the Oh and Radwin paper were about 5.5 centimeters for a tool rated at 50 Nm [17]. A possible reason for lower deflections with the rig could have been the levels of mass and stiffness that were chosen, which may have restricted a greater movement of the handle, which might have been possible with lower MK values.

6.3 Results of reaction torque peaks and ranges.

The reaction torque was obtained from the reaction force measurements at the tool

116 handle. The peak reaction torque in the negative and positive direction, and the maximum

range of torque values were used as ergonomic assessment criteria. Previous studies have

used EMG to estimate the grip force exerted during tool operation. Studies by Kihlberg et al., measured the ground reaction forces between the subject and the floor using a force plate, and correlated the reaction force to discomfort ratings [5] [6]. Lin et al [8] used right angle, pistol grip and inline tools with instrumented handles to measure the hand- handle interface force and moment generated by the operator hand, for different tool shapes, torques and joint hardnesses. Four input factors i.e., mass and stiffness, controller algorithm, joint hardness and the soft stop feature, were incorporated in this thesis and their effects on the reaction torque response is discussed below.

For the mass and stiffness factor, the lowest level resulted in higher peak torque positive, while there was not much of a difference between the three MK levels for the torque range. Some unusual trends were observed for the peak torque negative, especially for when the mass and stiffness interacted with the controller algorithm and joint hardness. The Tukey test performed on the MK and controller algorithm showed that the highest peak torque negative with ATC and Manual Downshift occurred at the highest

MK level, which is contrary to what would be expected. With Two Stage Control, there the peak torque negative values at the three mass and stiffness levels were not significantly different.

The effect of tightening algorithms significant on the torque metrics with the Two

Stage Control resulting in the highest peak torque negative and torque range at all MK

levels, soft stop and hard and soft joint. The peak torque positive was the highest for Two

117 Stage Control at all other factors combinations. There was no significant difference

between ATC and Manual Downshift at the different mass and stiffness levels and joint

hardness for all the torque metrics.

There was a considerable amount of interaction between joint hardness and the controller algorithm. With the Two Stage Control algorithm, the maximum value for all the torque metrics occurred were equal at the hard and soft joints and greater than at the medium joint. With ATC and Manual Downshift the greatest peak torque negative and torque range resulted from the soft joint. This was in agreement with the results of Lin et al., who found the hand-handle interface force and the torque at the handle to be the

greatest with the soft joint, for all tool shapes. There was no effect of joint hardness on

the peak torque positive values with ATC and Manual Downshift.

The soft stop default settings were found to reduce the peak torque positive at all

the algorithms. The only significant difference between the response with ATC and

Manual Downshift was observed at the default soft stop for peak torque negative, where

the peak value was higher with ATC.

All the torque values used for ergonomic assessment in this thesis were based on

the measurements from the load cell. This thesis did not use the torque from the tool

controller for any assessment. The torque data from the tool would help determine how

different the trends of the torque curves are, whether the peak tool torque coincided with

the peak torque at the handle or if there is a delay between these. Although the peak

reaction torque (negative) values were lower than the target torque of 60 Nm for all the

runs, it would be important to incorporate other levels of mass and stiffness and joint

118 hardness and compare the tool and reaction torque.

6.4 Results of latency impulse

The latency impulse is an ergonomic evaluation criterion that was defined and

explained in detail in chapter 4, and was based on the studies on Oh and Radwin [17]

[18]. For this metric, the area under the reaction torque curve is calculated, after

excluding a portion of the curve, whose duration equaled the muscle latency time for a

particular joint hardness. It was assumed that there is no voluntary muscular activation in

response to the reaction torque during this period and thus no exertion is perceived by the operator. The latency times for this thesis were derived by fitting a regression model to the data from Oh and Radwin’s study [18].

For the mass and stiffness factor, there was no major difference in the magnitude of latency impulse at the three levels. This was similar to the results of torque impulse.

The effect of tightening algorithms was significant for the latency impulse and the highest latency impulse was seen with Two Stage Control at all joint hardnesses, MK levels and soft stop settings. There was no difference between the latency impulse obtained with ATC and Two Stage Control algorithms.

The response at a particular joint hardness was dependent upon the controller algorithm that was used. The latency impulses with ATC and Manual Downshift were the greatest at the soft joint. This was in agreement with the finding by Frievalds and Eklund

[2] as mentioned in section 6.1. Similar to the torque impulse results, the maximum latency impulse with the Two Stage Control algorithm occurred at the medium joint.

119 The soft stop default resulted in higher latency impulse than the none condition at

all three algorithms. This was due to an increased duration of the run as a result of the

soft stop settings, which ramp down the tool speed gradually, instead of bringing the tool

to an abrupt stop.

The latency times for the joints used in this thesis were obtained from the

regression model fitted to Oh and Radwin’s data [18]. An extension of this work can

involve human testing with the Stanley tool at different joint hardnesses. Operator EMG

measurements can be used to determine the muscle latency under the given test

conditions, and these values can be compared to those obtained from Oh and Radwin’s

study.

6.5 Chapter summary

The observed effects of the input factors on the responses were discussed in this chapter

and the results were compared to those from previous ergonomic studies. Some attempts were made at explaining the observed differences. The findings of this thesis will be

summarized in chapter 7 along with the most important contributions of this thesis.

120

CHAPTER 7

CONCLUSIONS AND FUTURE WORK

This thesis studied the interaction between tool controller algorithms, arm and joint

variables with the help of an ergonomic assessment rig. The ergonomic impact of these factors was quantified by developing a set of metrics. The first section of this chapter summarizes the conclusions that were drawn from the statistical analyses of the results.

The contributions made by this thesis are listed in the second section. The final section of this chapter contains recommendations for future research based on the learning from this work.

7.1 Summary of major findings

This thesis studied the impact of four factors, which were the tightening algorithm, the joint stiffness, the arm mass and stiffness and the soft stop feature on the response of the human arm model. The different levels of these factors will be compared in this section based on the discussions in the previous chapter.

121 7.1.1 Tightening algorithm

The three controller algorithms had a significant effect on the evaluation metrics.

The Two stage algorithm was seen to have the worst ergonomic effect based on most of

the metrics that were developed. A higher torque impulse and latency impulse for the

Two stage algorithm was not surprising since these results take into account the duration

of reaction as well as magnitudes. Thus, clearly among the three tightening approaches,

the Two Stage Control was the worst as seen with this rig.

There was no statistically significant difference between the effect of ATC and

Manual Downshift for almost all the metrics. It is possible that this is due to the narrow range of joint hardnesses that were studied. Also, all three joints belonged to the medium

joint category according to ISO 5393 [26]. It would be important to test these algorithms

at very hard and very soft joints, and see whether one of these is ergonomically preferable

with such joints. This thesis used the ATC automatic mode which uses the default values

of ATC start torque, end torque, free speed and end speed. The ATC envelope can be

shaped in the custom mode and this may result in better ergonomic results than Manual

Downshift.

7.1.2 Joint stiffness

The effect of the joint stiffness was found to be significant for all the torque

impulse metrics, deflection and torque metrics. The response at the particular joint was

found to depend on the algorithm that was used. One limitation of this thesis is the range

of joint stiffnesses that were investigated. Since all three joints belong to the medium

122 joint category according to the ISO 5393 definition [26], the effect of the input factors

need to be determined at the extremes, i.e. very hard versus very soft joints. Investigating

a wider range of joint hardnesses would lead to a better understanding of the factor

effects.

7.1.3 Arm mass and stiffness

This levels of this factor had significant effect only for the deflection metrics

(positive, negative and range) and the peak torque positive. The torque impulses and the

latency impulse values were not different at the three mass and stiffness levels, at all

other factor combinations. The values of the mass and stiffness used in this rig may have

been set too high. Also, the mass and stiffness was combined as a single factor in this

research. The “fitness” of a person is what helps in countering the reaction force, and the

fitness is a function of both the mass and stiffness as independent factors. For example, a

person with low arm mass but better toned muscles resulting in higher stiffness falls into a high fitness category and would have a different response from someone in the low fitness category that has high arm mass and low muscle stiffness.

7.1.4 Soft stop feature

The two soft stop conditions had a significant effect on all the torque impulses

except at 70 % and 75 % and for the latency impulse. For these measures the default soft

stop parameters resulted in a higher impulse. The higher impulse values with soft stop

were attributed to a longer duration of the run due to a gradual release of the load on the

123 tool. The soft stop was seen to be effective in reducing the rebound (positive) reaction torque and deflection for most conditions.

The rig results show that using soft stop causes opposing effects on two types of metrics. Enabling the soft stop feature increases the area under the curve thus increasing effort, but it also decreases the rebound reaction and deflections which are related to arm oscillations. Testing human subjects and comparing these quantitative results (area under the curve and rebound reaction, deflection) to their subjective ratings of perceived exertion will demonstrate which of these effects is ergonomically better. This will help conclude whether or not using the soft stop is beneficial.

7.2 Contributions

The work described in this thesis made the following contributions:

i. Creation of a repeatable ergonomic assessment rig which contained a model of

the human arm mass and stiffness and incorporated other process variables (joint

stiffness, tool program parameters etc).

ii. A better understanding of DC torque tools, speed control algorithms, and

different parameters such as torque limits, angle limits, speed, acceleration etc

that control the tool. An important part of this thesis was learning to program the

Stanley tool controller and set values for the parameters in order to have a

successful rundown.

iii. Quantitative information provided by the experimental results that describes how

joint stiffness, arm characteristics, and tool controller settings interact. This

124 information can be used to develop optimum factor combinations for better

ergonomics.

iv. A set of ergonomic metrics were created using published research results for

evaluating the curves from the rig. Some new ways of assessing the response

were put forward, such as torque impulses at several different thresholds, and

using the muscle latency time in calculating the area under the reaction curve.

These metrics can be used in future for response curves from human subject

tests.

7.3 Recommendations for future work

The contributions of this thesis provide a foundation for future studies on the ergonomic impact of torque tools, program algorithms, joints and other associated factors. Based on the learning from this work, several recommendations for future research are made in this section.

The ergonomic assessment rig used in this research was based on the dynamic model developed by Radwin et al [11] [13]. The arm mass and stiffness elements were combined as a single factor and their values were chosen so that a wide range of population can be represented. But the effect of this factor was not significant on many of the metrics. The mass and stiffness should be separated into two independent variables for future experiments with the rig and a wider range of values need to be explored. Also

“fitness” of a person is a more appropriate factor since it is a function of both the mass and stiffness and its effect should be investigated. A person with “high fitness” (low

125 mass-high stiffness) may have a better control over the tool than someone with “low fitness” (high arm mass-low muscle stiffness). A damping element also needs to be included in the rig for a better simulation of the human arm.

Another limitation of this thesis was the levels of joint hardnesses that were selected, and all three levels being a form of medium joint. Future experiments need to investigate a wider range of joints, and incorporate levels from the hard and soft category.

This may also help bring out the differences between ATC and Manual Downshift, and help understand which algorithm is ergonomically better for a certain range of joint hardnesses.

The pilot study with the human subjects showed the differences between the deflections in the rig and humans. The mechanical elements of the human arm do not remain constant during a run as was indicated by the whole body movement of a person.

Future research should include the creation of an ergonomic arm tester for assessing mechanical properties of the human arm which include the effective stiffness, mass, and damping coefficients for different work conditions. These will act as inputs for the ergonomic assessment rig. The arm tester will also demonstrate how the arm properties vary during run down and the rig model of the human arm should be modified to account for these variations by using, for instance, a viscous liquid or other systems that can simulate these variations. Following this, the response of the rig and human subjects should be compared under identical conditions to verify how closely the rig replicates the human arm. It is also necessary to correlate the output of the rig with the biomechanical and physiological response of the tool operator, and thus develop measures that are more

126 accurate predictors of musculoskeletal discomfort or injury. An eventual goal would be to move away from human testing when a more accurate representation of the human arm is possible and strong correlation between the rig and human response has been established.

127

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2. Freivalds, A., Eklund, J., Reaction torque and operator stress while using powered nutrunners, Applied Ergonomics, Vol. 24 No.3, 158-164 (1993).

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4. Johnson, S.L., Childress, L.J., Powered screwdriver design and use: tool, task and operator effects, International Journal of Industrial Ergonomics, Vol. 2, 183-191 (1988).

5. Kihlberg, S., Lindbeck L., Kjellberg, A., Pneumatic tool torque reactions: reaction forces, displacement, muscle activity and discomfort in the hand arm system, Applied Ergonomics, Vol. 24 No.3, 165-173 (1993).

6. Kihlberg, S., Lindbeck L., Kjellberg, A., Pneumatic tool torque reactions: reaction forces, tool handle displacements and discomfort ratings during work with shut off nutrunners, Applied Ergonomics, Vol. 25 No.4, 242-247 (1994).

7. Kihlberg, S., Lindbeck L., Kjellberg, A., Discomfort from pneumatic tool torque reaction: acceptability limits, International Journal of Industrial Ergonomics, Vol. 15, 417-426 (1995).

8. Lin, J., McGorry, R.W., Chang, C., Hand-handle interface force and torque measurement system for pneumatic assembly tool operations: suggested enhancement to ISO 6544, Journal of Occupational and Environmental Hygiene, Vol. 4, 332-340 (2007).

9. Lin, J., McGorry, R.W., Dempsey, P.G., Chang, C., Handle displacement and operator responses to pneumatic nutrunner torque buildup, Applied Ergonomics, Vol. 37, 367-376 (2006).

128

10. Lin, J., Radwin, R.G., Fronczak, F.J., Richard, T.G., Forces associated with pneumatic power screwdriver operation: statics and dynamics, Ergonomics, Vol. 46 No.12, 1161-1177 (2003).

11. Lin, J., Radwin, R.G., Nembhard, D.A., Ergonomics applications of a mechanical model of the human operator in power hand tool operation, Journal of Occupational and Environmental Hygiene, Vol. 2, 111-119 (2005).

12. Lin, J., Radwin, R.G., Richard, T.G., Dynamic biomechanical model of the hand and arm in pistol grip power hand tool usage, Ergonomics, Vol. 44 No.3, 295-312 (2001).

13. Lin, J., Radwin, R.G., Richard, T.G., A single-degree-of-freedom dynamic model predicts the range of human responses to impulsive forces produced by power hand tools, Journal of biomechanics, Vol. 36, 1845-1852 (2003).

14. Lin, J., Radwin, R.G., Richard, T.G., Handle dynamics predictions for selected power hand tool applications, Human Factors, Vol. 45 No.4, 645-656 (2003).

15. Lindquist, B., Torque reaction in angled nutrunners, Applied Ergonomics, Vol. 24 No.3, 174-180 (1993).

16. McGorry, R.W., A system for the measurement of grip forces and applied moments during hand tool use, Applied Ergonomics, Vol. 32, 271-279 (2001).

17. Oh, S.A., Radwin, R.G., The effects of power hand tool dynamics and workstation design on handle kinematics and muscle activity, International Journal of Industrial Ergonomics, Vol. 20, 59-74 (1997).

18. Oh, S.A., Radwin, R.G., The influence of target torque and torque build-up time on physical stress in right angle nutrunner operation, Ergonomics, Vol. 41 No. 2, 188-206 (1998).

19. Oh, S.A., Radwin, R.G, Fronczak, F.J., A dynamic mechanical model for hand force in right angle nutrunner operation, Human Factors, Vol. 39 No.4, 497-506 (1997).

20. Osu, R., Gomi, H., Mulitijoint muscle regulation mechanism examined by measured human arm stiffness and EMG signals, Journal of Neurophysiology, Vol. 81, 1458-1468 (1999).

21. Radwin, R.G., Armstrong, T.J., Assessment of hand vibration exposure on an assembly line, American Industrial Hygiene Association Journal, Vol. 46 No.4, 211-219 (1985).

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22. Radwin, R.G., Armstrong, T.J., Chaffin, D.B., Power hand tool vibration effects on grip exertions, Ergonomics, Vol. 30 No.5, 833-855 (1987).

23. Radwin, R.G., VanBergeijk, E., Armstrong, T.J., Muscle response to pneumatic hand tool torque reaction forces, Ergonomics, Vol. 32 No.6, 655-673 (1989).

24. Sesto, M.E, Radwin, R.G., Block, W.F., Best, T.M., Upper limb dynamic responses to impulsive forces for selected assembly workers, Journal of Occupational and Environmental Hygiene , Vol. 3, 72-79 (2006).

25. ISO 6544: Hand held pneumatic assembly tools for installing threaded fasteners - Reaction torque and torque impulse measurements (1981).

26. ISO 5393: Rotary tools for threaded fasteners - Performance test method (1994).

27. Amini, A., Bambaeur, N., Candra, T., Kerlek, A., DC Torque tools ergonomic improvement, ME 565 Report, The Ohio State University, Unpublished (2005).

28. Bickford, John H., An introduction to the design and behavior of bolted joints, Third edition, Revised and expanded, Marcel Dekker, Inc. (1995).

29. Montgomery, Douglas C., Design and analysis of experiments, Wiley 5th Edition (2000).

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31. Wright, R.B., How tight should fasteners be tightened?, Fastener Technology International, June/July 2005.

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33. http://www.boltscience.com/pages/tighten.htm, “Methods of tightening threaded fasteners”, accessed 4 September 2007.

34. http://www.boltscience.com/pages/basics1.htm, “A Tutorial on the Basics of Bolted Joints”, accessed 4 September 2007.

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130

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131

APPENDIX A

SPRING RATE ANALYSIS OF AIR CYLINDER

This appendix contains a detailed description of the analysis of the double rod double acting cylinder to arrive at the force-displacement curves. The spring rate for different cylinder specifications was calculated from these curves. The Matlab script that relates the inlet pressure to spring rate is also included.

Pin Pin

d dr F F 1 2 Side 2 Side 1

Vex Vex

Figure A.1: Schematic of double rod cylinder and volume plenums

132 A.1 Calculations to obtain force-displacement curve

Consider a double acting double rod cylinder connected to two volume plenums one on each side of the piston shown in figure A.1. The piston is initially positioned at the mid-point of its stroke. The variables used are defined below and are followed by the calculations.

L = Cylinder stroke d = Cylinder bore

L Xc = Half of cylinder stroke = 2 dr = Diameter of piston rod

Vex = Volume of each plenum

Pin = Initial pressure on both sides of the piston

π d 2 Area of the piston, Ap = 4

2 π dr Area of piston rod, Ar = 4

π 22 Effective area on each side of the piston, A = Ap− Ar = {}dd− r 4

Initial volume on each side of the piston, Vin = VAXex+ ( c )

According to the ideal gas law, the pressure, volume and temperature are related as

PV= nRT (A.1) where P = absolute pressure (Pa)

V = volume (m3)

133 n = number of moles of the gas

R = Universal gas constant (8.3143 m3 Pa/mol -K)

T = absolute temperature (K)

Assuming that air behaves as an ideal gas, the number of moles of air on each side of the piston can be calculated as

PVin in n = (A.2) RT

At the initial position, the pressures, volumes and number of moles of air are equal on side 1 and side 2. Thus the piston is balanced and the resultant force on it equals zero. It is assumed that the piston is perfectly sealing and air does not leak from one side of the piston to the other, or through any of the air ports, so that the number of moles remains constant at all times. Now, when the piston moves a distance X towards side 2, the volumes on side 1 and side 2 are no longer the same and can be calculated as shown below.

VV1 =+ex AXcX( +) (A.3)

VVAXX2 =+ex( c −) (A.4)

nRT P1 = (A.5) VAXXex++( c )

nRT P2 = (A.6) VAXXex+−( c )

It is evident that the pressure on side 2 is greater than the pressure on side 1. Since force is pressure multiplied by area, the force F2 is greater than F1 and the net force (F) acting on the piston is calculated as:

134 FF= 212−= F PAPA − 1 (A.7)

⎡ nRT nRT ⎤ FA=−⎢ ⎥ (A.8) ⎣VAXXVAXXex+−() c ex ++( c )⎦

The resultant force (F) was plotted against incremental displacements (X) of the piston over the entire stroke to arrive at the force-displacement curve. This was done for different values of bores, strokes, external volumes and initial pressures. The dimensions of the cylinder were selected based on cylinder and external volume specifications that gave a force-displacement curve which had the most linear behavior over the entire stroke. The stiffness was calculated by fitting an equation of a line using Matlab. The cylinder specifications were also decided based on dimensions that could produce a range of spring rates that covered the arm stiffness values observed by Lin et al. [4] [6]. The

Matlab script for this analysis is included.

135 A.2 Matlab code to find spring rate of cylinder clc; clear; l=4; %stroke length in inches bore=1.5; % bore inches dr=7/16; %enter piston rod dia in inches vextern=8; %enter external volume in in^3' Vex=vextern*1.6387*(10^-5); % m^3 z=input('\nenterinitial pressure Pin\n');

%Absolute pressure = atm P + gauge P, 1atm =14.8psi, 1psi = 6894.8 pascals Pin=(14.8+z)*6894.8;

T=298; %Kelvin R=8.3143; %gas constant (m^3 pa/mole K) rc=bore*.0254/2 %cylinder radius in m Ap=rc^2*pi; %area of piston in m^2 r=dr*.0254/2; %piston rod radius (m) Ar=r^2*pi; %Area of piston rod in m^2 cylinderlength=l*.0254; %Stroke in m xc=cylinderlength/2; %half of stroke in m x1=xc-.001; Vin=Vex+(Ap-Ar).*xc; n=(Pin.*Vin)/(R.*T); x=-x1:.0001:x1; V1=Vex+(Ap-Ar).*(xc-x); V2=Vex+(Ap-Ar).*(xc+x); P1=(n.*R.*T)./V1; P2=(n.*R.*T)./V2; f=(P1.*(Ap-Ar))-(P2.*(Ap-Ar)); plot(x,f); grid on; axis ([-xc xc -200 200]); title(['Pin= ',num2str(z),'psi ',' stroke length =',num2str(l),'in bore= ',num2str(bore),'(in) external volume=',num2str(vextern),'(in^3)']) x1=min(x); x2=max(x); y1=min(f); y2=max(f); %equation of stiffness curve y=mx+c, m is spring rate p=polyfit(x,f,1);

136

APPENDIX B

DESIGN DRAWINGS

The drawings used in the design of different components of the ergonomic assessment rig are given in this appendix. These may be used for future design improvements and for duplicating various parts of the rig.

137 138

Figure B.1: Device to protect 50 lb load cell from bending loads

138 139

Figure B.2: Blue tube of load cell protection device

139 140

Figure B.3: Pink rod of load cell protection device that slides into the blue tube

140 141

Figure B.4: Orange rod of load cell protection device

141 14 2

Figure B.5: Pivot plate of the arm mass box

142 143

Figure B.6: Arm mass plate that connects to air cylinder

143 144

Figure B.7: Bottom plate of the arm mass box

144 145

Figure B.8: First slotted plate of arm mass box

145 146

Figure B.9: Second slotted plate of arm mass box

146 147

Figure B.10: Top plate of the arm mass box

147 148

Figure B.11: Larger size arm mass plate

148 149

Figure B.12: Smaller size arm mass plate

149

Figure B.13: Angle plate at clevis end of the rig

150

APPENDIX C

DATA SHEETS FOR SENSORS

The specification sheets for the sensors used in this research are documented in this appendix for easy reference. The load cell used was a Model 31 Mid range precision

Miniature load cell with a range of +/− 50 pounds. The LVDT used was a Schaevitz DC-

EC 2000, with a range of +/− 2 inches. A 300 degree/sec rate gyro (model number

ADXRS300) from Analog Devices was used for the pilot tests with humans.

151

C.1 Specification sheet for Model 31 Sensotec load cell

152

153

154

C.2 Calibration certificate for Model 31 Sensotec load cell

155

C.3 Installation instruction for Model 31 Sensotec load cell

156

157

C.4 Specification sheet for Schaevitz 2000 DC-EC LVDT

158

159

160

161

C.5 Calibration sheet for Schaevitz 2000 DC-EC LVDT

162

C.6 Installation and wiring instruction for Schaevitz 2000 DC-EC LVDT

163

C.7: Specification sheet for Analog Devices ADXRS300 rate gyro

164

165

APPENDIX D

STANLEY CONTROLLER PARAMETER SETS

The controller parameter sets that were used for this thesis are documented in this section. Three program algorithms were used, and each was used at the two soft stop conditions, resulting in six parameter sets. The parameter set names as stored in the controller refer to the program algorithm and soft stop setting used. The notation is explained in table A.1. The entire set of parameter values is tabulated in table A.2.

Notation Parameter set name

MDS 0.000 Manual Downshift with no soft stop

MDS 0.075 Manual Downshift with default soft stop

TSC 0.000 Two Stage Control with no soft stop TSC 0.075 Two Stage Control with default soft stop ATC 0.000 ATC with no soft stop ATC 0.075 ATC with default soft stop

Table D.1: Explanation of parameter set names

166

Parameter set name Parameters MDS SS 0.000 MDS SS 0.075 TSC SS 0.000 TSC SS 0.075 ATC SS 0.000 ATC SS 0.075 Step Name Secure Secure Stage 1 Stage 2 Stage 1 Stage 2 Secure Secure Strategy TC/AM TC/AM TC/AM TC/AM TC/AM TC/AM TC/AM TC/AM Fastener replace 66 66 66 66 66 66 66 66 High torque 66 66 66 66 66 66 66 66 Target torque 60 60 45 60 45 60 60 60 Low torque 60 60 45 60 45 60 60 60 Snug torque 6 6 6 6 6 6 6 6 High angle 999999 999999 999999 999999 999999 999999 999999 999999 Low angle 0 0 0 0 0 0 0 0 Accumulate angle No No No No No No No No 167 Motor power 100 100 100 100 100 100 100 100 Acceleration 5000 5000 5000 500 5000 500 5000 5000 Tool speed 636 636 636 159 636 159 ATC ATC Downshift speed 159 159 0 0 0 0 ATC ATC Downshift torque 30 30 0 0 0 0 ATC ATC Cycle abort 5 5 5 5 5 5 5 5 Delay between 0 0 0.05 0 0.05 0 0 0 steps Current off 0.000 0.001 0.000 0.000 0.000 0.001 0.000 0.001 Current hold 0.000 0.025 0.000 0.000 0.000 0.025 0.000 0.025 Current ramp 0.000 0.075 0.000 0.000 0.000 0.075 0.000 0.075

167 Table D.2: Stanley controller parameter values

APPENDIX E

MATLAB CODES - TORQUE IMPULSE AND LATENCY IMPULSE

The Matlab codes that used to calculate torque impulse and latency impulse are documented in this appendix.

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E.1 Matlab code for torque impulse clear all; clc; x=input('\nenter percentage of target torque such as x%\n');; % threshold value of the torque threshold=(x/100)*60; file=xlsread('run_1.xls'); time=file(:,1); force=file(:,2); distance=file(:,3); torque=(-1.*force).*0.470; % 18.5 inches length of handle n1=length(time);

%finding the first point in the main array after which the torque crosses threshold n=0; for i=1:n1; if (torque(i)<=threshold) n=n+1; else break; end; end;

%finding curve portion above threshold torque j=1; for i=1:n1 if(torque(i)>= threshold) newtime(j)=time(i); newtorque(j)=torque(i); j=j+1; end end if (j==1) newtime=[]; newtorque=[]; end

%split first time t1=length(newtime)-1; for i=1:t1 if((newtime(i+1)-newtime(i))>0.00025) j=i+1; break; end end 169

if(i==t1) newtime2=[]; newtorque2=[]; else newtime2=newtime(j:t1+1); newtorque2=newtorque(j:t1+1); end newtime1=newtime(1:j-1); newtorque1=newtorque(1:j-1);

%interpolate newtime1,newtorque1 l1=length(newtime1); Xo_beg1=time(n); %Beginning of newtime1,newtorque1 Yo_beg1=torque(n); X1_beg1=time(n+1); Y1_beg1=torque(n+1); X_beg1=Xo_beg1 + ((threshold-Yo_beg1)/(Y1_beg1- Yo_beg1))*(X1_beg1-Xo_beg1); Impulse_beg1=abs(0.5*(X1_beg1-X_beg1)*(Y1_beg1-threshold));

Xo_end1=time(n+l1+1); %End of newtime1,newtorque1 Yo_end1=torque(n+l1+1); X1_end1=time(n+l1); Y1_end1=torque(n+l1); X_end1=Xo_end1 + ((threshold-Yo_end1)/(Y1_end1- Yo_end1))*(X1_end1-Xo_end1); Impulse_end1=abs(0.5*(X1_end1-X_end1)*(Y1_end1-threshold));

%split second time t2=length(newtime2)-1; j=1; for i=1:t2 if((newtime2(i+1)-newtime2(i))>0.00025) j=i+1; break; end end if (i==t2)

newtime2b=[]; newtorque2b=[]; newtime2a=newtime2; newtorque2a=newtorque2; else newtime2b=newtime2(j:t2+1); 170

newtorque2b=newtorque2(j:t2+1); newtime2a=newtime2(1:j-1); newtorque2a=newtorque2(1:j-1); end

%interpolate newtime2a,newtime2b l2a=length(newtime2a); if (l2a>0) %Beginning of newtime2a,newtorque2a firstindex2a=((newtime2a(1)-time(1))/0.0002)+1; beg_2a=int16(firstindex2a - 1); index1_2a=int16(firstindex2a); Xo_beg2a=time(beg_2a); Yo_beg2a=torque(beg_2a); X1_beg2a=time(index1_2a); Y1_beg2a=torque(index1_2a); X_beg2a=Xo_beg2a + ((threshold-Yo_beg2a)/(Y1_beg2a- Yo_beg2a))*(X1_beg2a-Xo_beg2a); Impulse_beg2a=abs(0.5*(X1_beg2a-X_beg2a)*(Y1_beg2a-threshold)); else Impulse_beg2a=0; end; if (l2a>0) %End of newtime2a,newtorque2a indexlast2a=int16(index1_2a+l2a-1); end_2a=int16(indexlast2a+1); Xo_end2a=time(indexlast2a); Yo_end2a=torque(indexlast2a); X1_end2a=time(end_2a); Y1_end2a=torque(end_2a); X_end2a=Xo_end2a + ((threshold-Yo_end2a)/(Y1_end2a- Yo_end2a))*(X1_end2a-Xo_end2a); Impulse_end2a=abs(0.5*(X1_end2a-X_end2a)*(Y1_end2a-threshold)); else Impulse_end2a=0; end;

%interpolate newtime2b,newtime2b l2b=length(newtime2b); if (l2b>0) %Beginning of newtime2b,newtorque2b firstindex2b=((newtime2b(1)-time(1))/0.0002)+1; beg_2b=int16(firstindex2b - 1); index1_2b=int16(firstindex2b); Xo_beg2b=time(beg_2b); Yo_beg2b=torque(beg_2b); X1_beg2b=time(index1_2b); Y1_beg2b=torque(index1_2b); X_beg2b=Xo_beg2b + ((threshold-Yo_beg2b)/(Y1_beg2b- Yo_beg2b))*(X1_beg2b-Xo_beg2b); Impulse_beg2b=abs(0.5*(X1_beg2b-X_beg2b)*(Y1_beg2b-threshold)); else Impulse_beg2b=0; end; 171

if (l2b>0) %End of newtime2b,newtorque2b indexlast2b=int16(index1_2b+l2b-1); end_2b=int16(indexlast2b+1); Xo_end2b=time(indexlast2b); Yo_end2b=torque(indexlast2b); X1_end2b=time(end_2b); Y1_end2b=torque(end_2b);

X_end2b=Xo_end2b + ((threshold-Yo_end2b)/(Y1_end2b- Yo_end2b))*(X1_end2b-Xo_end2b); Impulse_end2b=abs(0.5*(X1_end2b-X_end2b)*(Y1_end2b-threshold)); else Impulse_end2b=0; end;

%Torque-time curve for impulse finaltime1=[X_beg1;newtime1';X_end1]; finaltorque1=[threshold;newtorque1';threshold]; if(l2a>0) finaltime2a=[X_beg2a;newtime2a';X_end2a]; finaltorque2a=[threshold;newtorque2a';threshold]; else finaltime2a=[]; finaltorque2a=[]; end; if(l2b>0) finaltime2b=[X_beg2b;newtime2b';X_end2b]; finaltorque2b=[threshold;newtorque2b';threshold]; else finaltime2b=[]; finaltorque2b=[]; end;

%add impulses impulse1=trapz(newtorque1)*0.0002; impulse2a=trapz(newtorque2a)*0.0002; impulse2b=trapz(newtorque2b)*0.0002; impulse_A=impulse1+impulse2a+impulse2b; impulse_B=Impulse_beg1+Impulse_end1+Impulse_beg2a+Impulse_end2a+I mpulse_beg2b+Impulse_end2b; impulse_total=impulse_A+impulse_B; %total torque impulse

%Plots figure(1); plot(time,torque); grid on; title(‘Entire torque-time curve’); xlabel(‘Time (s)’); ylabel(‘Torque (Nm)’); 172

figure(2); plot(finaltime1,finaltorque1,'r'); grid on; hold on; plot(finaltime2a,finaltorque2a,'b'); hold on; plot(finaltime2b,finaltorque2b,'g'); title('Impulse curve'); xlabel('Time (s)'); ylabel('Torque (Nm);')

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E.2 Matlab code for latency impulse clear all; clc; file=xlsread('run_1.xls'); time=file(:,1); force=file(:,2); distance=file(:,3); torque=(-1.*force).*0.470; % 18.5 inches length of handle n1=length(time); peakdistance=max(abs(distance));

%finding index of peak deflection point for i=1:n1 if(abs(distance(i))== peakdistance) j=i; break; end; end;

%emg latency clock starts when deflection is equal to 0 for i=j:-1:1 if(distance(i)>= 0) emg_startpt=i; break; end; end;

if(i==1) emg_startpt=[]; break; end;

% impulse area calculation stops when torque=0 peaktorque=max(torque); %finding index for peak torque point for i=1:n1 if(torque(i)== peaktorque) k=i; break; end; end; for i=k:1:n1 if(torque(i)<=0) emg_endpt=i; break; end; end; 174

latency = 0.0614; time_after_latency=time(emg_startpt)+ latency; for i = 1:n1 if(time(i)= time_after_latency) n = i; break; end; end; %Curve with latency excluded torque1_new=torque(n:emg_endpt,:); time1_new=time(n:emg_endpt,:); impulse1=trapz(torque1_new)*0.0002

%Interpolation at Beginning of emg clock indexstart=int16(emg_startpt); Xo_beg1=time(indexstart+1); Yo_beg1=distance(indexstart+1); X1_beg1=time(indexstart); Y1_beg1=distance(indexstart); Y_beg1=0; X_beg1=Xo_beg1 + ((Y_beg1-Yo_beg1)/(Y1_beg1-Yo_beg1))*(X1_beg1- Xo_beg1); %Interpolation at end of emg clock timex=X_beg1+latency; x=((timex-time(1))/0.0002)+1; indexlatency=int16(x); Xo_latency1=time(indexlatency-1); Yo_latency1=torque(indexlatency-1); X1_latency1=time(indexlatency); Y1_latency1=torque(indexlatency); X_latency1=timex; Y_latency1=Yo_latency1 + ((X_latency1-Xo_latency1)/(X1_latency1- Xo_latency1))*(Y1_latency1-Yo_latency1); Impulse_latency=abs(0.5*(X_latency1-Xo_latency1)*(Y_latency1- Yo_latency1)); %Interpolation at end of torque curve indexend=int16(emg_endpt); Xo_end1=time(indexend); Yo_end1=torque(indexend); X1_end1=time(indexend-1); Y1_end1=torque(indexend-1); Y_end1=0; X_end1=Xo_end1 + ((Y_end1-Yo_end1)/(Y1_end1-Yo_end1))*(X1_end1- Xo_end1); Impulse_end=abs(0.5*(X_end1-Xo_end1)*(Y_end1-Yo_end1));

%Calculate total latency impulse latency_impulse=impulse1-Impulse_end-Impulse_latency; 175