Masters Thesis

A Thesis

entitled

Monitoring of Surface Grinding process using Acoustic Emission (AE) with

emphasis on Cutting Fluid selection

Tejas V. Nisal

Submitted to the Graduate Faculty as partial fulfillment of the requirements for

the Master of Science Degree in Industrial Engineering

______Dr. Ioan D. Marinescu, Committee Chair

______Dr. Efstratios Nikolaidis, Committee Member

______Dr. Matthew Franchetti, Committee Member

______Dr. Patricia R. Komuniecki, Dean College of Graduate Studies

The University of Toledo August 2014

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.

An abstract of

Monitoring of Surface Grinding process using Acoustic Emission (AE) with emphasis on Cutting Fluid selection

Tejas V. Nisal

Submitted to the Graduate Faculty as partial fulfillment of the requirements for The Master of Science Degree in Industrial Engineering

The University of Toledo

August 2014

Correct selection of cutting fluid is an important step in all machining operations. In this study, experiments were designed and conducted on AISI

52100 steel to determine the effects of using different cutting fluids in Surface

Grinding. The grinding parameters varied were wheel speed, feed, depth of cut and type of cutting fluid. The grinding responses studied here were Acoustic

Emission (AE) Signals, Normal and Tangential Forces on the workpiece surface,

Grinding Temperature and Surface Roughness. Potential of Acoustic Emission technique as a tool to provide efficient real-time knowledge and monitoring of the grinding process, is tested in this research. AERMS values were used to analyses the process characteristics. This paper proposes four different statistical models for predicting Grinding Temperature, Force, Acoustic Emission (AERMS) and Roughness, based on grinding parameters. This research concludes that the selection of Cutting Fluids influence the Surface finish, AE signals, Temperature

iii and grinding Forces measured. Further, prediction of surface roughness during the grinding process using AE signal monitoring is demonstrated in this work.

Acknowledgements

First of all, I would like to express my sincere gratitude to my advisor, Dr.

Marinescu, for giving me opportunity to do research under his supervision and for his exemplary guidance during my studies at The University of Toledo. I learned a lot through all the expert ideas provided by Dr. Marinescu. He always motivated me by giving me a couple of extra things to do in my research, just to keep me on my toes. I am also grateful to the thesis defense committee members

Dr. Nikolaidis and Dr. Franchetti for spending time to read this thesis and providing useful suggestions. Special thanks to the faculty and staff at the MIME department for giving me the opportunity to continue my education here and kindly guiding me in maintaining the academic progress.

I express my gratitude towards Mr. Todd Gearig, and Mr. John Jaegley, for the technical assistance throughout the research, in fabricating the fixtures and running the grinding machine. I would also like to thank Dr. Weismiller from Master Chemical Corp. for providing the grinding fluids and also for his useful suggestions. I highly appreciate the prompt technical support and friendly guidance provided by Mr. Tom Gigliotti and Dr. Ronnie Miller of Physical

Acoustic Corp for using Acoustic Emission (AE) system.

A vote of thanks to all my colleagues-cum-friends at Precision Micro-

Machining Center (PMMC), especially to Mr. Yin Guoxu and Mr. Aditya

Dhulubulu, as we always worked together, and I believe that we made a good team.

Last but not least, for always supporting me and keeping me motivated I specially thank my family and close friends in Toledo and elsewhere.

Contents

Abstract ...... iii

Acknowledgments ...... v

Table of Contents ...... vii

List of Tables ...... x

List of Figures ...... xi

List of Abbreviation ...... xiii

List of symbols ...... xiv

1. Introduction

1.1 Overview ...... 1

1.2 Objective ...... 2

2. Fundamentals and Literature Review

2.1 Cutting Fluids ...... 4

2.1.1 Neat cutting oils ...... 4

2.1.2 Gaseous cutting fluids ...... 6

2.1.3 Synthetic Fluids ...... 7

2.1.4 Emulsions cutting Fluids ...... 7

vii

2.1.5 Semi-synthetic fluids ...... 7

2.2 Monitoring of Surface grinding process ...... 8

2.3 Acoustic Emission ......

2.3.1 Introduction to Acoustic Emission (AE) ...... 12

2.3.2 Use of Acoustic Emission in Manufacturing . . . . 14

2.3.3 Acoustic Emission Signal recording and analysis 16

2.3.4 Acoustic Emission Setup

a. Sensors...... 22

b. Calibration ...... 24

c. Sensor Coupling ...... 25

d. Silicon rubber sealant ...... 26

2.4 AE Vs. Grinding Mechanism (theoretical)

2.4.1 Overview ...... 27

2.4.2 AE Vs. MRR (Literature review)...... 30

2.4.3 AE Vs. Forces (Literature review)...... 32

2.4.4 AE Vs. Temperature (Literature review) ...... 33

2.4.5 AE Vs. Surface roughness (Literature review). . . . 35

2.4.6 AE Vs. Fluids (Literature review) ...... 38

2.5 Literature Review of use of AE related to Grinding Mechanism 40

3. Experimentation ...... 43

4. Results and Analysis ...... 50

viii

4.1 Effect of cutting fluid selection on AE, Normal forces,

Roughness and Temperature ...... 55

4.2 Effect of grinding parameters on AE, Normal Forces, Roughness

and Temperature ...... 63

4.3 Regression Modeling ...... 70

4.3.1 Grinding Force Model ...... 74

4.3.2 Acoustic Emission (AERMS) Model ...... 77

4.3.3 Surface Roughness Model ...... 80

4.3.4 Grinding Temperature Model ...... 83

4.4 Prediction of Surface Roughness (Ra) based on AERMS monitoring 86

4.5 Validation of Model ...... 88

5. Conclusion ...... 90

Reference ...... 94

List of Tables

Table 1.1 Effects of different factors on AE ...... 15

Table 3.1 The specifics of the grinding conditions ...... 44

Table 3.2 Configuration parameters for AE monitoring system . . . . . 46

Table 3.3 Physical properties of four fluids used for the study ...... 49

Table 4.1 Design of Experiment ...... 52

Table 4.2 Grinding fluid characteristics ...... 59

Table 4.3 Physical properties of four fluids used for the study ...... 55

Table 4.4 Composition/information on ingredients ...... 60

Table 4.6 Validation of experiments ...... 89

List of Figures

Figure 1.1 Classification of Cutting fluids ...... 7

Figure 2.1 Parameters used to characterize emission events ...... 18

Figure 2.2 AE signals and power spectrum graphs ...... 20

Figure 2.3 Block diagram of Acoustic Emission system ...... 22

Figure 2.4 Schematic Cut section diagram of piezoelectric sensor ...... 23

Figure 2.5 Photo of actual sensors available in different sizes ...... 23

Figure 2.6 Sources of AE in Grinding at the grinding wheel-workpiece

contact area ...... 28

Figure 3.1 Experiment set-up schematic ...... 44

Figure 3.2 Photo of the complete set-up for the experiments ...... 44

Figure 3.3 Photo of the workpiece and fixture set-up for experiment . . . . 44

Figure 4.1 AERMS Values of two different grinding parameters and four

replicates ...... 57

Figure 4.2 Shows variation in AE, Surface roughness, Normal Forces, and

Temperature measurement with selection of four different

cutting fluids ...... 58

Figure 4.3 Shows variation in AE, Surface roughness, Normal Forces, and

Temperature measurement with increase in Depth of cut ...... 66

Figure 4.4 Shows variation in AE, Surface roughness, Normal Forces, and

Temperature measurement with increase in Cutting speed . . . . 67

Figure 4.5 Shows variation in AE, Surface roughness, Normal Forces, and

Temperature measurement with increase in Feed rate ...... 68

Figure 4.6 Graph of Cutting force vs. DOC Vs. Feed and viscosity (Fluid) 75

Figure 4.7 Normal probability plot for normal force (F) ...... 76

Figure 4.8 Graph of AERMS vs. DOC vs. Feed and viscosity (Fluid) ...... 78

Figure 4.9 Normal probability plot for normal AERMS ...... 79

Figure 4.10 Graph of AERMS vs. DOC Vs. Feed and viscosity (Fluid) ...... 81

Figure 4.11 Normal probability plot for Surface roughness (Ra) ...... 82

xii

Figure 4.12 Graph of Temperature vs. DOC vs. Feed and viscosity (Fluid) 84

Figure 4.13 Normal Probability plot for Temperature (˚F) ...... 85

Figure 4.14 Correlation Graphs of Acoustic Emission (AERMS) Vs. Surface

roughness (Ra) ...... 87

xiii

List of Abbreviations

AE Acoustic Emission

AET Acoustic Emission Technology

RMS Root Mean Square

AERMS Root Mean Square of the AE signal

CNC Computerized Numerical Controls

ASTM American Society of Testing and Materials

NDT Non-Destructive testing

xiv

List of symbols

η Fluid viscosity mPa S

F Normal force in Newton (N)

C Regression Constant

α Regression constant

β Regression constant

γ Regression constant

δ Regression constant

Ra Average Surface Roughness d Depth of cut

Vc Grinding wheel peripheral speed f Feed rate, or workpiece speed

E Young’s modulus

Chapter 1.

Introduction

1.1 Overview

Surface Grinding is a machining process that employs an abrasive grinding wheel which rotates at high speed to remove material from a comparatively softer workpiece surface. Surface grinding is a widely used machining process in the industry; because it can produce unparalleled surface quality, precise geometry, and better surface finish; with lower costs and higher material removal rates. Surface grinding machines can produce perfectly flat and/or smooth machine-part surfaces. In order to produce precision parts, these grinding machines must use grinding wheels whose dimensions are perfectly suited to the task. The grinding machine spins the grinding wheel very fast and holds it perfectly in place as the machine’s precision translation table moves the part being ground underneath the grinding wheel. Surface grinders are capable of removing as little as one ten-thousandth of an inch or less of material on each pass over a part.

1.2 Objective

In the past, researchers have worked towards implementing real time monitoring of the grinding process, using different techniques like measurement of - grinding forces, surface temperature, vibrations, ultrasonic emissions and acoustic emissions. Over past two decades there has been much development and use of AE technique, to monitor the grinding process. Few researchers in the past have reported the Comparison of Grinding forces measured with the AE signal collected in the grinding Passes. No research has been reported till date that compares AE signals with measured surface temperature, surface roughness, for in particular to surface grinding.

Secondly, in all the machining operations cutting fluids play very important role towards the efficiency of the cutting process as well as the quality of the work. Especially if the process is an automated production unit using precision machining techniques like Computerized Numerical controls (CNC’s); the quality of the surface generated in the machining operation is highly influenced by the quality of the cutting fluid being used in the operation and the choice of cutting fluid selected. The primary objective of this research is summarized below:

1) Primary research objective in this work is to select four different grinding

fluids and analyses the effect of selection of different cutting fluids has on

grinding process, Viz. AE, Grinding forces, Surface temperature and 2

surface roughness. And accordingly rank the cutting fluids based on the

surface quality obtained.

2) Second objective of this research is to study the scope of real time

monitoring the Grinding process using measurement of AE, Grinding

forces and surface temperature.

3) Third objective of this research is to establish a statistical model showing

the relationship between different control and response variables of the

grinding process, in order to predict the surface quality before the

grinding pass is finished.

Chapter 2.

Fundamentals and Literature Review

1.1 Cutting Fluids

The use of coolants for machining was first introduced in machining processes by Taylor in 1907. Using water as coolant, 40% increase in cutting speed was achieved when machining steel with high speed steel tools

Cutting fluids are used in metal machining for a variety of reasons; such as reducing workpiece thermal deformation, improving tool life, improving surface finish and flushing away chips from the cutting zone. Cutting fluids assist to improve the efficiency of machining in terms of increased tool life, better surface finish and dimensional accuracy, reduced cutting forces and reduced vibrations [M.A. E Baradie, 1996]

There is a large range of variety of cutting fluids available in the market today for use in industry. Though, optimal method is not always implemented in industries for proper selection and application of cutting fluids. When optimal selection and application method is involved, cutting fluids will guaranty to

4 allow higher cutting depth of cuts and feed rates and therefore increase productivity and reduce costs. Correct application of cutting fluids, is another important next step after correct fluid is selected. Application method of cutting fluid will further help in increasing the tools life, decreasing surface roughness, decrease power consumption, and provide better dimensional accuracy.

Principle methods of cutting fluid application include Flood Application of

Fluid, Jet Application of Fluid, and Mist Application of Fluid. [M.A. E Baradie,

1996]

The significant effects of cutting fluid selection on machining quality will be discussed in more detail in later chapters. Cutting fluids are basically classified into three major types; Neat cutting oils, Gases and Water- soluble

Fluids. [J. O. Cookson, 1977]

See Figure 1 for detail classification of Fluids

Neat cutting oils

Neat cutting oils are non-emulsifiable and are used in machining operations in an undiluted form. Neat cutting oils provide the best lubrication, but the poorest cooling characteristics among cutting fluids, in fact they are easily likely of burning. This is the reason they are not widely used in the

5 industry. They are composed of a base mineral or petroleum oil and often contains polar lubricants such as fats, vegetable oils and esters as well as extreme pressure additives such as Chlorine, Sulphur and Phosphorus. For applications where a metalworking fluid with better lubricating properties, and to obtain better surface integrity is needed, a neat cutting oils cutting fluid is recommended.

Gaseous cutting fluids

Gaseous cutting fluids like carbon dioxide or Nitrogen gas, instead of liquid cutting fluids have found their way in industry’s like aeronautics. Gaseous cutting fluids have advantages over liquid cutting fluids. Few of them are like, cleaner machining zone, control over cooling of the tools, and no cutting fluid penetration.

The further mentioned cutting fluids fall in the category of water-miscible cutting fluids. For machining with high cutting velocities, a water-miscible fluid is often preferred due to its better cooling properties [E. Brinksmeier, C. Heinzel,

M. Wittmann, 1999]

Synthetic Fluids

Synthetic Fluids contain no petroleum or mineral oil base and instead are formulated from alkaline inorganic and organic compounds along with additives for corrosion inhibition. They are generally used in a diluted form (usual concentration = 3 to 10%). Synthetic fluids often provide the best cooling performance among all cutting fluids.

Emulsions cutting Fluids

Emulsions cutting Fluids are basically soluble oils which form an emulsion when mixed with water. The concentrate consists of a base mineral oil and emulsifiers to help produce a stable emulsion. They are used in a diluted form (usual concentration = 3 to 10%) and provide good lubrication and heat transfer performance. They are widely used in industry and are the least expensive among all cutting fluids.

Semi-synthetic fluids

Semi-synthetic fluids are combination of synthetic and soluble oil fluids and have characteristics common to both types. The cost and heat transfer performance of semi-synthetic fluids lay between those of synthetic and soluble oil fluids.

Figure 1.1: Classification of Cutting fluids [M.A. E Baradie, 1996K.H.W. Seah, X. Li 1995]

2.2 Monitoring of Surface grinding process

Grinding is a prevalent finishing process in mass production for manufacturing precision components such as bearings, gears, cams, shafts, etc. due to its capability for high surface finish, dimensional accuracy, and process reliability. Many components produced by grinding are used in rolling contact applications in machines, engines, and various mechanical systems for which fatigue failure is of great concern.

On the contrary to all usefulness of surface grinding and its wide range of applications; it is still a very complex machining process, in which many

8 randomly shaped cutting edges work simultaneously. Accurate surface grinding process monitoring and modeling for predicting the resulting output quality is very difficult considering that abrasive processes are complex, non-stationary in nature and have a large number of parameters. There are many factors those affect the surface quality obtained from grinding process: wheel speed, dressing conditions, depth of cut, feed rate, machine setup, cutting fluid quality etc. Apart from these factors the machine efficiency and working conditions will also affect the surface quality. Surface grinding process monitoring is therefore necessary for detecting the unexpected malfunctions which may occur in the process.

Information recorded during processing monitoring data can also help optimizing the process for future. [W. Hundt, 1994]

In order to develop an efficient monitoring system for surface grinding efforts have been made earlier to find a correlation between above mentioned process parameters with output parameters like grinding forces, surface temperature, vibrations, ultrasonic emissions and acoustic emissions [Amin

Mokbel, 2000]. Among all of these output parameters; measurement and analysis of Acoustic emission (AE) has been proved to be more suitable in providing the detailed real-time knowledge of grinding conditions. [W. Hundt, 1994] In this presented research acoustic emission signals are collected along with temperature measurement, surface roughness measurement and forces measurement, to get the better understanding of grinding phenomenon.

When a solid is subjected to stress at certain levels, discrete acoustic, wave packets are generated which can be detected by transducers placed on, or in acoustic contact with the solid, this is called as Acoustic Emission. Most materials which are designed to withstand high stress levels emit acoustic energy when stressed, including the well-known alloys such as steels, cast irons and alloys of aluminum. Glasses and fibers as wells as concrete and ceramic materials also emit AE. Modern Equipment’s can detect emissions produced by very small strain levels as low as 0.05% or less in certain materials and approaching fracture, emissions can be very easily detected from most engineering materials.

In last couple of decades, Acoustic emission technique (AET) is studied and recognized as one of the important and advanced nondestructive evaluation tools those have the capabilities for real time process monitoring applications. A transducer or sensor is acoustically coupled to a workpiece undergoing dynamic changes detects the acoustic energy emitted by the workpiece and gives information about the dynamic changes taking place in the sample. Effort has also been directed towards developing on-line condition monitoring systems that make use of features extracted from the AE signal. [T. Jayakumar, 2005]

The detection of different grinding phenomena is a very important consideration when carrying out grinding process monitoring. Multiple classification of the acoustic emission signals extracted during burn and chatter anomalies using genetic programming were studied by [Qiang Liu et al,

Mar2006]. The differentiation between each of the occurrence was identified from acoustic emission signals being converted to the frequency–time domains using

Short Time Fourier Transforms. The experiments were set up to take advantage of the same material characteristic and the slight variations in machining parameters to achieve the successful monitoring anomaly of interest. Looking at the results displayed in this paper, it can be seen that both chatter and burn provide distinct signatures which can be correlated to the unwanted events. [M.

Anthony Xavior, 2009]

Comparison of fatigue life and acoustic emission signals with the impact of surface integrity by hard turning versus grinding on rolling contact fatigue was done by A. W. Warren and Y. B. Guo [A. W. Warren, Aug2007]. The surface topographies show that skewness of the as-ground surface is much more negative than the as-turned one while other surface parameters are equivalent.

The turned surface has a thicker strain hardened zone and a thinner thermal affected zone than those of the ground one. The ground surface has higher micro- and Nano hardness on surface and in the subsurface than the turned one.

The amplitude of acoustic emission signal is the most stable and sensitive signal to fatigue failure.

2.3 Acoustic Emission

Introduction to Acoustic Emission (AE):

American Society of Testing and Materials (ASTM) defines AE as “a transient elastic stress wave generated by the rapid release of energy from a localized source within a material.” Acoustic means pertaining to the sense or organs of hearing, to sound, or to the science of sound

Dornfeld, (2008)]

Acoustic Emission signals can be seen as visual display of the transducers response to the acoustic energy generated within the material under stress. These signals are displayed in form of waves; on the monitor screens of the devices

12 those are used in given case. Though these Acoustic Emission (AE) signal waves are similar to the sound waves propagated in air and other fluids, but are more complex because the solid material through which they are travelling may resists the shear forces. In general, AE signals can be classified as two types, namely, continuous signals and burst signals. Continuous signals are associated with dislocation movement through the crystal lattice and friction between contacting surfaces. Burst signals are short duration pulses generated by a discrete release of high amplitude strain energy such as during crack initiation and growth and micro yielding. There are different methods by which AE signals can be, analyzed including absolute energy, root mean square energy (RMS), amplitude, count and average frequency. Data can be collected in both time driven and hit driven format. Either of the above method can be used to find the correlations with other grinding parameters [Javad Akbari, 1994, A.C Lucia, 1976]. In this paper, the amplitude and root mean square (RMS) values of AE raw signals were used to analyze the grinding process characteristics. Both time-driven and hit- driven data collection is possible, although only hit-driven data was collected for this study.

General applications of Acoustic Emission in

Manufacturing:

Previous studies indicated that the major sources of AE during metal cutting are the following; a) shear and plastic deformation of work piece, b)

Contact between metal chip and tool rake surface c) Contact between work piece and tool flank surface. D) Collision entangling and breakage of chips. [R. K.

Miller, 1987].

AE differs from other nondestructive testing’s (NDT’s) in two main aspects: The first difference pertains to the origin of the signal. Instead of supplying energy to the object under examination, AET simply listens for the energy released by the object. The second difference is that AET deals with dynamic processes, or changes, in a material. That means the ability to discern between developing and stagnant defects is significant. AET can distinguish between burst signals and continuous signals.

Table 1 summarizes the effects of different manufacturing related factors upon Acoustic Emission signals.

Table 2.1 : Effects of different factors on AE Factors increasing the amplitude AE Factors decreasing the amplitude of AE Rough Relief Smooth Relief High Hardness Low hardness Surface anisotropy Surface isotropy In-homogeneity of surface properties Homogeneity of surface properties Coarse gain fine grains Absence of texture presence of texture Low toughness high toughness Defects in surface layer absence of defects in surface layer Low temperature Elevated Temperature High sliding velocity Slow sliding velocity Heavy load Light load non stationary friction conditions Stationary friction condition Abrasive wear Adhesive wear Failure due to micro cutting Failure due to plastic deformation Dry friction presence of lubricant Boundary friction with liquid lubricant Boundary friction with solid lubricants presence of surfactants Absence of Surfactant presence of corrosive environment Absence of corrosive environment

AE’s have also been measured and recorded in polymers, wood, and concrete, ceramics among other materials. The frequency range for acoustic emission technique is mostly between 20 KHz and 1 MHz this is more than the vibrations created by the machine spindles, rotor, foundations, etc. AET can be

15 used presence of different noises. From point of view of metal cutting, most of the work emitting the required AE signals is done in the frequency range of 100 to 500 kHz. In this frequency range it is safe to trust the sensor dynamics and less worry of the AE signals getting excessively contaminated background noise of the machine or the cutting fluid hitting the workpiece.

The only down side of using Acoustic emission technology is that the acoustic pulses have energies in some cases near to the lower level of detection of piezoelectric transducers. Which means that sophisticated and expensive apparatus must be used sometimes, to overcome the difficulty to differentiate the background noise.

Acoustic Emission Signal recording and analysis:

AE signals can be classified into two types; continuous signals and burst signals. Continuous signals are generated from the dislocation movement through the crystal lattice and friction between contacting surfaces, when the material is under stress or strain. Continuous AE is related to plastic deformation, Metal corrosion and other physical phenomena [R. Babel, 2012]

Whereas, Burst signals are short duration AE associated with a discrete release of high amplitude strain energy during crack initiation and growth. There are several ways of analyzing AE signals which can be measuring absolute energy, root mean square (RMS), amplitude, counts, and average frequency.

Collection of data can be done in two ways Viz. time-driven and hit driven data collection is possible. Using the Acoustic Emission software, time-driven parameters are sampled at a fixed user-defined frequency, while hit-driven data are collected when and only when a preset threshold value has been surpassed, only one kind of data collection is possible at a time. For this research only hit- driven data was collected.

The averaged root mean square (RMS) energy of the signal is considered to have good sensitivity to the cutting speed, workpiece, hardness, lubrication, depth of cut and feed rate in Grinding. RMS is the measurement of the continuously varying and ‘‘averaged’’ amplitude of the AE signal. It is defined as the rectified, time averaged AE signal, measured on a linear scale and reported in volts. The root mean square (RMS) value of the AE signal, i.e. AERMS, is defined by,

………………………….[ D.J. Stephenson, Aug 2006]

where V(t) is the AE raw signal and T is the integration period. The AERMS value carries information of the AE raw signal power during each interval of time. AERMS has gained major importance, because the energy rate, dE/dt, of the acoustic emission signal is directly proportional to AERMS, can be expressed as:

푑퐸 2 훼 퐴퐸 ……………………….[ B.E Klamecki 1990] 푑푡 푅푀푆

Figure 2.1 Parameters used to characterize emission events [R. K. Miller, 1987]

The amplitude is the peak voltage in the signal waveform from an emission event. Amplitude is measured in a logarithmic (dB). In other words,

Amplitude is the maximum (positive or negative) AE signal excursion during an

AE hit. The amplitude is expressed in db using the relationship: [A.W. Warren,

2007]

…….….. (Eq. 1)

Count (ring-down count) is the number of times the acoustic emission amplitude exceeds the preset threshold. Counts are an AE hit feature that measures the number of signal excursions over the AE threshold. Duration is the length of time between the first count and the last one, which is normally measured in microseconds. The rise time is the length of time between the initial of event detection and the peak amplitude.

Average frequency is a calculated feature, reported in kHz, which determines an average frequency over the entire AE hit. It is derived from other collected AE features, namely AE counts and duration. It is a real time calculation determined as AE counts/duration. [Schofield B.H, 1975]

Figure 2.2 AE signals and power spectrum graphs under grinding conditions

displayed on screen (previous experimental observation)

AE pulses depends only on characteristics of measuring devices. Both

Discrete and continuous AE components may occur in all the above process.

Discrete AE is usually used to predict the damage since high amplitude signals are easy to detect. It is also used to monitor technological processes in which cracks may appear and to study and monitor corrosion cracking strength heat resistance fatigue damage and friction wear.

Rangwala and Dornfeld experimentally observed how the AE spectrum changes with metal cutting process parameters such as speed, feed rate, and

20 chip-tool contact length. The formulation of the model is based on the simplified

Ernst and Merchant model of orthogonal machining and builds a dependency of

AE energy on material properties such as flow stress, volume of material undergoing deformation and the strain rate. The relationship between the emission signal and the cutting parameters based on the Ernst and Merchant model can be written as:

……………………….. [David Dornfeld, 2008]

Acoustic Emission Setup

a. Sensors:

Widely used sensors in acoustic emission monitoring are piezoelectric type transducers in which element are usually a special ceramic such as lead zirconate titanate (PZT). This piezoelectric sensor normally consists of a piezoelectric element with electrodes on two sides. One electrode is connected to ground and the other is connected to a signal lead wire. A backing material behind the sensing element serves as the mechanical damping. A wear plate is normally bonded to the sensing element to avoid damage and wear. This sensor also has a case which protects the components inside and serves as an electromagnetic shield. Fig. 5 and 6 provides detail schematic diagram of Sensors

Figure 2.3: Block diagram of Acoustic Emission system

Figure 2.4: Schematic Cut section diagram of piezoelectric sensor [Physical Acoustic Corp. (PAC)]

Figure 2.5: Photo of actual sensors available in different sizes [Physical Acoustic Corp. (PAC)]

Few other observations related to AE sensors [Amir Rabani, (2012)]:

- AE sensors are usually sensitive in only one direction, normal to their

coupling face or base.

- The magnitude of the displacement will be altered due to presence of the

AE sensor.

- For larger sizes of test blocks the mechanical impedance mostly depends

on the material and not on the size of the block

- The critical point in using the AE sensors is to find the best location for the

sensor; this can usually be bound by field tests searching empirically for

the clearest and most relevant AE signal.

b. Calibration:

Calibration of AE transducers attached to the surface of the structure is one of the fundamental problems. Article by Jay Paul Daniel, describes a new method for the absolute – emission transducers for practical use. One benefit of this calibration is that absolute sensitivity is determined with ease and repeatability in an acoustical environment similar to that of actual structures. A relation is developed between transducer sensitivity and diameter and thickness of piezoelectric – ceramic disks. It’s shown that frequency where sensitivity

24 attains a peak decreases both with the diameter and the thickness. Few other observations related to AE sensors AE sensors are usually sensitive in only one direction, normal to their coupling face or base. The magnitude of the displacement will be altered due to presence of the AE sensor. For larger sizes of test blocks the mechanical impedance mostly depends on the material and not on the size of the block [F.J Moskal, 1973]

c. Sensor Coupling:

A thin couplant layer is normally used between the sensor and the test object to facilitate the transmission of acoustic emission. It is found that the sensor produces a very weak signal when a sensor has simply been placed on the surface of the material containing the acoustic wave. A much larger signal is obtained if a thin layer of a fluid is placed between the sensor and the surface.

For a shear wave with a variable strain component parallel to the surfaces, again very little strain will be transferred between the surfaces because of the few points in actual contact. The use of some type of couplant is essential for the detection of low level acoustic signals. On a microscopic scale the surfaces of the sensor and the material are quite rough, only a few spots actually touch when they are in contact. If the microscopic gaps are filled with a fluid, the pressure will be uniformly transferred between the surfaces. A high viscosity liquid will

25 help transmit the parallel strain between surfaces. The purpose of a couplant is to insure good contact between two surfaces on a microscopic level.

The most practical rule is to use a thin layer of any viscous fluid which wets both surfaces. The sensor should be held against the surface with some pressure furnished by magnets, springs, tape, rubber bands, etc. Few commonly used couplants are listed along with the temperature range where they can be used; Dow Corning V-9 resin (-40 C to 100 C), High vacuum stop cock grease (-40

C to 200 C), Ultrasonic Couplants (Room temperature.). Other commonly used couplants are grease, water-soluble glycols, resin, adhesive, and glue.

d. Silicon rubber sealant:

In addition to coupling, waterproofing is necessary in monitoring of the grinding process to prevent the sensor from the wet grinding environment. The most commonly used materials for waterproof are silicone adhesive and silicone sealant. Piezoelectric sensors are attached to the workpiece with the help of adhesive which will sustain high working temperature, and resistance to noise formed due to flow of coolant in the machining/ grinding process. Silicon rubber

26 is one of such material which is usually used for doing this job. Silicone rubber offers good resistance to extreme temperatures, being able to operate normally from −55 °C to +300 °C.

2.4 Application of Acoustic Emission monitoring to

grinding process:

Overview

Previously Acoustic emission has been used in process monitoring of manufacturing process. Viz. Single point cutting turning in which continuous AE signals are formed in shear zone at chip tool interface and burst AE signals are formed when chip breaks. Recently, use of AE is growing for multipoint cutting process, like grinding. Grinding is a typical multi-point cutting process that is also a process of chip formation, but in a much smaller scale.

Although the grinding wheel consists of abrasive grains which have irregular geometries and spaced randomly along the periphery of a wheel, similarities exist between the generation of acoustic emission signals between single-point turning process and multi-point grinding process. Use of Acoustic emission monitoring system has been proved helpful in case of conventional

27 grinding process and many studies have been recorded. Research over the past several years has established the effectiveness of AE based sensing methodologies for machine tool condition monitoring and process analysis. AE has been used in grinding research for detecting malfunctions and dressing cycle quantities such as: grinding wheel loading, chatter, grinding bum, grinding wheel sharpness and dressing monitoring [Lanteigne, 1977]

Fig 7: Sources of AE in Grinding at the grinding wheel-workpiece contact area [W. Hundt, 1994]

As seen in Fig 7, the sources of acoustic emission in grinding are combination of elastic impact, friction, indentation cracks, bond fracture, chip fracture, grain fracture, and grit removal at the tool/chip interface. Grinding burn is an overheating phenomenon on the workpiece surface. This transient thermal stress will cause microcracks, which create high acoustic emission.

Theoretically, each cutting grit on wheel generates a burst type AE signal when it cuts through the workpiece. Though, as numerous grits cut through the workpiece in such a way that the interval of two consecutive cuts (which are not necessarily in the same place) is much shorter than the decay time of each burst signal, then continuous type AE is formed.

The AE technique involves the detection and conversion of high frequency elastic waves generating from the source to electrical signals. This is done using a high frequency piezoelectric transducer which can be mounted directly to a workpiece. The output of the AE sensor is usually amplified and passed through filters to remove extraneous noise before being sent to a data processing unit. A major advantage of AE technique is that it allows processes to be monitored in real time and can be used to give advanced warning of material failure [E. Emel,

E. Kannatey-Asibu, 1991].

In year 2004, J. F. G. Oliveirab and C. M. Valente proposed a new method for Fast Grinding Process Control with AE Modulated Power Signals. The electric current at the main motor has being used to measure the grinding power

[]. However its response is slow. The AE signal presents a fast response but its level can be highly influenced by external factors. They proposed a monitoring approach based on a new parameter called Fast Abrasive Power (FAP). Power and acoustic emission (AE) are among the most commonly used signals for monitoring of grinding processes. [D.E. Leea, 2006]

The potential of using acoustic emission (AE) as a source of information for grinding control has been investigated extensively since 1989. The AE generation is evaluated for detecting phenomena besides the wheel and workpiece first contact. Grinding research uses AE for detection of malfunctions, grinding wheel loading, chatter, grinding bum, grinding wheel sharpness and dressing monitoring. [F. Gomes de Oliveira, David A. Downfield, Bernhard

Winter, 1994]

AE Vs. Material removal rate (Literature review)

Grinding is a machining process in which a wheel removes material from the surface of a less-resistant body, through relative movement and application of force. The material removed, in form of minute chips, slides on the face of the grain, known as tool rake face, submitting it to high normal and shear stresses and, moreover, to a high coefficient of friction during chip formation [D.J.

Stephenson, X. Sun, C. Zervos, Aug 2006].

The material removal rate is the grinding parameter which is result of the

Depth of cut (DOC), feed rate and cutting speed (wheel speed), selected to operate the grinding wheel. To achieve better machinability from grinding, it is recommended to choose MRR involving more ductile material removal mode and a more stable interaction between the material and wheel grains, resulting in less specific grinding energy generated. This is represented by low amplitude and fluctuation of AE signal and smaller grinding forces. This will ensure a better surface and subsurface integrity of the workpiece. [Qingliang Zhaoa, et al

Nov 2007]

It’s a generally accepted rule that AE energy generated by fracture dominated process is larger than AE energy generated by the plastic flow dominated process, leading to the expectation that brittle regime machining would produce more AE energy than ductile regime machining. Though, in research work undertaken by F. Gomes de Oliveira Et El. it’s demonstrated that this generalization is incorrect, and it’s shown that for given volume of material removed from a given material, AE energy for fracture dominated material is lower than that of the plastic dominated material. Important observation made was that AE activity is not only generated due to chip formation and fracture but also due to plowing and rubbing though there is no material removal. [A.W.

Warren and Y. B. Guo, (Aug2007)]

As an exceptional phenomenon, it is also observed that when the MRR increased, by increasing the cutting speed, the AE amplitude decreases and the surface roughness increase. This phenomenon can be explained as follows: as the grinding time advances the number of cutting edges on the wheel surface decreases due to the minute chipping of the grains. Hence, the signal level of the

AE decreases and the surface roughness increases. But generally, in order to obtain a better surface and subsurface integrity of the material, ductile material removal mode must take place during the machining process instead of brittle mode [Ichiro Inasaki, (1998)]

AE vs. Forces (Literature review)

Because all solid materials possess elasticity, they become strained or compressed under external forces and spring back when released. The higher the force, higher is the elastic deformation, and thus higher is the elastic energy. If the elastic limit is exceeded a fracture occurs immediately in case of a brittle material, or for ductile materials after a certain plastic deformation. Also If the internal stress in the materials or structures is suddenly redistributed such as crack initiation and growth, crack opening and closure, deformation, dislocation movement, void formation, interfacial failure, corrosion, tiny cracks in materials all structures will emit very intense AE signals .These waves propagate through

32 the material and eventually reach the surface, producing small temporary surface displacements. This rapid release of elastic energy is an AE event.

The cutting force depends on the area of the chip (i.e. feed and depth of cut), the tool path (i.e. width of cut), the material and cutting tool properties and some experimentally or empirically determined constants [Young Moon Lee,

Lee, 2006].

In the grinding process there are two components of the cutting force,

Longitudinal and Normal. Force is not an input factor but it is used as an indicator of the dynamic characteristics of the workpiece-grinding wheel- machine system. [P.G. Benardos, G.C. Vosniakos, 2002]

AE vs. Temperature (Literature review)

In every machining process, most of the mechanical energy used to form the chip becomes heat, which generates high temperatures in the cutting region.

High cutting temperature adversely affects dimensional and form accuracy. In industry, such high cutting temperature and its detrimental effects are generally reduced by proper selection of process parameters, proper selection application of cutting fluid, and using heat and wear resistance cutting tool materials like

33 carbides, coated carbides and high-performance ceramics. [L. De Chiffre, W.

Belluco, 2000]

In grinding the increase in Acoustic signals is result of increase in the frictional energy, which is usually a result of material removal rate. Increase in the frictional energy also increases the surface temperature of the workpiece.

Hence if the temperature increase is due to increase in the friction on workpiece surface, Acoustic Emission also increases.

In the past researchers in this field have observed that if depth of cut, wheel speed and feed rate are constant, the temperature of surface is controlled high enough to soften the surface being ground, the AE decreases. Because increased temperature facilitates elastic deformation, material at the surface becomes softer, can be removed easily and generates lesser elastic stress waves.

[I. Marinescu, et al (1994)]

Conventionally applied coolants, even with extreme pressure additives, fail to provide desirable control of cutting temperature, as they cannot penetrate into the chip–tool interface predominantly due to plastic contact between the tool and chip, especially at high cutting speed. High-pressure jet of conventional coolant has been reported to provide some reduction in cutting temperature [A.C

Lucia, G Redondi, (1976)].

Grinding burn occurs from the increased grinding temperature when abrasive grits come into contact with work-piece material. This elevated temperature, however, cannot dissipate quickly due to too much heat generated when material is being removed or there is not enough coolant present. There are other factors such as a worn grinding wheel due to loading. This burn has to be monitored in such a manner as to enable the safe detection of burn or, better, just before it occurs. Some of the early monitoring systems used AE root mean square (RMS) detection levels to determine the different types of phenomena

AE vs. Surface roughness (Literature review)

Measure of the quality of a product of grinding and a factor that greatly influences manufacturing cost is surface roughness. Many times the part must be machined more than once until an acceptable value is obtained. There is a need for a tool that will allow the evaluation of the surface roughness value before the machining of the part. It could be used for the determination of the optimum cutting conditions required for a specific surface roughness to be achieved. This technology can also be easily used in the production-floor environment contributing to the minimization of required time and cost. [T. S. Reddy, and C.

E. Reddy, 2010]

The depth of cut and feed rate influences surface quality in an indirect way. Increasing the depth of cut increases the cutting resistance and the amplitude of vibrations. Therefore, it is expected that surface quality will deteriorate. Depth of cut and feed rate also increases the Acoustic Emission and cutting temperature. Hence there is a correlation existing between the Acoustic

Emission emitted and the surface roughness produced.

In determining process performance and finished surface quality the chemical composition and mechanical properties of the work material, the tool and the cutting fluid are also of vital importance. To be able to predict the surface roughness prior to machining has finished, has attracted a lot of attention and thus has being the main goal of many research efforts. [C. Beggan, M. Woulfe, P.

Young and G. Byrne, (1999)]

In earlier studies, Acoustic emission data during machining have been taken into account along with a self-organizing network for real-time estimation of surface roughness [C.H. Palmer, R.E. Green, 1977]. In machining the dynamic characteristics and especially chattering was considered to be the most important factor for poor surface quality and reduced tool life [D. Dornfeld, He Gao Cai,

(1984)]. The change in the AE amplitude level, standard deviation of the AE amplitude level, and cumulated stock removal are used for estimating the surface roughness. These parameters are fed into the neural network to estimate roughness.

It is very difficult to calculate roughness value through analytical formulae especially because, the mechanism behind the formation of surface roughness is very complicated and process dependent. Various theoretical models that have been proposed earlier are not accurate enough and apply only to a limited range of processes and cutting conditions or must be used in conjunction with obscure diagrams and statistical tables. Consequently, The

Acoustic emission signals can be used in the prediction of the Surface roughness values.

In a research work presented by K.H.W. Seah, X. Li and K.S. Lee [1995], it is suggested that both static and dynamic variables of the grinding process should be included in the surface roughness model. The static variables include cutting conditions (cutting speed, feed, depth of cut), the shape of the cutting tool edge and the cutter insert run-out error, whilst the dynamic variables refer to the dynamic behavior of the cutting tool-workpiece system through the measurement of cutting forces.

Characterization of the grinding process by acoustic Emission was studied by Egon Susic and Igor Grabec.[47] It’s proposed that the properties of a ground surface can be estimated on-line during manufacturing based on the analysis of acoustic signals emitted by the grinding process. This possibility is demonstrated using an experimental system comprising an external grinding machine, a data acquisition unit and an artificial neural network. The properties of a ground

37 surface can be estimated on-line during manufacturing based on the analysis of acoustic signals emitted by the grinding process. This possibility is demonstrated using an experimental system comprising an external grinding machine, a data acquisition unit and an artificial neural network. With respect to the estimation error, three characteristic periods of the process were observed corresponding to grinding with a newly dressed, slightly worn, and worn out wheel. The experimental study involved a fractional factorial experimental design with influence of varying parameters on AE generation during machining.

Formulation to relate the AE output signal with the direct information of shearing, tool wear, chip segmentation and tool fracture during machining process was created.

AE vs. Fluids (Literature review)

As also stated in earlier sections, friction between surfaces can be monitored using acoustic emission. It has also been shown that the power of the

AE signal increases with friction between unlubricated surfaces as compared to lubricated surfaces [Hajimi Hatano, Eiji Mori (1975)]. It is expected that differences in detected acoustic emission can be measured when different lubricants are used and this information can be used to classify different lubricants and their performance.

The quality of a surface machined with the presence of cutting fluid is expected to be better than that obtained from dry cutting. The use of cutting fluid generally advantageous in regard to surface roughness because it affects the cutting process in three different ways. Firstly, it abstracts the heat that is generated during grinding by cooling mainly the grinding wheel and the work surface. It is able to reduce the friction. Lastly, the washing action of the cutting fluid consists of removing chip fragments and wear particles. [Jae-Seob Kwak, Ji-

Bok, 2001]

On the contrary to importance of using good quality cutting fluid in the machining process, the selection of the fluid is often decided by the purchasing management, these decisions further affect the efficiency of machining departments [G. Lorenz, (1985)].

It is well established fact that relevant evaluation of a cutting fluid can be expected through the use of a real machining operation. It is also a judgmental fact that, the efficiency of cutting fluids is highly influenced by type of operation, method of application of cutting fluid, workpiece material, surrounding environment as well as on operating conditions of the machine. On today’s date, as there are many manufacturers of cutting fluids and different varieties of cutting fluids found in the market, more than few cutting fluids are claimed to have same purpose and quality. Development and introduction of new cutting fluids in market involves many steps of time consuming lab tests and machining

39 tests. This makes evaluation of cutting fluid another big field of research. Ever growing environment concerns and government norms on using additives adds up to make this research field vast.

2.5 Literature Review of use of AE vs. Grinding

Mechanism

The very first use of Acoustic Emission in grinding was to determine the first contact between the grinding wheel and the workpiece. Then study by

Oliveira et al in 1994 showed the approach of using AE to measure the grinding wheel geometric characteristics [F. Gomes de Oliveira, David A. Dornfeld,

Bernhard Winter, 1994]. They also proposed a system for measuring the grinding wheel position. The use of acoustic emission (AE) as a source of information for grinding control has been investigated extensively in the last fifteen years. The

AE generation is evaluated regarding other phenomena besides the wheel and workpiece first contact. AE has been used in grinding research for detecting malfunctions and dressing cycle quantities such as: grinding wheel loading, chatter, grinding bum, grinding wheel sharpness, and dressing monitoring.

AE is found to be superior for detection of the wheel / work contact at start of cycle, using RMS signal processing. Most AE sensors can distort the signal from the process but deconvoulution can restore the signal. Aim of the research undertaken by I. Marinescu Et el was to find the relation between AErms, force, stock removal rate, specific energy and surface finish. [I. Marinescu, J.

Webster, R. Bennett, 1994]

Acoustic emission signals with an application in grinding wheel condition monitoring for Feature extraction and selection was done by, T. Warren Liao [T.

Warren Liao, 2010]. Two feature extraction methods, three feature selection methods, and five classifiers were employed in the study. Specifically, grinding wheel condition was monitored with acoustic emission signals. AE signals were extracted both by discrete wavelet decomposition and autoregressive modeling.

Hwang et al. (2000) reported that the amplitude of the AE signal, collected in high-speed grinding of silicon nitride using an electroplated single-layered diamond wheel, monotonically increases with wheel wear. [T. Warren Liao,

2010]

As a different approach to most acoustic emission (AE) research in grinding, study by J. Webster et El, focused on analyzing the raw AE signal instead of root mean-square signal (RMS) [J. Webster, W. P. Dong, R. (1996].This paper has discussed some important characteristics of the AE raw signal. It has shown that a grinding process possesses two types of wheel/workpiece a contact

41 process which can be detected by the AE raw, signal, that is the 'grit contact' and the 'wheel contact'. The former generates a burst type AE in the initial spark-in as well as sparkout stages. The latter generates a continuous type AE.

A new method of grinding burn identification with highly sensitive acoustic emission (AE) techniques was proposed by Qiang Liu et El [Qiang Liu,

Xun Chen, Nabil Gindy, Jun2005]. The wavelet packet transform is used to extract features from AE signals and fuzzy pattern recognition is employed for optimizing features and identifying the grinding status. Experimental results show that the accuracy of grinding burn recognition is satisfactory. Grinding burn damages materials and degrades properties, by causing tensile residual stresses or microfractures in the workpiece surface. By means of fuzzy pattern recognition, grinding burn can be identified based on distance criteria.

Experiment results demonstrate a successful application in grinding burn monitoring.

Chapter 3.

Experimentation

All the tests are carried out on the 152.4mm (6in) * 25.4mm (1in) * 38.1mm

(1.5in) AISI 52100 steel block. This steel is high-carbon chromium alloy steel, which is used in a variety of mechanical applications. In the annealed condition this steel is comparatively easy to machine, yet very high hardness and abrasion resistance was developed by heat treatment process. For experiment purposes these steel blocks were all hardened to same hardness of 62 RC.

The grinding of AISI 52100 steel was performed on Thompson Creep Feed

Grinder equipped with Fanuc System 3M-Model C Controller. Grinding was performed using an Al2O3 wheel which was dressed prior to machining and ample coolant was used to prevent excessive heat at the machined surface. The

Wheel spindle is also equipped with a Dynamic balance System, which provides a real-time dynamic balancing. Balancer eliminates the imbalance of the grinding wheel and to minimize the grinding wheel vibration as well as maintain an optimum grinding process. The grinding fluid employed for grinding process is

TRIM C270, manufactured by Master Chemical Corporation. Table 3.1, and Fig. 8 provides the outline of the experimental set up.

Table 3.1 -the specifics of the grinding conditions

Work Material AISI 52100 Steel (62 HRC) Grinding Wheel Norton 32A, Al2O3 wheel Wheel Speed (m/s) 15.18m/s, 22.72m/s. 30.23m/s Feed Rate (m/s) 0.064m/s, 0.085m/s, 0.106m/s Depth of Cut (μm) 12.7μm, 25.4μm, 38.1μm Cutting Fluids Xt585, E906, SC520 C270

Figure 3.1 - experiment set-up schematic

The test workpiece of steel AISI 52100 was secured in a fixture and positioned with four clamps that also maintain rigidity. Kistler 9257B

44 dynamometer is used in the experiment to record the normal and tangential forces. An acoustic emission sensor is glued to the metal plate using silicon adhesive. Sensor plate is mounted to the fixture after applying a thin layer of petroleum jelly at the contact surface; this avoids any loss of signal. PocketSurf portable surface roughness gage is used to measure the workpiece surface roughness. Figure 9 and 10 show the set-up of the experiment.

Table 3.1 refers to the configuration of AE system for experiment purpose.

Acoustic emission signals were acquired using piezoelectric transducer sensors, with a broad-band of 100–1000 kHz from Physical Acoustics Corp. The sensor was attached to the surface of the workpiece holder using petroleum jelly. The acoustic emission signals are converted into electrical signals by the sensor, amplified to usable voltage levels by the preamplifiers and transferred to the

AEDSP-32/16 card, which has 16-bit resolution for dada recording. The preamplifier (1220 A) provides a gain of 100 (40 dB) and uses a bandwidth filter with 100–1200 kHz bandwidth to eliminate the mechanical and acoustical background noise that prevails at low frequencies. A frequency of 2 Mega sample rate per second was selected for signal acquisition. The AE software

AEWIN, provided by Physical Acoustic Corp. was used to acquire AE raw signals with short operating duration for AERMS and Fast Fourier

Transformation (FFT) analysis.

Table 3.2 - Configuration parameters for AE monitoring system

Signal Sampling Rate 2 MHz Threshold 30 dB Preamplifier Gain 40 dB Filtering (kHz) Band pass (100-1200) Duration of AE Recording 1-2 sec System Used Physical acoustic Corporation Sensors Piezoelectric

The AE sources in a grinding operation are mainly caused by the interaction of the wheel and the workpiece. In Fig. 1, HF denotes the sensor with high frequency response and LF means low frequency response. There are different sources of noise during the grinding process. The noise from grinding wheel and grinding fluid were the most significant noises. Background noise consists only of frequency components below 100 kHz, so noise has only a small influence on the measurement system when its responding range is tuned to higher than 100 kHz. The noise at frequencies above 2.0 MHz could be attributed to the machine electrical system [B.E Klamecki, J.Hanchi, 1990]. By using a bandpass filter with cut-off frequency of 100–1200 kHz most of the noise generated by machine vibration and wheel rotation were easily eliminated from high frequency AE detection. Because the AE signal caused by grinding burn is relatively weak, the threshold, the prime variable to control AE system sensitivity, is usually in the range 10–99 dB. It was judged that the threshold

46 should be 30 dB to obtain higher sensitivity. Too low a threshold may bring background noise into the system. [P. Sutowski, S. Plichta; 2006]

For the temperature measurement purpose in this research two thermocouples are embedded closely under the workpiece surface instead of one with different distances to the surface. These two thermocouples are used to get the temperature reading simultaneously during grinding process. To measure proper temperature readings, the distances of the two thermocouples form the workpiece surface is important and also has to be same. To maintain the same distance several holes are drilled underneath the workpiece surface. Every two holes have exact distance of 50.8μm (0.002in) between them. The two thermocouples are inserted in every two of these adjacent holes, to maintained same distance. Before performing experiments, the distance of every pairs of holes to the surface are set.

Figure 3.2 - Photo of the complete set-up for the experiments

Figure 3.3 - Photo of the workpiece and fixture set-up for experiment

Table 3.3 - physical properties of four fluids used for the study

The Cutting fluids used for the grinding in these experiments are provided by Master chemical Corp. All these cutting fluids are popular in market and represent different categories of cutting fluids Viz. Semisynthetic, Emulsion, synthetic and micro-emulsion (See table 4).

Chapter 4.

Results and analysis

As mentioned in the above sections, four different cutting fluids were selected. Similar sets of grinding passes were taken with varying the depth of cut, Wheel speed and Feed rate. The response variables measured in these experiments were Acoustic Emission, Surface temperature, Normal Forces and

Surface roughness. Summary of grinding variables choose and responses collected, there values and units, are provided for quick reference in the Table 4.

Mixed level Full Factorial design of Experiment was created and all the Data was collected with changing the grinding parameters randomly. Fluid factor at four levels, and remaining factors Viz. Doc, cutting speed and feed rate at three levels required to take hundred and eight (4*3*3*3 = 108) observation (grinding passes).

More over four replications were taken for every variable set for better quality of data. Average values of all readings done of all replications done are displayed in the Table 5.

Acoustic emission parameters like amplitude, absolute energy and RMS are proportional to the fatigue failure occurring at the surface of the workpiece.

Values of these parameters vary when a grinding pass progresses. The first stage is when the wheel makes contact (break-in) with the surface; signals are higher, because initially the elastic and plastic deformation is higher. Later on, as the grinding wheel progresses the signals become more stable and less fluctuating.

For present experiments all the parameters are measured at the second stage, when the grinding wheel has already made a contact and completed the pass.

Table 4.1. Design of Experiment Process variables Response Variables Factor Level 1 2 3 4 *mean values of 3 replications (passes) η Fluid viscosity 1.127 1.137 1.304 1.312 Ra average surface mPa S roughness µm d depth of cut in 12.7 25.4 38.1 T Temperature in ℉ μm - Vc Wheel speed 15.18 22.72 30.23 F Normal force in in /min - Newton (N) f feed rate in 0.064 0.085 0.106 AE Acoustic mm/rev - Emission in mV

Fluid Viscocity Depth Feed Wheel AE Ra Temperature Forces (n) of Cut Rate Speed RMS (T) (F) (μm) (m/s) (m/s) (a) (f) (v) C270 1.1270 12.7 0.064 15.18 47.5 0.1712 30.04 66 C270 1.1270 12.7 0.064 22.72 48 0.1679 32.94 64.9 C270 1.1270 12.7 0.064 30.26 52 0.1603 34.55 63.8 C270 1.1270 12.7 0.085 15.18 53.5 0.1795 33.78 75 C270 1.1270 12.7 0.085 22.72 54.1 0.1709 34.62 71.5 C270 1.1270 12.7 0.085 30.26 56 0.1676 35.1 69 C270 1.1270 12.7 0.106 15.18 55 0.1909 36.37 75.2 C270 1.1270 12.7 0.106 22.72 57 0.1864 37.74 73.5 C270 1.1270 12.7 0.106 30.26 59 0.167 37.816 70.8 C270 1.1270 25.4 0.064 15.18 57 0.2098 36.9 130.8 C270 1.1270 25.4 0.064 22.72 59.9 0.2068 37.2 126.2 C270 1.1270 25.4 0.064 30.26 60 0.1954 37.4 123.2 C270 1.1270 25.4 0.085 15.18 61.5 0.2287 38.383 133.9 C270 1.1270 25.4 0.085 22.72 63 0.2282 38.5 128 C270 1.1270 25.4 0.085 30.26 64.9 0.2132 38.72 124.5 C270 1.1270 25.4 0.106 15.18 65.1 0.2513 39.53 134.5 C270 1.1270 25.4 0.106 22.72 67.5 0.248 39.28 130 C270 1.1270 25.4 0.106 30.26 69 0.233 39.244 126 C270 1.1270 38.1 0.064 15.18 68 0.2815 39.7 176 C270 1.1270 38.1 0.064 22.72 73.9 0.2711 39.59 172 C270 1.1270 38.1 0.064 30.26 75 0.2649 39.4 168 C270 1.1270 38.1 0.085 15.18 69.5 0.3009 39.85 177.5 C270 1.1270 38.1 0.085 22.72 71.8 0.2978 40 174 C270 1.1270 38.1 0.085 30.26 78.1 0.2812 39.9 170.7 C270 1.1270 38.1 0.106 15.18 83.6 0.3271 40.1 179.9 C270 1.1270 38.1 0.106 22.72 85.3 0.3261 40.5 176.1

C270 1.1270 38.1 0.106 30.26 88.4 0.3158 40.444 171.5 SC520 1.1370 12.7 0.064 15.18 34 0.2384 32.64 60 SC520 1.1370 12.7 0.064 22.72 36.5 0.2354 34.44 58.2 SC520 1.1370 12.7 0.064 30.26 39 0.2224 36.15 55.1 SC520 1.1370 12.7 0.085 15.18 37 0.2458 36.48 63.5 SC520 1.1370 12.7 0.085 22.72 38.9 0.2414 37.52 62 SC520 1.1370 12.7 0.085 30.26 39.5 0.2318 38.2 57 SC520 1.1370 12.7 0.106 15.18 42 0.2572 38.57 64.5 SC520 1.1370 12.7 0.106 22.72 45 0.2506 39.24 63 SC520 1.1370 12.7 0.106 30.26 46 0.2354 39.416 58 SC520 1.1370 25.4 0.064 15.18 47.9 0.274 39.6 117.1 SC520 1.1370 25.4 0.064 22.72 48 0.2752 40.1 114.6 SC520 1.1370 25.4 0.064 30.26 49.5 0.2638 40.5 109 SC520 1.1370 25.4 0.085 15.18 49.9 0.2992 40.53 119.9 SC520 1.1370 25.4 0.085 22.72 50 0.2966 40.78 116.3 SC520 1.1370 25.4 0.085 30.26 51.5 0.2816 40.844 112.9 SC520 1.1370 25.4 0.106 15.18 52 0.3176 41.083 121.5 SC520 1.1370 25.4 0.106 22.72 53.5 0.3164 41.4 119 SC520 1.1370 25.4 0.106 30.26 55.1 0.3014 41.82 114 SC520 1.1370 38.1 0.064 15.18 55.5 0.3436 42 153.8 SC520 1.1370 38.1 0.064 22.72 56 0.3374 42 150.8 SC520 1.1370 38.1 0.064 30.26 57.5 0.3312 42.044 146.2 SC520 1.1370 38.1 0.085 15.18 59.1 0.363 42.4 155.2 SC520 1.1370 38.1 0.085 22.72 61.8 0.362 42.49 152 SC520 1.1370 38.1 0.085 30.26 64.1 0.3496 42.5 148.1 SC520 1.1370 38.1 0.106 15.18 68.6 0.3892 42.55 156.8 SC520 1.1370 38.1 0.106 22.72 72.3 0.3882 42.9 154.7 SC520 1.1370 38.1 0.106 30.26 80.4 0.3758 43 150 E906 1.3040 12.7 0.064 15.18 25 0.247 35.64 48.2 E906 1.3040 12.7 0.064 22.72 26.5 0.244 37.94 47.43 E906 1.3040 12.7 0.064 30.26 27 0.231 39.75 46.66 E906 1.3040 12.7 0.085 15.18 27.5 0.25 41.18 54.5 E906 1.3040 12.7 0.085 22.72 28 0.248 42.42 52.05 E906 1.3040 12.7 0.085 30.26 30 0.234 43.3 50.3 E906 1.3040 12.7 0.106 15.18 30 0.261 41.57 54.64 E906 1.3040 12.7 0.106 22.72 32.5 0.256 42.74 53.45 E906 1.3040 12.7 0.106 30.26 34 0.244 43.016 51.56 E906 1.3040 25.4 0.064 15.18 36 0.281 43.53 88.56 E906 1.3040 25.4 0.064 22.72 38.9 0.279 44.28 85.34 E906 1.3040 25.4 0.064 30.26 39.5 0.266 44.444 83.24 E906 1.3040 25.4 0.085 15.18 39.9 0.303 44.3 90.73

E906 1.3040 25.4 0.085 22.72 42.3 0.302 45 86.6 E906 1.3040 25.4 0.085 30.26 45 0.287 45.6 84.15 E906 1.3040 25.4 0.106 15.18 42 0.325 45.783 91.15 E906 1.3040 25.4 0.106 22.72 43.6 0.323 46.3 88 E906 1.3040 25.4 0.106 30.26 47 0.31 46.92 85.2 E906 1.3040 38.1 0.064 15.18 44.1 0.349 44.55 122.2 E906 1.3040 38.1 0.064 22.72 46.5 0.346 44.9 119.4 E906 1.3040 38.1 0.064 30.26 48 0.335 45 116.6 E906 1.3040 38.1 0.085 15.18 47.2 0.37 45 123.25 E906 1.3040 38.1 0.085 22.72 48.8 0.369 45.5 120.8 E906 1.3040 38.1 0.085 30.26 51.1 0.355 45.644 118.49 E906 1.3040 38.1 0.106 15.18 53.6 0.393 47.1 124.93 E906 1.3040 38.1 0.106 22.72 58.3 0.392 47.39 122.27 E906 1.3040 38.1 0.106 30.26 65.4 0.378 47.6 119.05 XT585 1.3120 12.7 0.064 15.18 18 0.3 38.24 42.908 XT585 1.3120 12.7 0.064 22.72 18.5 0.297 39.44 42.1842 XT585 1.3120 12.7 0.064 30.26 19 0.284 41.35 41.4604 XT585 1.3120 12.7 0.085 15.18 20.5 0.305 43.77 48.83 XT585 1.3120 12.7 0.085 22.72 22 0.302 44.24 46.527 XT585 1.3120 12.7 0.085 30.26 23 0.289 44.616 44.882 XT585 1.3120 12.7 0.106 15.18 24.5 0.312 43.88 48.9616 XT585 1.3120 12.7 0.106 22.72 26 0.309 45.32 47.843 XT585 1.3120 12.7 0.106 30.26 27.5 0.296 46.4 46.0664 XT585 1.3120 25.4 0.064 15.18 27.9 0.335 44.53 82.6464 XT585 1.3120 25.4 0.064 22.72 28.1 0.332 45.78 79.6196 XT585 1.3120 25.4 0.064 30.26 28.9 0.319 46.044 77.6456 XT585 1.3120 25.4 0.085 15.18 29 0.357 47 84.6862 XT585 1.3120 25.4 0.085 22.72 30 0.354 47.9 80.804 XT585 1.3120 25.4 0.085 30.26 31.1 0.341 48.7 78.501 XT585 1.3120 25.4 0.106 15.18 32.5 0.38 48.483 85.081 XT585 1.3120 25.4 0.106 22.72 33.1 0.377 49.2 82.12 XT585 1.3120 25.4 0.106 30.26 34.5 0.364 50.02 79.488 XT585 1.3120 38.1 0.064 15.18 35 0.402 46.9 116.068 XT585 1.3120 38.1 0.064 22.72 36.5 0.399 47 113.436 XT585 1.3120 38.1 0.064 30.26 37.5 0.386 47.244 110.804 XT585 1.3120 38.1 0.085 15.18 38.8 0.425 47.25 117.055 XT585 1.3120 38.1 0.085 22.72 41 0.422 47.8 114.752 XT585 1.3120 38.1 0.085 30.26 41.6 0.409 48.1 112.5806 XT585 1.3120 38.1 0.106 15.18 47.1 0.447 49.8 118.6342 XT585 1.3120 38.1 0.106 22.72 51.3 0.444 50.29 116.1338 XT585 1.3120 38.1 0.106 30.26 59.4 0.431 50.7 113.107

4.1 Effect of cutting fluid selection on AE, Normal forces,

Roughness and Temperature:

First and principal purpose of this research has been to find out whether or not there exists any effect of cutting fluid selection on the grinding process. To find out the correlation, if there exists, between the grinding response and the selection of different cutting fluids; comparisons were made between the grinding responses collected at similar grinding conditions while using different cutting fluids. For example, measurements recorded for Wheel speed, Depth of cut and Feed rate of 22.72 m/s, 0.085 m/s and 25.4 µm respectively were compared with four different cutting fluids. As an outcome of the observation made in these grinding experiments, the influence of selection of cutting fluid on the grinding response variables was noticeable, and is discussed in detail further.

Response graphs in figure 11 A, B, C and D shows the change in the readings of

Acoustic Emission, Surface roughness, Normal Forces and surface temperature when the cutting fluid is changed.

As seen in these graphs a similar trend of increase in measured values is seen for Acoustic emission and Normal forces (Fig. 11 A and B), when the fluid is changed, the order of increase is Xt585 - E906 - Sc520 - C270. Any previous research in this field that is correlating the fluid selection with the acoustic emission has not been found. Whereas the graph of surface temperature (Fig. 11

D) shows an opposite trend of decrease in the value of temperature reading

55 when the fluid is changed in the same sequence. The graph in Fig 11 B shows that surface roughness reading do not follow the same similar trend of increase or decrease in the surface quality. The surface quality increases for cutting fluids in the order of Xt585 – Sc520 – C270 – E906, which is slightly different than that of other three measured quantity.

Though, this phenomenon of increase in AE and force, as well as decrease in Surface roughness and grinding temperature with change in fluid can be explained based on theories constructed by few researchers in the past, those consider the composition of the cutting fluid. Table 6 summarizes the understanding made from the literature review related to the cutting fluid properties. Table 6 and 7 highlights the ranking of different fluid types, based on their effects on heat removal, lubricity, wheel life and surface roughness.

Figure 4.1 : AERMS Values of two differnt griding parameters and four replicates

Figure 4.2 A, B, C and D Shows variation in AE, Surface roughness, Normal Forces, and Temperature measurement with selection of four different cutting fluids 58

Table 4.2 - grinding fluid characteristics [R.A. Irani1, R.J. Bauer, A. Warkentin, 2005, J. O. Cookson, 1977 and M.A. E Baradie, 1996]

Synthetics Semisynthetic Emulsions straight oils

Heat removal 4 3 2 1

Lubricity 1 2 3 4 wheel life 1 2 3 4 surface roughness 1 2 3 4

Key: score of 1: the Worst and 4: the Best

Table 4.3 - Physical properties of four fluids used for the study

Acoustic emission and Normal force measured for the C270 is the highest, which can be attributed to the fact that this fluid is a Synthetic cutting fluid and has the lowest viscosity among all four cutting fluids. The reason for low 59 viscosity this category of synthetic fluids is that these fluids contain no oil. Also according to the manufactures description of this fluid, C270 creates very low foam during the grinding. Viscosity and foam are favorable qualities to provide some level of dampening effect between the grinding wheel and the workpeice surface. Unless there are special additives added to the synthetic fluids like C270, they will show very low lubricity. Low oil content, low foam generation and low viscosity can be accounted as, reasons for generation of higher friction and thereby higher normal forces and Acoustic Emission. Whereas, low viscosity allows easy flow of the cutting fluid between wheel and workpeice surface. Low viscosity and less foam formation also allows more cutting fluid to remain in direct contact with the wheel and workpeice surface. Both of these reasons are favorable for easy heat transfer, and thus justify the low grinding temperature measured. Synthetic fluids are water miscible fluids which have a claimed advantage of having some detergent properties. This fluids maintain high degree of cleanliness, helps in getting rid of the grinding debris separated from the wheel and workpeice in the form of chips. Cleansing action of C270 helps in very clean and uniform contact between grinding wheel and the workpeice surface and thereby providing very smoother surface roughness.

Compositions of different cutting fluids are shown in the table 12.

Table 4.4 : Composition/information on ingredients *

Xt585 E906 SC520 C270

Triethanolamine Absent 1-10 % 1-10 % 5-15 %

Monoethanolamine 1-10 % Absent 1-10 % 1-10 % petroleum oil 30-40 % 45-55 % 1-10 % Absent

* The exact chemical identities and percentages of the raw materials used in Fluids are trade secrets of Master Chemical Corp.

As seen in the Figure 11 grinding passes taken using the cutting fluid

SC520, measure the values of AE, Forces, Roughness and temperature between those of C270 and E906. This observation can be explained by stating the fact that

SC520 is a semisynthetic fluid, the oil level in these category of fluids is about

10% to 15%, which falls between oil content of synthetic and Emulsion.

Semisynthetic fluids tend to foam very easily, there viscosity is usually between that of synthetic and emulsifiers [R.A. Irani1, R.J. Bauer, A. Warkentin, 2005].

Though, the oil droplet size added to these fluids is still small, but the presence of oil in the fluid always improves the lubricating properties of the cutting fluid.

The AE and forces measured while using the cutting fluid E906 is considerably less than that C270 and SC520, whereas the temperature measured is higher. Trim E906 is market as a premium quality emulsion fluid. Percentage of oil content in these emulsion fluids is higher that the semisynthetic, hence the viscosity range is high. Emulsion fluids have greater foaming tendency, when

61 subjected to shear and turbulence [M.A. E Baradie, 1996]. Emulsions have very good lubricating properties. Moreover content extreme pressure additives in this fluid is higher than other two fluids. This factors can be counted as the reason for low shear forces at the workpiece surface and thus the AE and forces measured are low. But the presence of foam can inhibit the heat transfer as it limits the amount of fluid in contact with the wheel and workpiece, which can be noticed from higher surface temperature. E906 provided better surface quality because of the relatively good proportion of oil content.

TRIM® MicroSol® 585XT is claimed as special highest grade quality fluid which possesses the characteristics of both synthetic and emulsion, hence they are categorized as semisynthetic and micro emulsion fluid. The viscosity of this fluid is highest among all of these fluids and extreme pressure additives are added. Hence, the normal forces generated in grinding and also the AE generated is the lowest. More foam formation in this fluid and high viscosity makes the heat removal difficult, maintaining higher workpeice surface temperatures. With this fluid it’s also harder to flush away the debris, and broken wheel grains, which keeps the workpeice surfaces relatively less clean, and creates less fine surface roughness.

From above discussion, which distinguishes different cutting fluids, it can be understood that the performance and ranking of cutting fluids depends less on the categories in which it falls, but, more on the ratio of the additives and oil

62 content in them. From the results it can be emphasized on the fact that as the oil addition in the cutting fluid increases, process forces, grinding energy surface roughness and temperatures decrease while the wheel life increases.

4.2 Effect of grinding parameters on AE, Normal Forces,

Roughness and Temperature

Fig. 1a shows the effect of the increase in depth of cut on AERMS energy,

Temperature, Normal forces and Roughness. It is observed that as the depth of cut with which the material is removed from the workpiece surface is increased, the measured values of responses also increase. To observe this variation, all the readings are recorded at the constant feed rate of 0.064 m/s and constant wheel speed of 30 m/s. This increase in the responses with increase in depth of cut is observed because with an increase in depth of cut shear forces developed in the elastic zone of the material rise and increase the uncut chip thickness at the face of each grain. Increase in depth of cut gives sudden rise in MRR when compared with rise in wheel speed or feed rate. With higher depth of cut, there is also a restriction created for the easy flow of cutting fluid in the grinding zone, which eventually increases the chances of crack generation, grinding burn and wheel clogging, resulting in rough surface. All these reasons are sufficient to increase the AE energy within the surface and body of material, thus showing increase in

63 its value with increase in depth of cut. Internal stress generation due to increasing depth of cut increases, not only the AE signals, but also the force and temperature signals.

As seen in the graph, the surface roughness increases as the feed rate and

Depth of cut increases, which can be justified based on the phenomenon explained in above paragraph. Though the surface roughness actually improved when the cutting speed was increased. Wheel speeds faster than 25m/s gives less time for each abrasive grain to remain in contact with surface, this allows for less indentation of each grain, and smaller chip thickness or minute chipping happens. As a result, more grains come in contact with the surface. These conditions are favorable for better surface finish. Hence, grinding wheel speed higher than 25 m/s will generate higher AERMS but better surface finish.

AE energy is increased with an increase in wheel speed and feed rate because with higher wheel speed and feed rate the depth at which each abrasive grain on the wheel penetrate the surface of material is decreased, which decreases the AE energy emitted at one particular grain. But at the same instance, the total number of active grains increases with higher wheel speed. Also, the speed at which each grain abrasives (indents) the surface rises with rising wheel speed and feed rate. Thus, at higher wheel speed higher plastic deformation occurs than at lower speeds, which emits higher acoustic energy than elastic

64 deformation. This phenomenon is different than that of AE energy increase that is seen with increase in depth of cut, which is explained in further paragraphs.

Figure 4.3 A, B, C and D Shows variation in AE, Surface roughness, Normal Forces, and Temperature measurement with increase in Depth of cut

Figure 4.4 A, B, C and D Shows variation in AE, Surface roughness, Normal Forces, and Temperature measurement with increase in Cutting speed

Figure 4.5 A, B, C and D Shows variation in AE, Surface roughness, Normal Forces, and Temperature measurement with increase in Feed Rate

As the wheel speed (cutting speed) is increased, the MRR also increases,

AE energy rises, but the normal forces acting on surface decrease. This phenomenon is explained here. Increase in wheel speed increases the number of abrasive grains in contact with the surface. This in fact decreases the depth of cut with which each abrasive grain is penetrating the surface and decreases the uncut chip thickness for each grain. Each active grain removes less material, generating lower magnitude of forces. Formation of continuous chips results in relatively lower levels of AERMS; but in high speed grinding the chips formed are much minute than those at the lower wheel speeds. Also because there are more active grains, acoustic energy generated still keeps increasing.

From above graphs a similar trend of increase in the surface temperature reading is observed when either cutting speed, Depth of cut and feed rate is increased. Increase in temperature is direct result of increase in friction between the wheel and workspace surface. More friction is in fact the result of increase in material removal rate (MRR). All three grinding parameters Viz. Cutting speed, depth of cut and feed rate in fact increase the material removal rate and thus the surface temperature.

4.3 Regression Modeling

One of the primary objective of this research study was to establish relationship between different control variables of grinding process, with the measured parameters. Surface grinding passes on the test piece were controlled by four different independent grinding variables; Depth of cut, wheel speed,

Feed rate and type of Cutting fluid. The machining responses measured for all the grinding passes were Acoustic Emission (E), Surface roughness (Ra), Normal force (F) and Temperature °C (T). To determine the relationship between these independent and response variables following four individual statistical relationship models were proposed:

Surface Roughness 푅 = 퐶 . 휂훼. 푑훽. 푉 훾. 푓훿 Model: 푎 푐 (Eq.1)

훼 훽 훾 훿 Grinding Temperature 푇 = 퐶 . 휂 . 푑 . 푉푐 . 푓 Model: (Eq.2)

훼 훽 훾 훿 Grinding force Model: 퐹 = 퐶 . 휂 . 푑 . 푉푐 . 푓 (Eq.3)

훼 훽 훾 훿 Acoustic Emission Model: A퐸 = 퐶 . 휂 . 푑 . 푉푐 . 푓 (Eq.4)

Where, Ra is the surface roughness in µm, T is temperature in °C, F is normal force on the test piece surface in Newton (N), AE represents the Acoustic

Emission signal in millivolts (mV), Vc cutting speed in m/s; d depth of cut in μm, f feed rate in mm/s, η viscosity in cSt. Where C, α, β, γ and δ are the constants, whose values are to be through analyzing experiments results.

Above four equations are taking the form of Exponential regression equations. Unlike linear regression, which plots values along a straight line, exponential regression describes a curve by calculating the array of values needed to plot it. The equation that describes an exponential regression curve can be generalized in the form of:

m1 m2 mn y = b * x1 * x2 * … * xn

Where x is the independent variable, y is the dependent variable, m represents the exponential component of every independent variable of the line, and b represents the constant value. If there were only one independent variable, the above equation will be:

y = b *xm

Solution to the above equation will be to find the numerical value of the exponential component ‘m’. Efficient way to find out solution to the above equation will be to transform the equation in the linear regression model form.

Log y = log b + m log x

If we put log y = Y, log b = B and Log x = X and rewrite the equation. The new equation Y = mX + B algebraically describes a straight line for a set of data with one independent variable m represents the slope of the line, and B represents the y-intercept. If a line represents a number of independent variables in a multiple regression analysis to an expected result, the equation of the regression line takes the form Y=m1X1+m2X2+...+mnXn+b in which Y is the dependent variable, X1 through Xn are n independent variables, m1 through mn are the coefficients of each independent variable, and B is a constant.

To perform regression analysis the non- linier equations 1 to 4 above are converted into linear (additive) form, by performing logarithmic transformations as shown below:

log(푅푎) = log(퐶) + 훼. log(휂) + 훽. log(푑) Surface Roughness Model: + 훾. log(푉 ) + 푓. log (푓) 푐 (Eq.5)

Grinding Temperature log(푇) = log(퐶) + 훼. log(휂) + 훽. log(푑) Model: + 훾. log(푉푐) + 푓. log (푓) (Eq.6)

Grinding force Model: log(퐹) = log(퐶) + 훼. log(휂) + 훽. log(푑) + 훾. log(푉푐) + 푓. log (푓) (Eq.7)

Acoustic Emission Model: log(퐴퐸) = log(퐶) + 훼. log(휂) + 훽. log(푑) (Eq.8) + 훾. log(푉푐) + 푓. log (푓)

Solution to the above equations can be found out by using least square method least square method. Usually manually calculating the solution using the least square method is easier when there are only two independent variables involved, otherwise the manual calculation process gets very tedious and erroneous. To find the solutions to the above four equations mixed level four factorial design of experiment was performed. Different factors and there levels are summarized in the Table 4. Statistical analysis software MINITAB was used to perform the regression analysis and to establish prediction model.

Grinding Force Model

Regression Equation (Minitab Output)

Log(F.) = 1.26224 - 2.32658 Log(n) + 0.829688 Log(a) + 0.106423 Log(f) - 0.0863936 Log(vc) Coefficients

Term Coef SE Coef T P Constant 1.26224 0.0414819 30.4287 0.000 Log(n) -2.32658 0.0737956 -31.5273 0.000 Log(a) 0.82969 0.0117719 70.4804 0.000 Log(f) 0.10642 0.0258562 4.1160 0.000 Log(vc) -0.08639 0.0188705 -4.5782 0.000

Summary of Model

S = 0.0241001 R-Sq = 98.31% R-Sq(adj) = 98.25% PRESS = 0.0661522 R-Sq(pred) = 98.13%

Analysis of Variance

Source DF Seq SS Adj SS Adj MS F P Regression 4 3.48452 3.48452 0.87113 1499.84 0.0000000 Log(n) 1 0.57731 0.57731 0.57731 993.97 0.0000000 Log(a) 1 2.88519 2.88519 2.88519 4967.48 0.0000000 Log(f) 1 0.00984 0.00984 0.00984 16.94 0.0000779 Log(vc) 1 0.01217 0.01217 0.01217 20.96 0.0000132 Error 103 0.05982 0.05982 0.00058 Total 107 3.54434

Fits and Diagnostics for Unusual Observations

Obs Log(F.) Fit SE Fit Residual St Resid 30 1.74115 1.79333 0.0059372 -0.0521825 -2.23409 R R denotes an observation with a large standardized residual.

Normal Force Model: Using the regression analysis we get:

Log C = 1.26224, therefore C = 10^(1.26224) = 18.291

α= - 2.32658, β= + 0.829688, γ = - 0.106423, δ = - 0.0863936

18.291 . 푑0.829688. 푓0.106423 퐹 = 2.32658 0.0863936 휂(퐹푙푢푖푑) . 푉푐 ……………. (eq. 9)

Figure 4.6: Graph of Cutting force vs. DOC, Vc, Feed and viscosity (Fluid)

Residual Plots for Log(F.) Normal Probability Plot Versus Fits 99.9 2

99 l a

u 1

90 d i t s n e e 0 R c

50 r d e e P t -1

10 e l e

1 D -2 0.1 -4 -2 0 2 4 1.6 1.8 2.0 2.2 Deleted Residual Fitted Value

Histogram Versus Order 16 2 l a

u 1

12 d y i c s n e e

R 0 u 8 q d e e t r -1 e F l

4 e

D -2 0 -2.25 -1.50 -0.75 0.00 0.75 1.50 1 10 20 30 40 50 60 70 80 90 100 Deleted Residual Observation Order

Figure 4.7: Normal probability plot for normal force (F)

For the Normal force regression equation, the R Sq. value of 99.42% and S value of 0.011 show that the model very well fits the given data and there is very negligible error in the model. The p value for the feed rate coefficient is larger, which tells that this variable is not much significant to the equation, and can be ignored if much simple model is to be obtained.

Based on the residual plots, there is no evidence of non-normality, skewness, outliers, or unidentified variables. Because these plots don’t reveal obvious pattern, it is concluded that the models is adequate.

Acoustic Emission (AERMS) Model

Regression Equation (Minitab Output)

Log(AE) = 1.57914 - 3.47591 Log(n) + 0.46356 Log(a) + 0.403312 Log(f) + 0.124667 Log(vc)

Coefficients

Term Coef SE Coef T P Constant 1.57914 0.100014 15.7892 0.000 Log(n) -3.47591 0.177923 -19.5361 0.000 Log(a) 0.46356 0.028382 16.3327 0.000 Log(f) 0.40331 0.062340 6.4696 0.000 Log(vc) 0.12467 0.045497 2.7401 0.007

Summary of Model

S = 0.0581059 R-Sq = 87.14% R-Sq(adj) = 86.64% PRESS = 0.382832 R-Sq(pred) = 85.84%

Analysis of Variance

Source DF Seq SS Adj SS Adj MS F P Regression 4 2.35591 2.35591 0.58898 174.445 0.0000000 Log(n) 1 1.28859 1.28859 1.28859 381.658 0.0000000 Log(a) 1 0.90065 0.90065 0.90065 266.757 0.0000000 Log(f) 1 0.14132 0.14132 0.14132 41.855 0.0000000 Log(vc) 1 0.02535 0.02535 0.02535 7.508 0.0072411 Error 103 0.34776 0.34776 0.00338 Total 107 2.70367

Fits and Diagnostics for Unusual Observations

No unusual observations

Acoustic Emission Model: Using the regression analysis we get:

Log C = 1.57914, therefore C = 10^(1.57914) = 37.94

α= - 3.47591, β= + 0.46356, γ = 0.403312, δ = + 0.124667

0.46356 0.403312 0.124667 37.94 . 푑 . 푓 . 푉푐 퐴퐸푟푚푠 = 3.47591 휂퐹푙푢푖푑

…. (eq. 10)

Figure 4.8: Graph of AERMS vs. DOC, Vc, Feed and viscosity (Fluid)

Residual Plots for Log(AE) Normal Probability Plot Versus Fits 99.9 2

99 l a u

90 d 1 i t s n e e R c

r 0 d e e P t

10 e l -1 e

1 D 0.1 -2 -4 -2 0 2 4 1.4 1.6 1.8 2.0 Deleted Residual Fitted Value

Histogram Versus Order 2

12 l a u d y 1 i c

9 s n e e R u 0 q 6 d e e t r e F l -1 3 e D

0 -2 -1.50 -0.75 0.00 0.75 1.50 1 10 20 30 40 50 60 70 80 90 100 Deleted Residual Observation Order

Figure 4.9: Normal probability plot for AERMS

In the analysis report, looking at the smaller P values in for the coefficients and p values for ANOVA, it shows that all the predictors are significant to the model. S value in the summary of model is small and the R value is higher, which shows that the Equation created has a good fit to the actual data. In

Residual Vs. normal probability plot for the Log E data, the residuals appear to follow a straight line and show no evidence of non-normality, skewness, outliers, or unidentified variables. In the Residual Vs. Fitted values plot, the residuals appear to be randomly scattered about zero. Evidence of non-constant variance, missing terms, outliers, or influential points does not exists. Because these plots don’t reveal obvious pattern, it is concluded that the models are adequate.

Surface Roughness Model

Regression Equation(Minitab Output)

Log(Ra) = -0.840925 + 1.6941 Log(n) + 0.369348 Log(a) + 0.224513 Log(f) - 0.0778001 Log(vc)

Coefficients

Term Coef SE Coef T P Constant -0.84092 0.087316 -9.6308 0.000 Log(n) 1.69410 0.155334 10.9062 0.000 Log(a) 0.36935 0.024779 14.9057 0.000 Log(f) 0.22451 0.054425 4.1252 0.000 Log(vc) -0.07780 0.039721 -1.9587 0.053

Summary of Model

S = 0.0507287 R-Sq = 77.85% R-Sq(adj) = 76.99% PRESS = 0.291962 R-Sq(pred) = 75.60%

Analysis of Variance

Source DF Seq SS Adj SS Adj MS F P Regression 4 0.93152 0.931520 0.232880 90.495 0.0000000 Log(n) 1 0.30609 0.306093 0.306093 118.945 0.0000000 Log(a) 1 0.57176 0.571762 0.571762 222.181 0.0000000 Log(f) 1 0.04379 0.043792 0.043792 17.017 0.0000753 Log(vc) 1 0.00987 0.009873 0.009873 3.836 0.0528555 Error 103 0.26506 0.265061 0.002573 Total 107 1.19658

Fits and Diagnostics for Unusual Observations

No unusual observations

Surface roughness Model: Using the regression analysis we get:

Log C = - 0.840925, therefore C = 10^(0.840925)= 4.67867

α= 1.6941, β= 0.369348, γ = 0.224513, δ = - 0.0778001

1.6941 0.369348 0.224513 휂퐹푙푢푖푑 . 푑 . 푓 푅푎 = 0.0778001 6.933 . 푉푐 ……………. (eq. 11)

Figure 4.10: Graph of Ra vs. DOC, Vc, Feed and viscosity (Fluid)

Residual Plots for Log(Ra) Normal Probability Plot Versus Fits 99.9 2

99 l a

u 1

90 d i t s n e e R c

50 0 r d e e P t

10 e

l -1 e

1 D -2 0.1 -4 -2 0 2 4 -0.7 -0.6 -0.5 -0.4 Deleted Residual Fitted Value

Histogram Versus Order 2 12 l a

u 1 d y

9 i c s n e e R

u 0

q 6 d e e t r e F

l -1

3 e D -2 0 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 1 10 20 30 40 50 60 70 80 90 100 Deleted Residual Observation Order

Figure 4.11: Normal probability plot for Surface roughness (Ra)

Low R sq. value at 82% shows that the regression model does closely fits the given data, a better regression equation is possible. Considering that P value for the Log (f) coefficient is very high, which shows that the log (f) data will not affect the regression equation, and may be ignored for developing a better regression equation. The residuals in the graph appear to follow a straight line and are randomly scattered about zero. These plots don’t reveal obvious pattern, hence is no evidence of non-normality, skewness, outliers, or unidentified variables which shows that linear model is appropriate for the data.

Though, if a regression equation with a better fit is to be obtained, it is suggested that the log (f), i.e. predictor variable feed, can be ignored.

Grinding Temperature Model

Regression Equation(Minitab Output)

Log(T) = 1.46967 + 1.05199 Log(n) + 0.120309 Log(a) + 0.148352 Log(f) + 0.0430248 Log(vc)

Coefficients

Term Coef SE Coef T P Constant 1.46967 0.0273715 53.6936 0.000 Log(n) 1.05199 0.0486935 21.6044 0.000 Log(a) 0.12031 0.0077676 15.4885 0.000 Log(f) 0.14835 0.0170610 8.6954 0.000 Log(vc) 0.04302 0.0124516 3.4554 0.001

Summary of Model

S = 0.0159023 R-Sq = 88.52% R-Sq(adj) = 88.07% PRESS = 0.0288742 R-Sq(pred) = 87.27%

Analysis of Variance

Source DF Seq SS Adj SS Adj MS F P Regression 4 0.200838 0.200838 0.050209 198.548 0.0000000 Log(n) 1 0.118033 0.118033 0.118033 466.750 0.0000000 Log(a) 1 0.060665 0.060665 0.060665 239.894 0.0000000 Log(f) 1 0.019120 0.019120 0.019120 75.609 0.0000000 Log(vc) 1 0.003019 0.003019 0.003019 11.940 0.0007994 Error 103 0.026047 0.026047 0.000253 Total 107 0.226885

Fits and Diagnostics for Unusual Observations

Obs Log(T) Fit SE Fit Residual St Resid 1 1.47770 1.53081 0.0040870 -0.0531140 -3.45611 R 46 1.62325 1.59225 0.0038644 0.0309976 2.00949 R 55 1.55194 1.59746 0.0040269 -0.0455239 -2.95918 R

R denotes an observation with a large standardized residual.

Surface temperature Model: Using the regression analysis we get:

Log C = 1.46967, therefore C = 10^(1.46967)= 29.4896

α= 1.05199, β= 0.120309, γ = 0.387664, δ = 0.0769186

1.05199 0.120309 0.148352 0.0430248 푇 = 29.4896 . 휂퐹푙푢푖푑 . 푑 . 푓 . 푉푐

……………. (eq. 12)

Figure 4.12: Graph of Temperature vs. DOC, Vc, Feed and viscosity (Fluid) 84

Residual Plots for Log(T) Normal Probability Plot Versus Fits 99.9 2

99 l a u

90 d i t s

n 0 e e R c

50 r d e e P t -2 10 e l e

1 D 0.1 -4 -4 -2 0 2 4 1.55 1.60 1.65 1.70 Deleted Residual Fitted Value

Histogram Versus Order 2 l

20 a u d y i c s 0 n 15 e e R u q d

e 10 e t r

e -2 F l

5 e D

0 -4 -3 -2 -1 0 1 2 1 10 20 30 40 50 60 70 80 90 100 Deleted Residual Observation Order

Figure 4.13: Normal Probability plot for Temperature (˚F)

From the regression analysis it is seen that the S value is low and the R sq. value is high, but less than 95%, which tells that the regression line does not fit the remaining 10% of the given data. For a better regression model either the number of predictor variables could be reduces, which will also make the model simple.

For the Log E data, the residuals appear to follow a straight line. There is no evidence of non-normality, skewness, outliers, or unidentified variables.

Based on Residual Vs. Fitted values plot, the residuals are randomly scattered about zero. This tells that this linear model is appropriate for the given data, and

85 there does not exist of non-constant variance, missing terms, outliers, or influential points.

4.4 Prediction of Surface Roughness (Ra) based on AERMS

monitoring:

This research work attempts to take the use of AE monitoring of surface grinding a step further by using the acoustic emission signals to make an real time estimation of the surface quality, in terms of surface roughness of the workpiece. Measurements of Ra and acoustic emission for a range of Depth of cut, cutting speeds and feeds are used to create Ra and AE model for the proposed technique.

Correlation graphs shown in Figure 26, are based on AERMS and Ra model created in the previous sections and the measured data. As discussed in above sections AERMS value is directly influenced by the depth of cut, cutting speed and feed rate. Whereas the Surface roughness measurement increase directly with the increase of Depth of cut and feed rate, but decrease for the higher cutting speeds.

Figure 4.14 Correlation Graphs of Acoustic Emission (AERMS) Vs. Surface roughness (Ra)

4.5 Validation of Model

Four different statistical models are established in above sections to predict the Grinding force, AERMS, Surface Roughness and Grinding temperature.

Statistical models has to be evaluated to determine their validity. Above models were validated by two methods, first by using Analysis of Variance (ANOVA) method and second one, by comparing the predicted and the measured values.

Although the second method is more accepted method in the industry. The regression model fitness calculated by statistical method showed that the measured values are in close vicinity of the predicted values. The R square values calculated by ANOVA showed that 85 to 95 % measured values are represented by above established models. The error observed between the measured and the predicted values was between 10 to 13%. Both the methods showed that there is very less variation between the measured and the predicted values. It was also observed that the percentage error can be reduced when the viscosity of the fluid is considered as categorical parameter, which will give individual models for every cutting fluids. Four models obtained above in the equation 5 to 8, show strong potential to be used for the purpose of monitoring of Surface grinding process.

Table 4.6: Validation of experiments

Model R Square Percentage error

Force 98.25% 4-7%

Acoustic Emission 86.64% 2 to 10%

Surface roughness 76.99% 5 to 12%

Temperature 88.07% 4 to 10%

Chapter 5

Conclusions

This thesis studies the surface grinding process and its response variables.

The research was focused on the selection of different fluids. Using four different cutting fluids grinding parameters, Depth of cut, feed rate and wheel speed were varied for different sets of grinding passes. Grinding response variables viz.

Acoustic Emission signals, Normal and tangential forces, surface temperature and Surface roughness were measured. Change in response variable readings were studied. The results indicate that the cutting fluid does have an effect on the surface finish, and that the cutting fluid interacts with other variables to have an effect on overall quality of the product. Statistical relationships in terms of

Regression model were found out between different grinding parameters.

Various conclusions derived from the experimental results were:

1) The results consistently identified the cutting fluid as a significant factor

influencing the surface finish, Acoustic Emission, surface temperature and

cutting forces.

2) It is concluded from study that Viscosity, and compositions (ingredients)

of the cutting fluids, have different effects on the grinding mechanism.

Though, this conclusion needs further research and validation. As per the

observations the synthetic (C270) fluid provided the better surface quality

and low surface temperature, highest cutting forces and AE. Micro-

emulsion (Xt585) showed lower AE signals and cutting forces, but higher

roughness and surface temperature.

3) The depth of cut influences surface quality in a direct way. Increasing the

depth of cut increases the cutting resistance and the amplitude of cutting

force involved. Depth of cut also increases the Acoustic Emission and

cutting temperature.

4) Experiments show that as feed rate increases surface roughness also

increases. Increase in feed rate shows very slight increase in other

response quantities.

5) Increase in the cutting speeds, increases the total number of active grains

involved, and introduces surface to newer abrasive grains more

frequently, hence, an increase of cutting speed generally improves surface

quality. It is observed that increase in cutting speed decreases the cutting

force, but increases the AE and Surface temperature.

6) The research presents correlation between the AERMS and the surface

roughness of a workpiece, which shows that the parameters of acoustic

emission signal can be a useful tool to predict the surface roughness

during the grinding process.

7) Acoustic emission energy (mV) increases with the increase in MRR. There

is steep rise in AE signals with each increment in depth of cut, and gentle

rise with each increment of wheel speed and feed rate.

8) If grinding parameters are kept constant, then the AE signal magnitude

should show consistency. In this case magnitude of AE signals may only

vary due to process inconsistencies like cutting fluid, vibration, wheel

dressing and temperature. Such scenario will also provide rough surfaces.

Acoustic Emission has very good sensitivity to the grinding process and

can be used to predict and assure the quality of the surface

9) Temperature, force and acoustic emission measurement together will

provide rigid and efficient monitoring of the grinding process. Using all

these monitoring tools cannot always be feasible and can be costly.

Behavior of temperature forces and acoustic emission with each other was

studied, and it can be concluded that acoustic emission provides the

clearest results and a common ground to predict the final surface quality

and monitoring of process.

10) The use of AE as an real time process monitoring and characterization tool

can serve as a means of closely linking the manufacturing and quality

92 control stages together, and with further development, in a fully automated manufacturing environment, the quality control stage can be entirely eliminated with optimal real time monitoring and process control.

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