ANALYSIS OF SURFACE FINISH IN DRILLING OF COMPOSITES USING NEURAL NETWORKS
A Thesis by
Shashidhar Madiwal
B.E, Karnatak University, 1998
Submitted to The Department of Mechanical Engineering and the faculty of the Graduate School of Wichita State University in partial fulfillment of the requirements for the Degree of Master of Science
July 2006
ANALYSIS OF SURFACE FINISH IN DRILLING OF COMPOSITES USING NEURAL NETWORKS
I have examined the final copy of this thesis for form and content, and recommend that it be accepted in partial fulfillment of the requirement for the degree of Master of Science with a major in Mechanical Engineering.
______
Behnam Bahr, Committee Chair
We have read this thesis and recommend its acceptance:
______
Krishna Krishnan, Committee Member
______
Kurt Soschinske, Committee Member
ii DEDICATION
To my parents and relatives
iii ACKNOWLEDGEMENTS
I would like to thank my advisor, Dr. Behnam Bahr, for his invaluable assistance and friendly guidance throughout my master’s program. I would also like to thank the other committee members, Dr. Kurt Soschinske and Dr. Krishna Krishnan for their comments and assistance in this study. I would like to thank student members Sudhama, Habib, Rupinder and
Ashkhan for their help and support. Also, I would like to express my gratitude to Dave
Richardson (Raytheon representative), Dan Thurnau (SpiritAero Systems), and Steve Shofler
(Superior Tool Services).
iv ABSTRACT
Composite materials are widely used in the aerospace industry because of their high strength-to-weight ratio. Although they have many advantages, their inhomogeneity and anisotropy pose problems. Because of these properties, machining of composites, unlike conventional metal working, needs more investigation. Conventional drilling of composites is one such field that requires extensive study and research. Among various parameters that determine the quality of a drilled hole, surface finish is of vital importance. The surface finish of a drilled hole depends on speed, feed-rate, material of the work piece, and geometry of the drill bit.
This project studied the effect of speed and feed on surface finish and also the optimization of these parameters. Experiments were conducted based on Design of Experiment
(DOE) and qualitative verification using Artificial Neural Network (ANN). Relevant behavior of surface finish was also studied.
In this project, holes were drilled using a conventional twist drill at different cutting speeds (2,000 to 5,000 rpm) and feed rate was varied from 0.001 to 0.01 ipr for solid carbon fiber laminate (composite material). The other material drilled is BMS 8-276 form 3 (toughened resin system). Also five different drill bits were used to conduct experiments on BMS 8-276 form 3.
Speed values were 5,000, 3,000, and 2,000 rpm and feed rates were 0.004, 0.006, and 0.01 ipr.
The effect of speed, feed rate, and different drill geometries was analyzed with respect to surface finish in the drilled composites.
v TABLE OF CONTENTS
Chapter Page
1. INTRODUCTION 1
1.1 Project Goal 1
2. INTRODUCTION TO DRILLING OF COMPOSITES 3
3. INTRODUCTION TO SURFACE TEXTURE 10
4. INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS 15
4.1 Multi-Layered Neural Network 16 4.2 Back Propagation Theory 19
5. LITERATURE SURVEY 22
6. EXPERIMENTAL ANALYSIS 31
6.1 Application of Artificial Neural Networks 32 6.2 Design of Experiment 49 6.3 Data Comparison 63
7. RESULTS AND DISCUSSION 69
8. CONCLUSION 76
9. LIMITATIONS 77
10. FUTURE WORK 78
LIST OF REFERENCES 79
APPENDICES 82
A. Figures Showing Optimum Artificial Neural Networks 83 B. Tabulation Showing Output Data from Neural Network Analysis 84 C. Pictorial Representation of Drilled Surfaces 88
vi LIST OF TABLES
Table Page
1. Notations Used for Various Drill Bits 7
2. Summary of Factor Effects: S/N Ratio Analysis 26
3. Training Data for ANN (SCFL material) 34
4. ANN Input for Brad Spur 35
5. ANN Input for Double Margin 35
6. ANN Input for Conventional 35
7. ANN Input for ST1257B 35
8. ANN Input for ST1255G 36
9. Network Characteristics 36
10. Network Architecture for SCFL 36
11. Network Architecture for BMS 8-276 form 3 37
12. Surface Finish Using Artificial Neural Network for SCFL material 37
13. Surface Finish Using Artificial Neural Network for Brad Spur 37
14. Surface Finish Using Artificial Neural Network for Double margin 37
15. Surface Finish Using Artificial Neural Network for Conventional 38
16. Surface Finish Using Artificial Neural Network for ST1257B 38
17. Surface Finish Using Artificial Neural Network for ST1255G 38
18. Design of Experiments Input Data for SCFL Material 51
19. Tabulation of Surface Finish Output Using DOE for SCFL Material 54
20. Design of Experiments Input Data for Brad Spur 55
21. Tabulation of Surface Finish Output Using DOE for Brad Spur 56
vii 22. Design of Experiments Input Data for Double Margin 57
23. Tabulation of Surface Finish Output Using DOE for Double Margin 57
24. Design of Experiments Input Data for Conventional 59
25. Tabulation of Surface Finish Output Using DOE for Conventional 59
26. Design of Experiments Input Data for ST1257B 61
27. Tabulation of Surface Finish Output Using DOE for ST1257B 61
28. Design of Experiments Input Data for ST1255G 62
29. Tabulation of Surface Finish Output Using DOE for ST1255G 62
30. Experimental Data for SCFL Material 69
31. Neural Network Data for SCFL Material 69
32. DOE Predicted Data for SCFL Material 69
33. Experimental Data for Brad Spur 70
34. Neural Network Data for Brad Spur 70
35. DOE Predicted Data for Brad Spur 70
36. Experimental Data for Double Margin 71
37. Neural Network Data for Double Margin 71
38. DOE Predicted Data for Double Margin 71
39. Experimental Data for Conventional 72
40. Neural Network Data for Conventional 72
41. DOE Predicted Data for Conventional 72
42. Experimental Data for ST1257B 72
43. Neural Network Data for ST1257B 73
44. DOE Predicted Data for ST1257B 73
viii 45. Experimental Data for ST1255G 73
46. Neural Network Data for ST1255G 73
47. DOE Predicted Data for ST1255G 74
48. Output Data from DOE and Neural Network Analysis for SCFL 84
49. Output Data for SCFL with Least RMS Value, RMS = 0.169401 92
50. Output Data for Brad Spur with Least RMS Value, RMS Error = 0.169615 92
51. Output Data for Double Margin with Least RMS Value, RMS Error = 0.102468 93
52. Output Data for Conventional with Least RMS Value, RMS Error = 0.090178 93
53. Output Data for ST1257B with Least RMS Value, RMS Error = 0.115093 93
54. Output Data for ST1255G with Least RMS Value, RMS Error = 0.146705 94
ix LIST OF FIGURES
Figure Page
1. Cutting heads used in drilling composites 4
2. Twist drill nomenclature 4
3. Hole shape deviations 5
4. Geometry of SCFL composite coupon 6
5. Brad spur carbide drill bit 7
6. Conventional carbide drill bit 7
7. Double margin (Amamco solid carbide double margin step drill) 8
8. Spirit (ST1255G SC parabolic flute drill) 8
9. ST1257B solid carbide straight flute drill 8
10. Design of drilling fixture to hold the curve work piece 9
11. Fadal VMC20 with experimental setup 9
12. Surface texture of Component 10
13. Surface profiling method 11
14. Profile with parameters 12
15. Mitutoyo-surf-test SJ400 13
16. Surface tester with probe 13
17. Neural network processing element 16
18. Neural network structure 17
19. Damaged zone extension, D, vs drilling speed-to-feed rate ratio, Vr/Vt 23
20. Quality criteria for drilling fiber reinforced composite materials 24
21. Factor effects for surface roughness 26
x 22. Factor effects for delamination 26
23. Comparison between delamination experimental measurements and predictions made with fusion model 28
24. Comparison between surface roughness experimental measurements and predictions made with fusion model 28
25. Actual, predicted Ra, Rz values 29
26. Evolution of arithmetic mean roughness with cutting time 29
27. Schematic of a single neuron in a multilayered feed forward network 31
28. Neural network output (feed vs surface finish at 5,000 rpm) 39
29. Neural network output (feed vs surface finish at 4,500 rpm) 39
30. Neural network output (feed vs surface finish at 4,000 rpm) 40
31. Neural network output (feed vs surface finish at 3,500 rpm) 40
32. Neural network output (feed vs surface finish at 3,000 rpm) 41
33. Neural network output (feed vs surface finish at 2,500 rpm) 41
34. Neural network output (feed vs surface finish at 2,000 rpm) 42
35. Neural network output (feed vs surface finish at 5,000 rpm) 42
36. Neural network output (feed vs surface finish at 3,000 rpm) 43
37. Neural network output (feed vs surface finish at 2,000 rpm) 43
38. Neural network output (feed vs surface finish at 5,000 rpm) 44
39. Neural network output (feed vs surface finish at 3,000 rpm) 44
40. Neural network output (feed vs surface finish at 2,000 rpm) 45
41. Neural network output (feed vs surface finish at 5,000 rpm) 45
42. Neural network output (feed vs surface finish at 3,000 rpm) 46
43. Neural network output (feed vs surface finish at 2,000 rpm) 46
xi 44. Neural network output (feed vs surface finish at 5,000 rpm) 47
45. Neural network output (feed vs surface finish at 3,000 rpm) 47
46. Neural network output (feed vs surface finish at 2,000 rpm) 48
47. Neural network output (feed vs surface finish at 5,000 rpm) 48
48. Neural network output (feed vs surface finish at 3,000 rpm) 49
49. Neural network output (feed vs surface finish at 2,000 rpm) 49
50. Response surface for DOE-predicted output for SCFL 54
51. Response surface for DOE-predicted output for brad spur 56
52. Response surface for DOE-predicted output for double margin 58
53. Response surface for DOE-predicted output for Conventional 60
54. Response surface for DOE-predicted output for ST1257B 60
55. Response surface for DOE-predicted output for ST1255G 63
56. Surface finish values at 5,000 rpm and 0.004 in/rev 64
57. Surface finish values at 5,000 rpm and 0.006 in/rev 64
58. Surface finish values at 5,000 rpm and 0.01 in/rev 65
59. Surface finish values at 3,000 rpm and 0.004 in/rev 65
60. Surface finish values at 3,000 rpm and 0.006 in/rev 66
61. Surface finish values at 3,000 rpm and 0.01 in/rev 66
62. Surface finish values at 2,000 rpm and 0.004 in/rev 67
63. Surface finish values at 2,000 rpm and 0.006 in/rev 67
64. Surface finish values at 2,000 rpm and 0.01 in/rev 68
65. Comparison of drill bits 68
66. Neural network showing two hidden layers with five nodes each 83
xii 67. Neural network showing one hidden layers with four nodes each 83
68. Drilled surface picture at 5,000 rpm, 0.001 ipr, surface finish 1.1 µm 88
69. Drilled surface picture at 5,000 rpm, 0.01 ipr, surface finish 1.72 µm 88
70. Drilled surface picture at 2,500 rpm, 0.002 ipr, surface finish 1.18 µm 88
71. Drilled surface picture at 2,500 rpm, 0.008 ipr, surface finish 1.59 µm 88
72. Drilled surface picture at 3,000 rpm, 0.004 ipr, surface finish 0.59 µm 88
73. Drilled surface picture at 3,000 rpm, 0.01 ipr, surface finish 0.99 µm 88
74. Drilled surface picture at 2,000 rpm, 0.004 ipr, surface finish 1.05 µm 89
75. Drilled surface picture at 2,000 rpm, 0.01 ipr, surface finish 1.49 µm 89
76. Drilled surface picture at 3,000 rpm, 0.004 ipr, surface finish 2.00 µm 89
77. Drilled surface picture at 3,000 rpm, 0.01 ipr, surface finish 2.53 µm 89
78. Drilled surface picture at 2,000 rpm, 0.004 ipr, surface finish 1.65 µm 89
79. Drilled surface picture at 2,000 rpm, 0.01 ipr, surface finish 2.05 µm 89
80. Drilled surface picture at 3,000 rpm, 0.004 ipr, surface finish 0.98 µm 90
81. Drilled surface picture at 3,000 rpm, 0.01 ipr, surface finish 1.55 µm 90
82. Fiber pullout 90
83. Damaged zone 90
84. Fiber pullout 91
85. Magnified damage zone 91
xiii LIST OF ABBREVATIONS
ANN Artificial Neural Network
BPN Back Propagation Network
BS Brad Spur
CD Conventional Drill
DM Double Margin
DOE Design Of Experiment ipr Inches per Revolution
ISO International Standards Organization rpm Revolutions Per Minute
SC Solid Carbide
SCFL Solid Carbon Fiber Laminate
SF Surface Finish
xiv LIST OF SYMBOLS
Rmax Maximum height of the irregularities
Ra Arithmetical mean value
L Sampling length
N Total sampling intervals
Xi Input to neural network
Wij Interconnection weight s Combined input f(s) Activation function
Yj Network output
Delj Error to be propagated back for the jth processor in the output layer
Tarj Target for the jth processor in the output layer
F’ Derivative of activation function
Vr/Vt Cutting speed to feed ratio
∆D Deviation of drilled hole diameter fk Roundness error
Da Delamination factor p Scalar input vector b Scalar bias
µmtr Micro-meter (surface finish measurement unit)
µinch Micro-inch (surface finish measurement unit) n Neuron output
º Degree
xv CHAPTER 1
INTRODUCTION
The aerospace industry is making a major effort to utilize increasing amounts of composite materials in order to obtain high strength-to-weight ratios. However, these materials are easily damaged unless machining is performed properly. Several hole-production processes, such as conventional drilling, ultrasonic drilling, laser-beam drilling, water-jet drilling, etc., have
been proposed for a variety of economic and quality reasons, but conventional drilling remains
the most preferred and adopted technique in the industry today. Due to inherent qualities such as
anisotropy and brittleness, composite materials, when subjected to drilling, exhibit damage
phenomena such as spalling, delamination, and crack formation.
The quality of a hole plays a vital role in drilling. Obtaining desired hole dimensions, roundness, and surface finish along the length of the hole are of vital importance to the industry.
This research involved qualitative analysis of surface finish obtained during the drilling of Solid carbon fiber laminate material specimen provided by Raytheon using a conventional drill bit, and a second set of experiments on BMS 8-276 form 3 material provided by SpiritAero Systems using five different drill bit types. Experimental results were verified using a neural network technique to set up a work platform for future experiments and research.
1.1 Project Goal
Research in the field of machining of composite materials is of prime importance for the industry.In conventional drilling used for composite materials, hole quality is the manufacturer’s priority. Hole quality is determined by surface finish, roundness, hole diameter, etc. This research involved the study of surface finish obtained during drilling of Solid carbon fiber laminate (SCFL) and BMS 8-276 form 3 material. Factors affecting surface finish were
1 determined and thus conclusions were drawn about optimum feed rate and speed of operation for better hole quality. Many factors affect hole quality, which can be divided into controllable and non-controllable.
Controllable Factors:
Speed
Feed Rate
Workpiece material
Drill geometry and material
Non-Controllable Factor: Machine Accuracy.
Taking the above factors into consideration, experiments were conducted to determine the best combination of factors to obtain optimum hole quality. A methodology was adopted to conduct these experiments in an organized fashion. This method was the Design of Experiment
(DOE). After the experiments were completed the data was analyzed using neural networks
Technique. Details regarding this technique and DOE will be explained in later chapters.
2 CHAPTER 2
INTRODUCTION TO DRILLING IN COMPOSITES
Composites can be constructed of any combination of two or more materials, whether
metallic, organic, or inorganic. Major consistent forms used in composite materials are fibers,
particles, laminate or layers, flakes, fillers and matrixes.
Conventional metal-cutting drill tips were designed so that the tip heating the metal
would provide the plastic flow needed for efficient cutting. Since composite materials can not
tolerate this heat, production must be slowed down to keep the heat as low as possible. Drill
designs had to abandon cutting tips with negative rakes and wide chisel points because the drill
scrapes the material and causes it to resist penetration by the drill tip. The operator must exert
pressure to drill the hole, and pressure causes heat buildup [2]. Modified drill geometries were
used to counter the problem. Typical drills are as shown in Figure 2.1. The use of conventional
twist drill is also popular in drilling of composites. Nomenclature of a twist drill is as shown in
Figure 2.2.
The best way to analyze the drilling operation in composites is to examine the chips, which ideally are dry and easily moved. If the speed of the cutting tool is too high, heat will make the resin sticky and produce a lumpy chip; if the cutting edge is scraping and not cutting the plastic, the chips will be large and flaky. Either type will eventually clog any evacuation system [2].
During hole fabrication in composites, shape deviations occur. Hole shape deviations define the difference between the shape of the machined hole and the geometrical shape required by the drawing. Hole shape deviates with respect to roundness (oval) and the profile from the mean line as shown in Figure 2.3. An oval occurs as either a single or a multiple.
3 Errors of a profile cross section are roughness, waviness, and lay. Vibration in the system machine tool work piece is the reason that surface waviness occurs [10].
Figure 2.1. Cutting heads used in drilling composites: (a) solid shank drill (b) drill guide system (c) fluted twist drill [2].
Figure 2.2. Twist drill nomenclature [16].
4 Parameters for Grading Hole Quality
Figure 2.3. Hole shape deviations: (a) Theoretical view (b) Actual view [10].
Other forms of drilling composites commonly practiced are as follows:
1. Laser drilling
2. Ultrasonic drilling
3. Abrasive drilling
5 Experiments conducted for this research used Solid Carbon Fiber Laminate as the workpiece material. Each workpiece was cut to a size of a one inch by six inches from a larger specimen that had a curvature of approximately eighty-three inches, as shown in Figure 2.4.
Figure 2.4. Geometry of SCFL composite coupon.
The drill bit used for these experiments was as follows:
Type Drill Diameter Material Point Angle Clearance
Two flute drill 0.25” Carbide 135° 12°
The geometry of the BMS 8-276 form 3 coupon was simple- a flat piece seven inches long length and approximately half inch thick.
Technical details of the five distinct drill bits used were not available; hence, the provider’s name or the commercial name of the bits were used for experimentation and analysis.
The five drill bits used are as shown in Table 2.1 along their respective notations. The pictures showing the five drill bits are in form of Figures 2.5, 2.6, 2.7, 2.8 and 2.9. There was a special fixture designed for holding the composite workpiece which is shown in Figure 2.10. The NC machine along with the fixture is shown in Figure 2.11.
6 TABLE 2.1
NOTATIONS USED FOR VARIOUS DRILL BITS
Drill Bit Type Notation Used
ST1255G solid carbide parabolic flute drill ST1255G
ST1257B solid carbide straight flute drill ST1257B
Amamco solid carbide double margin step drill DM
Brad spur carbide drill bit BS
Conventional carbide drill bit CD
Figures 2.5 to 2.9 show the five drill bits.
Figure 2.5. Brad spur carbide drill bit
Figure 2.6. Conventional carbide drill bit
7
Figure 2.7. Amamco solid carbide double margin step drill
Figure 2.8. ST1255G SC parabolic flute drill
Figure 2.9. ST1257B Solid carbide straight flute drill.
8
Figure 2.10. Design of Drill fixture to hold the curve work piece
Figure 2.11. Fadal VMC20 with experimental setup
9 CHAPTER 3
INTRODUCTION TO SURFACE TEXTURE
One of the principal design considerations for highly stressed components will be, the surface condition produced during manufacturing. Surface technology describes details and evaluates the condition of both the surface and the surface layers of manufactured components.
Surface texture has been extended to include the surface integrity, thereby including the influence of the outermost boundary of a component, as well as those at the outermost layers which differ measurably from the base material [1].
Definitions Related to Surface Quality
Figure 3.1. Surface Texture of Component [1].
Waviness: The recurrent deviation from an ideal surface and of a relatively large
wavelength as seen in Figure 3.1. Such deviations generally result from deflections of the tool,
workpiece, or machine vibration or warping, and means that the workpiece and tool should be
held rigidly with as little overhang as possible in order to minimize wariness.
Lay: The direction of the predominant surface pattern produced by feed marks as shown
in Figure 3.1.
10 Roughness: The finely spaced irregularities or irregular deviations as shown in Figure
3.1. Roughness is affected by tool shape and feed as well as machining conditions. The figure shown is an example from ISO/R468. Roughness is described by the maximum height of the irregularities, Rmax, and the arithmetical mean value, Ra. Rmax is the maximum peak-to-valley height within the sampling length. Ra is the average of the numerical deviations from the mean line of the surface within the sample length. The relation between Ra and Rmax for triangular irregularities with an approximation is,
Ra ≈ Rmax / 4 (3.1)
Profiling: A means of measuring the profile of a surface. This results in a two- dimensional graph of the shape of the surface in the sectioning plane created by the profiling instrument. The most common type of profiling instrument draws a stylus across the surface and measures its vertical displacement as a function of position as shown in Figure 3.2.
Figure 3.2. Surface profiling method [1].
Surface Texture Measurement
The most prevalent measuring technique for surface texture employs a mechanical-
electronic device, that provides a readout indicating the roughness of the surface profile taken
during the passage of a small radius stylus over a short straight line path on the surface. The most
11 common diamond stylus has a 0.0004-inch radius and usually is used with a 0.030-inch (0.08 mm) cutoff width. The total stylus travel is usually twenty to sixty times the cutoff width, with the electronic circuitry continuously averaging the readings over the set cutoff width. These instruments can read average roughness, Ra, Peak count or other roughness designations depending on the particular instrument design [1]. Details regarding this instrument will be discussed in detail in the following sections.
Average Roughness (Ra): The area between the roughness profile and its mean line on the integral of the absolute value of the roughness profile height over the evaluation length.
Equation 3.2 gives the mathematical relation for Ra.
L Ra = 1/L 0∫ │r(x) │dx (3.2)
When evaluated from the digital data, the integral is normally approximated by the trapezoidal rule which is given by equation 3.3.
N Ra = 1/N 1Σ │rn│ (3.3)
Graphically, the average roughness is the area between the roughness profile and its center line divided by the evaluation length. Refer to Figure 3.3.
Figure 3.3. Profile with parameters [1].
The Mitutoyo Surf Test SJ400 was used for measuring the surface profile in these experiments. The Surf Test 400 (Figure 3.4) consists of various precision parts and should be
12 treated with utmost care. The instrument is sensitive to vibration, shock, and heat. When the instrument is used for measuring the surface finish, it should always be placed on a measuring desk.
Surface Tester
Figure 3.4. Mitutoyo Surf Test SJ400. Figure 3.5. Surface tester with probe.
The machine should be calibrated before it is used for taking measurements. A precision reference specimen should be used for calibration. If the instrument-displayed Ra value does not agree with that of the specimen, then the gain volume of the drive should be adjusted to agree with it. Once the instrument has been attached completely and calibrated, the nosepiece (Figure
3.5) is placed on the surface of the workpiece, and the zero adjust knob is rotated for proper indication. Before taking the reading of surface finish, the following must be determined and set:
Parameter: Ra (Average Surface roughness)
Range: Based upon the estimated roughness
Cut off length: To determine the evaluation length
13 Generally the workpiece surface to be measured is not uniform in roughness and varies depending on the portion or portions to be measured so that the population mean of surface roughness can be obtained. For the direction in which the measurement is made, the workpiece surface must be set so that the maximum value of surface roughness is obtained.
For measuring Ra, an evaluation length is not always favorable, because the measurement of a machined surface having a series of lays at regular intervals, such as those from shaping or milling, it is not rare that the peaks or valleys of less than five only are included for evaluation by a recommended length, resulting in a false measurement. To solve this problem, it is recommended that an evaluation length of at least six times longer then the interval of lays is used. This eliminates waviness, thus making the evaluation length longer and measurement result better.
14 CHAPTER 4
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
Neural systems can learn to approximate any function and behave like associative memories using exemplar data that is representative of the desired task. Neural systems estimate a function without requiring a mathematical description of how the output functionally depends on the input. They learn from the input-output data samples.
An artificial neural network (ANN) consists of numerous simple processing units or neurons that can be modified to realize a desired behavior. Neural networks are “trained” by being given a series of examples of correct responses, and then the connections between processors are strengthened or weakened according to the level of success in reproducing what is wanted. The network is never given an explicit body of rules to follow-its program is contained in the strengths and weaknesses of different links within it.
Neural Network Background
In general, neural networks can be thought of as a collection of interconnected parallel processing elements, in which knowledge possessed by the network is represented by the strength of interconnections between processors. The strength of an interconnection is denoted by a numerical quantity, referred to as an interconnection weight. The interconnections themselves can be thought of as unidirectional communication links, which provide a means of transmitting input/output signals between processing elements. The interconnection weight modifies the signal (usually by multiplication) to reflect the knowledge stored along the data path. The processing element, illustrated in Figure 4.1 may have any number of input paths but only one output (interconnection links). The input, Xi, can originate from the output paths of other processing elements or themselves, in the case of feedback or from external sources.
15
Figure 4.1. Neural network processing element.
Inputs are modified by the interconnection weights (Wij) and combined to from a single result (typically by summing), s. The combined input, s, is then modified by an activation function, f(s), which can be as simple as a threshold function, for which output is produced only if the combined inputs exceed a given level, or as a complex as a nonlinear continuous function such as the Sigmoid or Hyperbolic Tangent, which generates an output, Yj, proportional to the combined input. The activation function response is transmitted along the output path. Output signals may become the input to other processing elements or sent to external sources for interpretation.
A neural network consists of a number of processing elements joined together. Its architecture generally resembles layers of processing elements with full or random connections between successive layers as illustrated in Figure 4.2. The first layer is usually an input buffer where the data is presented to the network; the last layer is an output buffer that holds the networks response. The layers between the input and output buffer are called hidden layers [15].
16
Figure 4.2. Neural network structure.
4.1 Multilayer Feed Neural Network
A neural network is a parallel-processing architecture in which knowledge is represented in the form of weights between a set of highly connected processing elements. Analog ANNs have demonstrated the capability to perform nonlinear pattern association between input and output variables. Development of the back propagation algorithm has resulted in renewed interest in this area. The back propagation network (BPN) algorithm computes weights in the network in order to minimize the output error in a least-squared sense. Robustness and generalization capabilities make it an attractive alternative to conventional classifiers [11].
A multilayer BPN consists of multiple layers of processing elements that are interconnected by weighted arcs. Each element sums the product of its inputs and the connection weights from the previous layer, and then limits it by a nonlinear thresholding function. The sigmoidal function defined as:
f(s) = 1/ (1+exp(-s)) (4.1)
This threshold function has been a choice in many applications. The next layer uses
outputs from the processing elements of the previous layer and computes the weighted sum
limited by the thresholding function, and so on. The training stage of the BPN uses errors
17 propagated from the output layer nodes to lower-level nodes to adjust weights. The local weight corrections are performed using the Norm-Cum-Delta learning rule.
Network Operations
There are two distinct phases in network operations: learning and recall. Because the recall operation is part of the back propagation algorithm used in the learning process, this will be described first.
Recall: Compared to learning, recall is relatively simple. It begins by presenting the input layer with an input pattern. Input signals, now representing output from the input buffer, are broadcast to the hidden layer processors through the connection weights, Wji. Signals are multiplied by the weights and summed by the hidden-layer processors (threshold is also included in the summation). The summed inputs are passed through the activation function f(s) to yield an output signal, Yj, that propagates through the weights Wji to the next layer (output). There, each processor receives the weighted output of every element in the previous (hidden) layer. For a network of multiple (hidden) layers, the operation is repeated layer by layer. The two equations used in the recall mode are shown in equations (4.2) and (4.3).
General form of the recall operation:
Summation:
N Sum, Sj= X * Wji (4.2) ∑i i
where Sj is the summed weighted signals for the jth processor in the current layer, Xi is the
output from the ith of previous layer, and Wji is the weight from the ith processor of the
previous layer to jth processor of current layer.
Output Signal:
Yj=f(s) = 1 (4.3) 1( + exp( −Sj ))
18 where Yj is the output and summed weighted input of the jth processor in the current layer
4.2 Back Propagation Theory
Learning:
In essence, the back propagation algorithm teaches the network by presenting a known input pattern and having the network calculate an output response using the current set of weights and thresholds. The output pattern, or “Target,” and an error are computed. The error is propagated back through the network to adjust the weights and thresholds to minimize error between the two patterns.
This two-step learning process of feeding forward and propagating the error back is repeated for every pattern in the training set until the network converges and responds with the desired patterns (training sets include input pattern and desired output pattern).
The first step in the back propagation algorithm has already been described in the section on recall. The second step in the algorithm begins by subtracting the output of each processor
(output layer) from the corresponding “Target” patterns to produce a difference (error). The value is then multiplied by the derivative activation function, evaluated at the current net value of the output processor to produce an error value (Del) for that particular processor. Del is computed by equation (4.4).
Del j = (Tar j - Yj)* F’ (S j) (4.4)
where Delj is the error to be propagated back for the jth processor in the output layer, Sj is the
summed weighted signals for the jth processor in the current layer, Yj is the output for the jth
processor in the output layer, Tarj is the Target for the jth processor in the output layer, and F’ is
the derivative of the activation function (Sigmoid).
The derivative of Sigmoid equals
19 F’(s) = F(s)(1-F(s)) (4.5)
The next step is the adjustment of the weights between the output and hidden layers. This is done in two steps. The first step determines the actual amount of weight change, including the momentum term that enhances network convergence. The second step actually changes the interconnection weight. The two relationships are shown by equations (4.6) and (4.7).
Del Wji(n+1) = Φ * DelWji(n)+ β *Delj*Yi (4.6) where DelW ji(n+1) is the current change in weight W ji at the next step n+1, DelW ji(n) is the previous change in weight W ji at step n, initially set to zero, Delj is the error to be propagated back from the jth processor in noutput layer, Yi is the output for the ith processor in the lower layer connected to the weight in question, and O is the processor gain level set from 0 to 1.
New Weight Value:
Wji(n+1) = Wji(n) + DelWji(n+1) (4.7)
where W ji(n+1) is the new weight W ji value at step n+1 (after adjustment), and W ji(n) is the
previous weight W ji at step n(before adjustment).
The momentum term changes the weight according to the previous weight changes. This
has a tendency to filter out high-frequency variations in the error surface. It has been observed
that values around 0.9 for both gain and momentum provide good converging rates with an
acceptance level of oscillation.
Next, the interconnection weights associated with the hidden layer are adjusted. The
training process used earlier will not work here, since hidden layers have no target. Therefore,
the solution lies in propagating the output error back through the network layer by layer
adjusting weights at each layer. Hence, each processor in the hidden layer receives the Del error
signal which is the weighted sum of the preceding (output) layer’s error signal. The hidden
20 processor passes this error on to all processors in the next level to which it connects, stopping only when the next lower layer is the input buffer.
Thus, the propagation error Del for a hidden layer processor is produced by the summing the products of each processor’s Dels in the preceding (output) layer and the interconnection weight joining the two processors and then multiplying by the derivative of the activation function evaluated at the current processor’s net level (calculated earlier during the feed forward process and stored). This calculation is expressed in the equation form as
N Del = F’ (sum ) * ( Del * W ) (4.8) i j ∑k k kj where Delj is the error to be propagated back for the jth processor in the hidden layer, Delk is the error from the kth processor in the previous layer, Sumj is the summed weighted from the kth processor of the previous layer to jth processor of the current layer, and F’is the derivative of the activation function (Sigmoid).
After finding the hidden layer’s Dels, the weights associated with the processors must be adjusted by applying the equation used earlier. The process is repeated for every processor in each layer, from output to input, including threshold weights. The back propagation algorithm is applied to each pattern set, input and target, for all pattern sets in the training set. Because the learning process is iterative, the entire training set will have to be presented to the network repeatedly, until the global error reaches a minimum acceptable value [15].
21 CHAPTER 5 LITERATURE REVIEW
Over the years, less research has been conducted to determine the surface finish obtained during the drilling of composites. From various research papers it can be concluded that the quality of a hole is determined by the hole roundness, burr height, fiber pullout, and delamination. Although surface finish is a important factor in determining hole quality, very few reviews mention about this factor.
In machining composite parts, a finish comparable to metals cannot be achieved because of inhomogeneity and anisotropy of material. Although some new technologies can attain satisfactory results in terms of cut quality and operational times, their industrial applicability is strongly limited by machine cost, and their effectiveness is confined to specific materials and/or operations.
Fiber pullout and fuzzing, intralaminar cracks, and delamination are typical damage modes occurring in a composite material subjected to drilling. It is expected that such damage will result in poor mechanical properties of the material around the hole. A good finish may be of considerable importance, especially when the edges of the hole are designed to carry a concentrated load, such as in riveted and bolted joints. In spite of this, little research effort has been expended to determine the optimum cutting parameters for obtaining a satisfactory hole quality in drilling composite materials by conventional methods [9].
The problem of optimizing of drilling parameters in machining glass fiber-reinforced plastics (GFRP) occurred where flat panels obtained by hand lay-up and reinforced with mat and woven roving were machined under different drilling conditions by using a conventional high speed steel tool. Two polyester resins were adopted as matrix systems. Because of lack of
22 standards to quantitatively evaluate the damage, the width of the damage zone was conveniently assumed as an index of drilling quality. Experimental results showed that this quality index was strongly affected by the cutting speed to feed ratio, Vr/Vt; in particular, large damaged zones were observed when low Vr/Vt values were adopted. When Vr/Vt was increased, the extent of the fractured zone reached a minimum, beyond which it remained constant as shown in Figure
5.1. Both the width of the damaged zone and minimum Vr/Vt ratio resulting in the minimum damage width were found to be negligibly influenced by matrix type [8].
Figure 5.1. Damaged zone extension, D, vs drilling speed to feed rate ratio, Vr/Vt [8].
Parameters for grading hole quality in drilling of composites were suggested by Koberic
and Miskovic [10]. They suggested an assessment of various features involved in the drilling
process. The assessment of the form deviations in view of dimensional accuracy is based on the
deviation of the drilled hole diameter ∆D against the tool diameter (as measure for the required
diameter). Regarding accuracy of the shape the roundness error (f k) is applied which describes the deviation from the ideal roundness as shown in Figure 5.2. Due to low wall thickness of the
23 components and depths of drilling, cylindrically deviation becomes a significant criterion of quality [10].
Figure 5.2. Quality criteria for drilling fiber reinforced composite materials [10].
The assessment of the form deviations in view of dimensional accuracy is based on the deviation of the drilled hole diameter ∆D against the tool diameter (as measure for the required
diameter). Regarding accuracy of the shape, the roundness error (f k), which describes the deviation from the ideal roundness is applied as shown in Figure 5.2. Due to low wall thickness of the components and depths of drilling, cylindrical deviation becomes a significant criterion of quality. All these errors are caused by the behavior of the tool, particularly the misalignment of the axes well as rigidity. A significant criteria of quality are material damages, mainly in the surface layers. Characteristic shapes of damage are edge chipping and spalling that appear in composites with glass and carbon fibers, and a fuzzing characteristic in agamid fibers.
Delamination that represents separation of surface layers of the material upon entrance of the exit
24 of the tool, differs from crack formation within the working piece, which, in the case of laminates, are often interlaminar. Usually a measure of this error, the maximally damaged surface vertical to the drilling axis, is taken [10]. In addition to surface roughness and roundness error they also suggest using the fuzzing parameter and delamination parameter as a measure of hole quality.
Other related research involving drilling of composite material and testing of surface roughness was presented by Enemuoh [12]. Material used in this research consisted of a magnamite graphite fiber-reinforced polyether ether ketone (AS4/PEEK) composite. Cutting speed and drill tool material were reported to have the primary effect on surface roughness and delamination during drilling of this material.
This research involved a method for characterization of the machinability of composite materials. The method was based on the parametric analysis of the drilling process using the
Design of Experiment approach. It aimed at quantifying the effect of cutting speed, feed rate, tool material and tool geometry on delamination, surface roughness, and thrust force during drilling of carbon fiber-reinforced composites.
The analysis of the data obtained was conducted using Analysis of Variance (ANOVA).
The summary of factor effects is shown in Table 5.1. Clearly, tool material has the strongest effect on machinability responses measured by surface roughness and delamination of holes drilled in AS4/PEEK. Additionally, cutting speed is the most significant factor affecting the surface roughness accounting for 45 percent of the total effect. Unlike with the machining of metals, feed rate has only a minor influence on surface roughness of machined composites. The type of the tool material has the most significant effect on delamination, a 60 percent contribution. The cutting speed and drill point angle are the next highest contributors, almost 15
25 percent for each factor, refer to Figure 5.3. Finally, delamination is least affected by feed rate with its ten percent contribution, refer to Figure 5.4 [12].
TABLE 5.1
SUMMARY OF FACTOR EFFECTS: S/N RATIO ANALYSIS [12]
Figure 5.3. Factor Effects for Figure 5.4. Factor Effects for Surface Roughness [12]. Delamination [12]
Another study presented by Enemuoh and El-Gizaway [6] involved the use of neural network based sensor fusion for prediction of delamination and surface roughness in composite
26 drilling. They suggested that numerous factors influence the quality characteristics, surface finish
(Ra) and delamination (Da) during drilling operations. Rather than specific cutting condition values, a more appropriate neural network was designed using a range of drilling parameters and conditions. This study was restricted to two drilling parameters (feed rate and cutting speed) and two drilling conditions (tool material and tool geometry). The two sensors used were thrust force
(z-component) and acoustic emission (z-component). It has been shown that these sensors provide relevant data that correlate with the aforementioned drilling conditions. Cutting speed, tool material, tool point angle, and feed rate comprise a Taguchi Orthogonal Array. The values selected for the experiment were chosen so as not to obscure the influence of any factor on the neural network. Additionally, each of the nine array experiments were repeated in order to evaluate the variability associated with a given test condition and to reduce the experimental errors. The experimental responses in this design include drilling thrust force, acoustic emission, delamination, and surface roughness of the drilled holes. These data, which comprise the mean of the repetitive measurements were used as examples for training the artificial neural network.
The conclusion drawn was to show the efficiency of the neural network model that they employed, refer to Figure 5.5 and 5.6.
They did not show any relative analysis between the factors governing the experiment and the quality of the holes obtained.
Elanayar [11] used neural networks to monitor tool wear and surface roughness for automation. In his study, he extracted tool ware and surface finish data by using three components of force signals using neural networks. This is because cutting forces are related to the state of tool wear. He employed a three-layer back propagation neural network for the
27 monitoring of system conditions. The networks were first trained using the back propagation algorithm with a known set of measured data at the training stage [11].
Figure 5.5. Comparison between delamination experimental measurements and predictions made with fusion model [6].
Figure 5.6. Comparison between surface roughness experimental measurements and predictions made with fusion model [6].
Off-line measurements were taken for tool wear and surface finish at pre-determined
intervals. A hierarchical network architecture was chosen to represent the physical relation of
variables and reduce network sizes. After training was completed, the networks were exposed to
external stimuli, that is cutting forces, in order to extract process conditions. The results
demonstrated the feasibility of using neural networks to represent ill-defined relationships
between tool wear, surface finish, and cutting forces, refer to Figure 5.7. This work showed that
28 the ability to represent nonlinear relationships between patterns can be effectively used for monitoring purposes [11].
Figure5.7. Actual, Predicted Ra, Rz values [11].
Figure5.8. Evolution of Arithmetic mean roughness with cutting time [7].
Davim and Baptista [7], conducted experiments to measure the cutting force, tool wear and surface finish in metal matrix composites. They carried out the experiments with three different cutting speeds and four different feed rates. The measured average roughness value Ra varied between 0.25 and 1.25 µm [7]. As could be expected for geometrical reasons, the increase
29 of the feed determined the increase of Ra values, refer Figure 5.8. For the same feed, the increase of the cutting speed should diminish these values, as usually observed in machining operations. However, the present results did not agree with this assertion [7].
30 CHAPTER 6
EXPERIMENTAL ANALYSIS
Neuron Model
A Neuron with scalar input vector “p” and a scalar bias “b” is shown in Figure 6.1.
Inputs 1 st layer 2nd layer
P1
n (Output)
P2
P1(w1,1) ∑ f
n P2(w2,2) b
Figure 6.1. Schematic of a single neuron in a multilayered feed forward network.
The transfer function net input “n,” again a scalar given by equation (6.1), is the sum of the weighed input “p” and the bias “b.” This sum is the argument of the transfer function “f.”
P n = i=1 ∑ WipPi + b (6.1)
The weights “w” and “b” are both adjustable scalar parameters of the neuron. The central idea of neural networks is that such parameters can be adjusted so that the network exhibits some desired or interesting behavior. Thus, we can train the network to do a particular job by adjusting the weight or bias parameters, or perhaps the network itself will adjust these parameters to achieve some desired end.
31 Back Propagation Theory
This algorithm basically is designed to minimize E, the sum of squared errors between the estimated networks outputs (Oij) and the desired outputs (Tij) over the N exemplars in the training data set, each of them containing M outputs. The performance function of resilient back propagation algorithm is illustrated as
N M 2 E = 1/2( i=1 ∑ j=1 ∑ (Oij – Tij) ) (6.2)
Multilayered networks typically use sigmoid transfer functions in their hidden layers.
Sigmoid transfer functions are characterized by the fact that their slope must approach zero as the input increases. This causes a problem when using the steepest descent to train multilayered networks with sigmoid functions. This is because the gradient can have a very small magnitude and, therefore, cause small changes in weights and biases, even though the weights and biases are far from their optimal values.
6.1 Application of Artificial Neural Networks
Based on the DOE conducted for respective inputs that is seven speeds and six feed rates, and considering three replicates a total of 126 experimental runs were performed for SCFL material.
For the BMS 8-276 form 3 material, five different drill bits were used to conduct experiments and obtain surface finish data. For each drill bit, three speeds and three feed rates were considered. Three replicates were considered for each experimental set. After obtaining surface finish data from each replicate, the output data was subjected to both ANOVA and
Neural network analysis. Neural network analysis was conducted in two phases. The learning phase is where data is fed to the network for training purposes. The data set used for training included all cutting speed values and only certain feed rates as shown in the Table 6.1. The
32 learning data for BMS 8-276 form 3 material and five different drill bits is shown in Tables 6.2 to 6.6. The test phase is where the network is capable of generating an output with respect to the developed function. The test data was selected at all speed values and at 0.002, and 0.008 ipr as feed rates for SCFL material. For the BMS 8-276 form 3 material the test data was run at 0.006 ipr as feed rate. A set of output data obtained by conducting drilling experiments was compared to the output obtained from the network to achieve a RMS error value. The different configurations were tested until the RMS error value was minimum.
The analysis using the network was conducted using norm-cum-delta as the learning rule and sigmoid as the transfer function. The network characteristics used for both type of material is shown in Table 6.7. The network architecture for SCFL material and BMS 8-276 form 3 material are shown in Table 6.8 and 6.9 respectively. Data was fed to the network with the following configurations: single hidden layer with combinations of five nodes, that is single node through five nodes. Two hidden layers with combinations of five nodes, that is nodes one through five.
Figures A.1 and A.2 show the optimum combination networks are shown in Appendix A.
The number of runs conducted for the learning process totaled 200,000. The output data obtained after the test run was tabulated, and using the output test data the RMS error value was calculated. The optimum calculated values are depicted along with the RMS error values for different configurations in Table B.1 to B.7 in Appendix B.
The least RMS value for SCFL material was with a single hidden layer four node configuration.
For this optimum network the output for the entire set of values was determined and it is tabulated in the Table 6.10. Similarly for the different drill bits used the optimum network was one with two hidden layers and five nodes each. The entire output data set for the five different drill bits is shown in Tables 6.11 to 6.15.
33 TABLE 6.1
TRAINING DATA FOR ANN (SCFL)
SPEED FEED S.FINISH SPEED FEED S.FINISH 5000 0.001 1.1 3500 0.006 1.37 5000 0.001 0.96 3500 0.006 1.43 5000 0.001 1.04 3500 0.006 1.55 5000 0.004 1.32 3500 0.01 1.78 5000 0.004 1.29 3500 0.01 1.62 5000 0.004 1.38 3500 0.01 1.59 5000 0.006 1.46 3000 0.001 0.76 5000 0.006 1.56 3000 0.001 0.89 5000 0.006 1.6 3000 0.001 1.01 5000 0.01 1.72 3000 0.004 1.22 5000 0.01 1.8 3000 0.004 1.15 5000 0.01 1.68 3000 0.004 1.2 4500 0.001 0.98 3000 0.006 1.36 4500 0.001 0.7 3000 0.006 1.45 4500 0.001 1.06 3000 0.006 1.51 4500 0.004 1.32 3000 0.01 1.68 4500 0.004 1.19 3000 0.01 1.76 4500 0.004 1.45 3000 0.01 1.9 4500 0.006 1.49 2500 0.001 1.1 4500 0.006 1.57 2500 0.001 0.95 4500 0.006 1.51 2500 0.001 0.87 4500 0.01 1.59 2500 0.004 1.21 4500 0.01 1.71 2500 0.004 1.14 4500 0.01 1.85 2500 0.004 1.29 4000 0.001 1.03 2500 0.006 1.39 4000 0.001 0.87 2500 0.006 1.45 4000 0.001 1.12 2500 0.006 1.31 4000 0.004 1.24 2500 0.01 1.64 4000 0.004 1.29 2500 0.01 1.73 4000 0.004 1.36 2500 0.01 1.59 4000 0.006 1.42 2000 0.001 0.96 4000 0.006 1.5 2000 0.001 1.05 4000 0.006 1.58 2000 0.001 0.92 4000 0.01 1.77 2000 0.004 1.23 4000 0.01 1.89 2000 0.004 1.29 4000 0.01 2.01 2000 0.004 1.19 3500 0.001 0.94 2000 0.006 1.44 3500 0.001 1.02 2000 0.006 1.39 3500 0.001 0.86 2000 0.006 1.49 3500 0.004 1.13 2000 0.01 1.71 3500 0.004 1.24 2000 0.01 1.82 3500 0.004 1.19 2000 0.01 1.63
34 TABLE 6.2 TABLE 6.4
ANN INPUT FOR BRAD SPUR ANN INPUT FOR CONVENTIONAL
SPEED FEED S.FINISH SPEED FEED S.FINISH 5000 0.004 1.35 5000 0.004 0.7 5000 0.004 1.37 5000 0.004 0.72 5000 0.004 1.35 5000 0.004 0.73 5000 0.01 2.05 5000 0.01 1.2 5000 0.01 2.01 5000 0.01 1.22 5000 0.01 2.03 5000 0.01 1.23 3000 0.004 1.15 3000 0.004 0.59 3000 0.004 1.15 3000 0.004 0.6 3000 0.004 1.17 3000 0.004 0.62 3000 0.01 1.56 3000 0.01 1 3000 0.01 1.54 3000 0.01 0.99 3000 0.01 1.55 3000 0.01 0.99 2000 0.004 1.07 2000 0.004 0.56 2000 0.004 1.06 2000 0.004 0.57 2000 0.004 1.05 2000 0.004 0.58 2000 0.01 1.49 2000 0.01 0.91 2000 0.01 1.45 2000 0.01 0.95 2000 0.01 1.48 2000 0.01 0.96
TABLE 6.3 TABLE 6.5
ANN INPUT FOR DOUBLE MARGIN ANN INPUT FOR ST1257B
SPEED FEED S.FINISH SPEED FEED S.FINISH 5000 0.004 2.23 5000 0.004 1.99 5000 0.004 2.25 5000 0.004 1.99 5000 0.004 2.25 5000 0.004 1.98 5000 0.01 2.73 5000 0.01 2.95 5000 0.01 2.7 5000 0.01 2.96 5000 0.01 2.75 5000 0.01 2.98 3000 0.004 2 3000 0.004 1.72 3000 0.004 2.01 3000 0.004 1.72 3000 0.004 2.03 3000 0.004 1.75 3000 0.01 2.53 3000 0.01 2.35 3000 0.01 2.5 3000 0.01 2.36 3000 0.01 2.52 3000 0.01 2.35 2000 0.004 1.93 2000 0.004 1.68 2000 0.004 1.95 2000 0.004 1.69 2000 0.004 1.97 2000 0.004 1.65 2000 0.01 2.33 2000 0.01 2.05
2000 0.01 2.35 2000 0.01 2.01 2000 0.01 2.33 2000 0.01 2.02
35 TABLE 6.6
ANN INPUT FOR ST1255G
SPEED FEED S.FINISH 5000 0.004 1.15 5000 0.004 1.19 5000 0.004 1.2 5000 0.01 1.84 5000 0.01 1.88 5000 0.01 1.85 3000 0.004 1.05 3000 0.004 1.02 3000 0.004 0.98 3000 0.01 1.55 3000 0.01 1.55 3000 0.01 1.54 2000 0.004 0.95 2000 0.004 0.98 2000 0.004 0.96 2000 0.01 1.35 2000 0.01 1.36 2000 0.01 1.35
TABLE 6.7
NETWORK CHARACTERISTICS
Artificial Neural Network
Characteristics Value Paradigm Back Propagation Learn Function Norm-cum-Delta Transfer Function Sigmoid Code Binary Learning Cycle 200000
TABLE 6.8
NETWORK ARCHITECTURE FOR SCFL
Architecture
Layer Processing Elements No of Inputs 2 No of Hidden 1 (Nodes 4) No of Outputs 1
36 TABLE 6.9
NETWORK ARCHITECTURE FOR BMS 8-276 FORM 3
Architecture
Layer Processing Elements No of Inputs 2 No of Hidden 2 (Nodes 5) No of Outputs 1
TABLE 6.10
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR SCFL MATERIAL
Feed (inch/rev) Speed(rpm) 0.001 0.002 0.004 0.006 0.008 0.01 5000 1.030617 1.116811 1.290615 1.474671 1.649249 1.820094 4500 1.016725 1.101726 1.273175 1.456223 1.63311 1.804131 4000 1.003551 1.086215 1.256279 1.438014 1.616943 1.787775 3500 0.991108 1.070471 1.240016 1.420166 1.600985 1.771111 3000 0.979393 1.054718 1.224458 1.402793 1.585451 1.754237 2500 0.968393 1.03919 1.209658 1.385999 1.570503 1.737263 2000 0.958078 1.024099 1.195652 1.369872 1.556249 1.720307
TABLE 6.11
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR BRAD SPUR
Feed(inch/rev) Speed(rpm) 0.004 0.006 0.01 5000 1.415239 1.598431 1.943253 3000 1.12815 1.280777 1.637637 2000 1.019406 1.14388 1.465196
TABLE 6.12
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR DOUBLE MARGIN
Feed(inch/rev) Speed(rpm) 0.004 0.006 0.01 5000 2.254497 2.423081 2.718515 3000 2.019475 2.167738 2.496588 2000 1.928052 2.052708 2.362576
37 TABLE 6.13
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR CONVENTIONAL
Feed(inch/rev) Speed(rpm) 0.004 0.006 0.01 5000 0.739995 0.891971 1.177708 3000 0.599187 0.728379 1.027552 2000 0.543877 0.65496 0.93924
TABLE 6.14
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR ST1257B
Feed(inch/rev) Speed(rpm) 0.004 0.006 0.01 5000 2.092706 2.353373 2.841483 3000 1.697164 1.902937 2.376869 2000 1.555699 1.712011 2.110624
TABLE 6.15
SURFACE FINISH USING ARTIFICIAL NEURAL NETWORK FOR ST1255G
Feed(inch/rev) Speed(rpm) 0.004 0.006 0.01 5000 1.230692 1.430033 1.802249 3000 0.999933 1.165676 1.54826 2000 0.912598 1.048799 1.397568
After obtaining the output data from the optimum network these values were plotted in comparison to experimental surface finish values for different speeds. Figures 6.2 to 6.8 show this comparative plot for SCFL material at seven different speeds. Figures 6.9 to 6.11 show the comparative plot for brad spur drill bit. Figures 6.12 to 6.14 show the comparative plot for double margin drill bit. Figures 6.15 to 6.17 show the comparative plot for conventional drill bit.
Figures 6.18 to 6.20 show the comparative plot for ST1257B drill bit. Figures 6.21 to 6.23 show the comparative plot for ST1255G drill bit. All the plots clearly indicate the increase in surface finish value with increase in feed rate at a given cutting speed.
38 FEED VS SUR FINISH (5000 RPM)
95
85 A.N.N 75 Data 65 Exp 55
45 S.FINISH (MIC-INCH) . S.FINISH 35
25 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED (INCH /REV)
Figure 6.2. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
FEED VS SUR FINISH (4500 RPM)
95
85 A.N.N 75 Data 65 Exp 55
45 S.FINISH (MIC-INCH) . S.FINISH 35
25 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED (INCH /REV)
Figure 6.3. Neural Network Output (Feed Vs Surface Finish at 4,500 rpm)
39 FEED VS SUR FINISH (4000 RPM)
95
85 A.N.N 75 Data 65 Exp 55
45 S.FINISH (MIC-INCH) . S.FINISH 35
25 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED (INCH /REV)
Figure 6.4. Neural Network Output (Feed Vs Surface Finish at 4,000 rpm)
FEED VS SUR FINISH (3500 RPM)
95
85 A.N.N 75 Data 65 Exp 55
45 S.FINISH (MIC-INCH) . S.FINISH 35
25 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED (INCH /REV)
Figure 6.5. Neural Network Output (Feed Vs Surface Finish at 3,500 rpm)
40 FEED VS SUR FINISH (3000 RPM)
95
85 A.N.N 75 Data 65 Exp 55
45 S.FINISH (MIC-INCH) . S.FINISH 35
25 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED (INCH /REV)
Figure 6.6. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
FEED VS SUR FINISH (2500 RPM)
95
85 A.N.N 75 Data 65 Exp c 55
45 S.FINISH (MIC-INCH) . S.FINISH 35
25 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED (INCH /REV)
Figure 6.7. Neural Network Output (Feed Vs Surface Finish at 2,500 rpm)
41 FEED VS SUR FINISH (2000 RPM)
95
85 A.N.N 75 Data 65 Exp 55
45 S.FINISH (MIC-INCH) . S.FINISH 35
25 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED (INCH /REV)
Figure 6.8. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
Plots to show Surface finish trend of ANN Output for Brad spur
FEED VS SURFACE FINISH (@ 5000 RPM)
90 80 70 60 50 A.N.N Data 40 Exp 30 20 10 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE INCH/REV)
Figure 6.9. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
42 FEED VS SURFACE FINISH (@ 3000 RPM)
70
60
50
40 A.N.N Data 30 Exp
20
10 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE INCH/REV)
Figure 6.10. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
FEED VS SURFACE FINISH (@ 2000 RPM)
70
60
50
40 A.N.N Data 30 Exp
20
10 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE INCH/REV)
Figure 6.11. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
43 Plots to show Surface finish trend of ANN Output for Double margin
FEED VS SURFACE FINISH (@ 5000 RPM)
120 110 100 90 80 70 A.N.N Data 60 50 Exp 40 30 20
SUR FINISH (MIC-INCH)SUR . 10 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.12. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
FEED VS SURFACE FINISH (@ 3000 RPM)
110 100 90 80 70 60 A.N.N Data 50 Exp 40 30 20
SUR FINISH (MIC-INCH)SUR . 10 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.13. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
44 FEED VS SURFACE FINISH (@ 2000 RPM)
100 90 80 70 60 A.N.N Data 50 Exp 40 30 20
SUR FINISH (MIC-INCH)SUR . 10 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.14. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
Plots to show Surface finish trend of ANN Output for Conventional
FEED VS SURFACE FINISH (@ 5000 RPM)
60
50
40 A.N.N Data 30 Exp 20
10 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.15. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
45 FEED VS SURFACE FINISH (@ 3000 RPM)
45 40 35 30 25 A.N.N Data 20 Exp 15 10 5 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.16. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
FEED VS SURFACE FINISH (@ 2000 RPM)
45 40 35 30 25 A.N.N Data 20 Exp 15 10 5 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.17. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
46 Plots to show Surface finish trend of ANN Output for ST1257B
FEED VS SURFACE FINISH (@ 5000 RPM)
140 120 100 80 A.N.N Data 60 Exp 40 20 SUR FINISH (MIC-INCH) SURFINISH . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.18. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
FEED VS SURFACE FINISH (@ 3000 RPM)
100 90 80 70 60 A.N.N Data 50 Exp 40 30 20 SUR FINISH (MIC-INCH) SURFINISH . 10 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.19. Neural Network Output (Feed Vs Surface Finish at 3,000 rpm)
47 FEED VS SURFACE FINISH (@ 2000 RPM)
90 80 70 60 50 A.N.N Data 40 Exp 30 20 10 SUR FINISH (MIC-INCH) SURFINISH . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.20. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
Plots to show Surface finish trend of ANN Output for ST1255G
FEED VS SURFACE FINISH (@ 5000 RPM)
80 70 60 50 A.N.N Data 40 Exp 30 20 10 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.21. Neural Network Output (Feed Vs Surface Finish at 5,000 rpm)
48 FEED VS SURFACE FINISH (@ 3000 RPM)
70
60
50
40 A.N.N Data 30 Exp
20
10 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.22. Neural Network Output (Feed Vs Surface Finish at 3,000 Rpm)
FEED VS SURFACE FINISH (@ 2000 RPM)
60
50
40 A.N.N Data 30 Exp 20
10 SUR FINISH (MIC-INCH)SUR . 0 0 0.002 0.004 0.006 0.008 0.01 0.012 FEED RATE (INCH/REV)
Figure 6.23. Neural Network Output (Feed Vs Surface Finish at 2,000 rpm)
6.2 Design of Experiments
The Factorial Design
Factorial designs are widely used in experiments involving several factors where it is
49 necessary to study the joint effect of the factors on a response. However, there are several special cases of the general factorial design that are important because they are widely used in research work, and also because they form the basis of other designs of considerable practical value.
The most important of these special cases is that of k factors, which are at only two levels. These levels may be quantitative, such as two values of temperature, pressure, or time.
They may be qualitative, such as two machines, two operators, the “high” and “low” levels of a factor, or perhaps the presence and absence of a factor. A complete replicate of such a design requires 2 x 2 x … x 2 = 2 k observations and is called a 2 k factorial design.
Here we assume that (1) the factors are fixed, (2) the designs are completely randomized, and (3) the usual normality assumptions are satisfied.
The 2k design is particularly useful in the early stages of experimental work, where there are likely to be many factors to be investigated. It provides the smallest number of runs with which k factors can be studied in a complete factorial design. Because there are only two levels for each factor, we must assume that the response is approximately linear over the range of the factor levels chosen.
Before conducting the actual experiments the experiments were designed using Stat-ease
Software. Factorial method was employed to design the experiments with 2 input variables
(speed, feed-rate) and one output (surface finish value).Machining was carried out based on the run order and randomization obtained from the software.
ANOVA was done only to supplement the results obtained from Neural networks and not to actually observe the experimental data in detail. The DOE input is shown in Table 6.16. The
ANOVA output results for SCFL material are tabulated in Table 6.17.
50 TABLE 6.16
DESIGN OF EXPERIMENT INPUT DATA FOR SCFL MATERIAL
S. No Run order Speed rpm Feed ipr Surface Finish value µm 1 23 5000 .001 1.1 2 41 5000 .001 0.96 3 7 5000 .001 1.04 4 95 4500 .001 0.98 5 40 4500 .001 0.7 6 76 4500 .001 1.06 7 46 4000 .001 1.03 8 109 4000 .001 0.87 9 34 4000 .001 1.12 10 9 3500 .001 0.94 11 118 3500 .001 1.02 12 44 3500 .001 0.86 13 67 3000 .001 0.76 14 94 3000 .001 0.89 15 8 3000 .001 1.01 16 39 2500 .001 1.1 17 12 2500 .001 0.95 18 96 2500 .001 0.87 19 97 2000 .001 0.96 20 119 2000 .001 1.05 21 122 2000 .001 0.92 22 1 5000 .002 1.28 23 58 5000 .002 1.21 24 33 5000 .002 1.16 25 75 4500 .002 1.22 26 16 4500 .002 1.32 27 42 4500 .002 1.29 28 126 4000 .002 1.19 29 86 4000 .002 1.3 30 74 4000 .002 1.27 31 2 3500 .002 1.3 32 71 3500 .002 1.18 33 51 3500 .002 1.41 34 116 3000 .002 1.22 35 35 3000 .002 1.37 36 32 3000 .002 1.43 37 30 2500 .002 1.18 38 19 2500 .002 1.37 39 89 2500 .002 1.26 40 108 2000 .002 1.32 41 29 2000 .002 1.19 42 25 2000 .002 1.27 43 43 5000 .004 1.32
51 TABLE 6.16 (cont)
DESIGN OF EXPERIMENT INPUT DATA FOR SCFL MATERIAL
44 100 5000 .004 1.29 45 104 5000 .004 1.38 46 15 4500 .004 1.32 47 11 4500 .004 1.19 48 10 4500 .004 1.45 49 3 4000 .004 1.24 50 70 4000 .004 1.29 51 65 4000 .004 1.36 52 52 3500 .004 1.13
53 28 3500 .004 1.24 54 5 3500 .004 1.19
55 103 3000 .004 1.22 56 83 3000 .004 1.15 57 117 3000 .004 1.2 58 115 2500 .004 1.21 59 92 2500 .004 1.14 60 112 2500 .004 1.29 61 107 2000 .004 1.23 62 110 2000 .004 1.29 63 27 2000 .004 1.19 64 85 5000 .006 1.46 65 84 5000 .006 1.56 66 57 5000 .006 1.6 67 21 4500 .006 1.49 68 87 4500 .006 1.57 69 22 4500 .006 1.51 70 101 4000 .006 1.42 71 99 4000 .006 1.5 72 60 4000 .006 1.58 73 49 3500 .006 1.37 74 106 3500 .006 1.43 75 47 3500 .006 1.55 76 20 3000 .006 1.36 77 98 3000 .006 1.45 78 26 3000 .006 1.51 79 6 2500 .006 1.39
80 55 2500 .006 1.45 81 73 2500 .006 1.31 82 14 2000 .006 1.44 83 68 2000 .006 1.39 84 91 2000 .006 1.49
52 TABLE 6.16 (cont)
DESIGN OF EXPERIMENT INPUT DATA FOR SCFL MATERIAL
85 120 5000 .008 1.59 86 45 5000 .008 1.61 87 64 5000 .008 1.7 88 54 4500 .008 1.64 89 38 4500 .008 1.69 90 81 4500 .008 1.72 91 4 4000 .008 1.61 92 69 4000 .008 1.75 93 90 4000 .008 1.59
94 80 3500 .008 1.67 95 61 3500 .008 1.55
96 77 3500 .008 1.72 97 18 3000 .008 1.54 98 66 3000 .008 1.66 99 125 3000 .008 1.73 100 82 2500 .008 1.59 101 102 2500 .008 1.79 102 124 2500 .008 1.61 103 56 2000 .008 1.65 104 13 2000 .008 1.74 105 72 2000 .008 1.56 106 121 5000 .01 1.72 107 114 5000 .01 1.8 108 111 5000 .01 1.68 109 24 4500 .01 1.59 110 88 4500 .01 1.71 111 31 4500 .01 1.85 112 123 4000 .01 1.77 113 53 4000 .01 1.89 114 62 4000 .01 2.01 115 93 3500 .01 1.78 116 59 3500 .01 1.62 117 79 3500 .01 1.59 118 17 3000 .01 1.68
119 37 3000 .01 1.76 120 50 3000 .01 1.9
121 63 2500 .01 1.64 122 113 2500 .01 1.73 123 48 2500 .01 1.59 124 105 2000 .01 1.71 125 36 2000 .01 1.82 126 78 2000 .01 1.63
53 TABLE 6.17
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR SCFL MATERIAL
Feed (inch/rev) Speed(rpm) 0.001 0.002 0.004 0.006 0.008 0.01 5000 1.033333 1.216667 1.33 1.54 1.633333 1.73333 4500 0.913333 1.276667 1.32 1.523333 1.683333 1.716667 4000 1.006667 1.253333 1.296667 1.5 1.65 1.716667 3500 0.94 1.296667 1.186667 1.45 1.646667 1.663333 3000 0.886667 1.34 1.19 1.44 1.643333 1.78 2500 0.973333 1.27 1.213333 1.383333 1.663333 1.653333 2000 0.976667 1.26 1.236667 1.34 1.65 1.72
The response plot is as shown in Figure 6.24. Also the governing equation obtained by
ANOVA is in form of equation (6.3).
DESIGN-EXPERT Plot
S.FINISH .Ra X = A: SPEED Y = B: FEED
1.761
1.575
1.389
1.203
1.017 S.Finish (micro-mtr) (micro-mtr) S.Finish
0.010 5000 0.007 4250 0.005 3500
B: Feed (inch/rev)0.002 2750 A: Speed (RPM) 0.000 2000
Figure 6.24. Response Surface for DOE predicted Output for SCFL
54 Governing ANOVA equation for SFCL data is as follows:
-003 -003 S.F=1.44+0.029*A+0.34*B+9.048*e *A 2-0.060*B 2+4.033e *A*B (6.3)
The DOE input data for brad spur drill bit is shown in Table 6.18.
TABLE 6.18
DESIGN OF EXPERIMENT INPUT DATA FOR BRAD SPUR
Run Speed Feed S.Finish Order rpm inch/rev µm 1 5000 0.01 2.01 2 3000 0.004 1.15 3 3000 0.004 1.17 4 2000 0.004 1.06 5 2000 0.004 1.05 6 2000 0.004 1.07 7 5000 0.01 2.03 8 3000 0.006 1.35 9 5000 0.006 1.85 10 5000 0.004 1.35 11 3000 0.006 1.33 12 2000 0.01 1.45 13 3000 0.01 1.54 14 2000 0.006 1.25 15 3000 0.004 1.15 16 3000 0.01 1.56 17 3000 0.006 1.37 18 5000 0.006 1.85 19 2000 0.01 1.49 20 2000 0.006 1.23 21 5000 0.004 1.35 22 5000 0.01 2.05 23 3000 0.01 1.55 24 5000 0.004 1.37 25 2000 0.01 1.48 26 2000 0.006 1.26 27 5000 0.006 1.89
The output data from ANOVA for brad spur drill bit is tabulated in Table 6.19. The response plot for brad spur is as shown in Figure 6.25. Also the governing equation obtained by
ANOVA is in form of equation (6.4).
55 TABLE 6.19
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR BRAD SPUR
Speed rpm Feed rate 5000 3000 2000 inch/rev 0.004 1.356667 1.156667 1.06 0.006 1.863333 1.35 1.246667 0.01 2.03 1.55 1.473333
DESIGN-EXPERT Plot
surf ace roughness X = A: speed Y = B: Feed
2.051
1.799
1.547
1.294
1.042 S.Finish (mic-mtr) (mic-mtr) S.Finish
0.010 5000.00 0.009 4250.00 0.007 3500.00
B: Feed (inch/rev)0.006 2750.00 A: Speed (RPM) 0.004 2000.00
Figure 6.25. Response Surface for DOE predicted Output for Brad spur
Governing ANOVA equation for brad spur drill data is as follows:
S.F =1.56+0.25*A+0.25*B+0.080*A 2-0.15*B 2+0.057*A*B (6.4)
The DOE input data for double margin drill bit is shown in Table 6.20. The ANOVA output data is tabulated in Table 6.21. The response plot for double margin is as shown in Figure
6.26. Also the governing equation obtained by ANOVA is in form of equation (6.5).
56 TABLE 6.20
DESIGN OF EXPERIMENT INPUT DATA FOR DOUBLE MARGIN
Run Speed Feed S.Finish Order rpm inch/rev µm 1 5000 0.01 2.73 2 3000 0.004 2 3 3000 0.004 2.01 4 2000 0.004 1.93 5 2000 0.004 1.95 6 2000 0.004 1.97 7 5000 0.01 2.7 8 3000 0.006 2.27 9 5000 0.006 2.55 10 5000 0.004 2.23 11 3000 0.006 2.28 12 2000 0.01 2.33 13 3000 0.01 2.53 14 2000 0.006 2.13 15 3000 0.004 2.03 16 3000 0.01 2.5 17 3000 0.006 2.25 18 5000 0.006 2.55 19 2000 0.01 2.35 20 2000 0.006 2.15 21 5000 0.004 2.25 22 5000 0.01 2.75 23 3000 0.01 2.52 24 5000 0.004 2.25 25 2000 0.01 2.33 26 2000 0.006 2.14 27 5000 0.006 2.52
TABLE 6.21
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR DOUBLE MARGIN
Speed rpm Feed rate 5000 3000 2000 inch/rev 0.004 2.243333 2.013333 1.95 0.006 2.666667 2.266667 2.14 0.01 2.6 2.516667 2.336667
57 DESIGN-EXPERT Plot
surf ace roughness X = A: speed Y = B: Feed
2.746
2.540
2.334
2.127
1.921 S.Finish (mic-mtr) (mic-mtr) S.Finish
0.010 5000.00 0.009 4250.00 0.007 3500.00
B: Feed (inch/rev)0.006 2750.00 A: Speed (RPM) 0.004 2000.00
Figure 6.26. Response Surface for DOE predicted Output for Double margin
Governing ANOVA equation for double margin drill data is as follows:
S.F =2.42+0.18*A+0.23*B-0.11*B 2+0.016*A*B (6.5)
The DOE input data for conventional drill bit is shown in Table 6.22. The ANOVA output data is tabulated in Table 6.23. The response plot for conventional is as shown in Figure
6.27. Also the governing equation obtained by ANOVA is in form of equation (6.6).
The DOE input data for ST1257B drill bit is shown in Table 6.24. The ANOVA output data is tabulated in Table 6.25. The response plot for ST1257B is as shown in Figure 6.28. Also the governing equation obtained by ANOVA is in form of equation (6.7).
The DOE input data for ST1255G bit is shown in Table 6.26. The ANOVA output data is tabulated in Table 6.27. The response plot for ST1255G is as shown in Figure 6.29. Also the governing equation obtained by ANOVA is in form of equation (6.8).
58 TABLE 6.22
DESIGN OF EXPERIMENT INPUT DATA FOR CONVENTIONAL
Run Speed Feed S.Finish Order rpm inch/rev µm 1 5000 0.01 1.2 2 3000 0.004 0.59 3 3000 0.004 0.6 4 2000 0.004 0.56 5 2000 0.004 0.57 6 2000 0.004 0.58 7 5000 0.01 1.22 8 3000 0.006 0.75 9 5000 0.006 0.95 10 5000 0.004 0.7 11 3000 0.006 0.8 12 2000 0.01 0.91 13 3000 0.01 1 14 2000 0.006 0.78 15 3000 0.004 0.62 16 3000 0.01 0.99 17 3000 0.006 0.78 18 5000 0.006 0.9 19 2000 0.01 0.95 20 2000 0.006 0.81 21 5000 0.004 0.72 22 5000 0.01 1.23 23 3000 0.01 0.99 24 5000 0.004 0.73 25 2000 0.01 0.96 26 2000 0.006 0.8 27 5000 0.006 0.92
TABLE 6.23
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR CONVENTIONAL
Speed rpm Feed rate 5000 3000 2000 inch/rev 0.004 0.716667 0.603333 0.57 0.006 0.923333 0.776667 0.796667 0.01 1.216667 0.993333 0.94
59 DESIGN-EXPERT Plot
surf ace roughness X = A: speed Y = B: Feed
1.210
1.054
0.898
0.741
0.585 S.Finish (mic-mtr) (mic-mtr) S.Finish
0.010 5000.00 0.009 4250.00 0.007 3500.00
B: Feed (inch/rev)0.006 2750.00 A: Speed RPM 0.004 2000.00
Figure 6.27. Response Surface for DOE predicted Output for Conventional
Governing ANOVA equation for conventional drill data is as follows:
S.F =0.89+0.096*A+0.21*B+0.044*A 2-0.070*B 2+0.037*A*B (6.6)
DESIGN-EXPERT Plot
surf ace roughness X = A: speed Y = B: Feed
2.968
2.643
2.317
1.992
1.667 S.Finish (mic-mtr) (mic-mtr) S.Finish
0.010 5000.00 0.009 4250.00 0.007 3500.00
B: Feed (inch/rev)0.006 2750.00 A: Speed (RPM) 0.004 2000.00
Figure 6.28. Response Surface for DOE predicted Output for ST1257B
60 TABLE 6.24
DESIGN OF EXPERIMENT INPUT DATA FOR ST1257B
Run Speed Feed S.Finish Order rpm inch/rev µm 1 5000 0.01 2.95 2 3000 0.004 1.72 3 3000 0.004 1.72 4 2000 0.004 1.68 5 2000 0.004 1.69 6 2000 0.004 1.65 7 5000 0.01 2.96 8 3000 0.006 2.03 9 5000 0.006 2.39 10 5000 0.004 1.99 11 3000 0.006 2.01 12 2000 0.01 2.05 13 3000 0.01 2.35 14 2000 0.006 1.89 15 3000 0.004 1.75 16 3000 0.01 2.36 17 3000 0.006 1.99 18 5000 0.006 2.35 19 2000 0.01 2.01 20 2000 0.006 1.85 21 5000 0.004 1.99 22 5000 0.01 2.98 23 3000 0.01 2.35 24 5000 0.004 1.98 25 2000 0.01 2.02 26 2000 0.006 1.89 27 5000 0.006 2.37
TABLE 6.25
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR ST1257B
Speed rpm Feed rate 5000 3000 2000 inch/rev 0.004 1.986667 1.73 1.673333 0.006 2.37 2.01 1.876667 0.01 2.963333 2.353333 2.026667
61 Governing ANOVA equation for ST1257B drill data is as follows:
S.F =2.22+0.31*A+0.34*B+0.024*A 2-0.081*B 2+0.15*A*B (6.7)
TABLE 6.26
DESIGN OF EXPERIMENT INPUT DATA FOR ST1255G
Run Speed Feed S.Finish Order rpm inch/rev µm 1 5000 0.01 1.84 2 3000 0.004 1.05 3 3000 0.004 1.02 4 2000 0.004 0.95 5 2000 0.004 0.98 6 2000 0.004 0.96 7 5000 0.01 1.88 8 3000 0.006 1.28 9 5000 0.006 1.65 10 5000 0.004 1.15 11 3000 0.006 1.25 12 2000 0.01 1.35 13 3000 0.01 1.55 14 2000 0.006 1.15 15 3000 0.004 0.98 16 3000 0.01 1.55 17 3000 0.006 1.25 18 5000 0.006 1.64 19 2000 0.01 1.36 20 2000 0.006 1.12 21 5000 0.004 1.19 22 5000 0.01 1.85 23 3000 0.01 1.54 24 5000 0.004 1.2 25 2000 0.01 1.35 26 2000 0.006 1.15 27 5000 0.006 1.65
TABLE 6.27
TABULATION OF SURFACE FINISH OUTPUT USING DOE FOR ST1255G
Speed rpm Feed rate 5000 3000 2000 inch/rev 0.004 1.18 1.016667 0.963333 0.006 1.646667 1.26 1.14 0.01 1.856667 1.546667 1.353333
62 DESIGN-EXPERT Plot
surf ace roughness X = A: speed Y = B: Feed
1.889
1.648
1.406
1.164
0.922 S.Finish (mic-mtr) (mic-mtr) S.Finish
0.010 5000.00 0.009 4250.00 0.007 3500.00
B: Feed (inch/rev)0.006 2750.00 A: Speed (RPM) 0.004 2000.00
Figure 6.29. Response Surface for DOE predicted Output for ST1255G
Governing ANOVA equation for ST1255G drill data is as follows:
S.F =1.46+0.21*A+0.27*B+0.016*A 2-0.13*B 2+0.058*A*B (6.8)
6.3 Data Comparison
Based on the surface finish data obtained from five different drill bits for BMS 8-
276 form 3 material, the drill bit performance was compared. For each individual speed and feed
rate the experimental surface finish values for different drill bits were plotted as shown in
Figures 6.30 to 6.38. The drill bits based on their degree of performance are arranged as follows:
(1). Conventional (2). ST1255G (3). Brad spur (4). ST1257B (5). Double margin. This signified that for any given speed and feed rate, the conventional drill bit showed the best performance and the double margin drill bit had the worst. Also a three dimensional plot explaining the same phenomenon is shown in Figure 6.39. This comparison helped to understand the behavior of each drill bit for different speed and feed rate conditions for the BMS 8-276 form 3 material. The surface finish value comparison for different drill bits is done in chapter seven.
63
SURFACE FINISH @ 5000 RPM, 0.004 INCH/REV
100 90 80 70 60 50 5000 RPM, 40 0.004 inch/rev 30
SUR FINISH (MIC-MICH). SUR FINISH 20 10 0 BS DM CD ST1255GST1257B DRILL BITS
Figure 6.30. Surface finish values at 5,000 rpm and 0.004 inch/rev
SURFACE FINISH @ 5000 RPM, 0.006 INCH/REV
120
100
80
60 5000 RPM, 0.006 inch/rev 40
SUR FINISH (MIC-MICH). SURFINISH 20
0 BS DM CD ST1255GST1257B DRILL BITS
Figure 6.31. Surface finish values at 5,000 rpm and 0.006 inch/rev
64 SURFACE FINISH @ 5000 RPM, 0.01 INCH/REV
140
120
100
80 5000 RPM, 60 0.01 inch/rev
40 SUR FINISH (MIC-MICH). SUR FINISH 20
0 BS DM CD ST1255GST1257B DRILL BITS
Figure 6.32. Surface finish values at 5,000 rpm and 0.01 inch/rev
SURFACE FINISH @ 3000 RPM, 0.004 INCH/REV
90 80
70 60 50 3000 RPM, 40 0.004 inch/rev 30 20 SUR FINISH (MIC-MICH). SUR FINISH 10 0 BS DM CD ST1255G ST1257B DRILL BITS
Figure 6.33. Surface finish values at 3,000 rpm and 0.004 inch/rev
65 SURFACE FINISH @ 3000 RPM, 0.006 INCH/REV
100 90 80 70 60 50 3000 RPM, 40 0.006 inch/rev 30
SUR FINISH (MIC-MICH). SUR FINISH 20 10 0 BS DM CD ST1255GST1257B DRILL BITS
Figure 6.34. Surface finish values at 3,000 rpm and 0.006 inch/rev
SURFACE FINISH @ 3000 RPM, 0.01 INCH/REV
120
100
80
60 3000 RPM, 0.01 inch/rev 40
SUR FINISH (MIC-MICH). SUR FINISH 20
0 BS DM CD ST1255G ST1257B DRILL BITS
Figure 6.35. Surface finish values at 3,000 rpm and 0.01 inch/rev
66 SURFACE FINISH @ 2000 RPM, 0.004 INCH/REV
90 80 70
60 50
40 2000 RPM, 30 0.004 inch/rev 20 SUR FINISH (MIC-MICH). SUR FINISH 10
0 BS DM CD ST1255G ST1257B DRILL BITS
Figure 6.36. Surface finish values at 2,000 rpm and 0.004 inch/rev
SURFACE FINISH @ 2000 RPM, 0.006 INCH/REV
90
80 70
60
50 40 2000 RPM, 30 0.006 inch/rev 20 SUR FINISH (MIC-MICH). SUR FINISH 10
0 BS DM CD ST1255G ST1257B DRILL BITS
Figure 6.37. Surface finish values at 2,000 rpm and 0.006 inch/rev
67 SURFACE FINISH @ 2000 RPM, 0.01 INCH/REV
100 90 80 70 60 50 40 2000 RPM, 30 0.01 inch/rev
SUR FINISH (MIC-MICH). SURFINISH 20 10 0 BS DM CD ST1255G ST1257B DRILL BITS
Figure 6.38. Surface finish values at 2,000 rpm and 0.01 inch/rev
Comparison of Drill bit performance 120
100
80
60
40 SUR FINISH (MIC-INCH) FINISH SUR .
20
BS DM 0 CD 2 DRILL 2 0 ST1255G 2 0 0 3 0 0 BITS 3 0 0 0 0 ST1257B 3 0 0 0 R 5 0 0 5 0 0 R P 0 0 R 5 0 0 0 R P M 0 0 0 R P M R P M , 0 0 P 0 R M , 0 R P M , 0 . P M , 0 0 R P , 0 .0 M . . 1 P M ,0 0 0 0 0 i , . 0 p M , 0 . 0 1 6 r 0 0 4 , . 0 0 i i .0 0 p i p 0 1 4 6 r p r . 0 r 0 i ip 0 6 ip p r r 4 ip r r ip r
Figure 6.39. Comparison of Drill bits
68 CHAPTER 7
RESULTS AND DISCUSSION
The lowest and highest experimental surface finish values for SCFL material is tabulated in Table 7.1. Similarly the neural network data and DOE predicted data are in Table 7.2 and 7.3 respectively.
TABLE 7.1
EXPERIMENTAL DATA FOR SCFL MATERIAL
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 4500 0.001 0.7
Highest 4000 0.01 2.01
TABLE 7.2
NEURAL NETWORK DATA FOR SCFL MATERIAL
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 4500 0.001 0.95
Highest 4000 0.01 1.82
TABLE 7.3
DOE PREDICTED DATA FOR SCFL MATERIAL
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 4500 0.001 0.89
Highest 4000 0.01 1.78
69 The lowest and highest experimental surface finish values for brad spur drill bit used in drilling of BMS 8-276 form 3 material is tabulated in Table 7.4. Similarly the neural network data and DOE predicted data are in Table 7.5 and 7.6 respectively.
TABLE 7.4
EXPERIMENTAL DATA FOR BRAD SPUR
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 1.05
Highest 2000 0.01 2.05
TABLE 7.5
NEURAL NETWORK DATA FOR BRAD SPUR
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 1.47
Highest 2000 0.01 1.94
TABLE 7.6
DOE PREDICTED DATA FOR BRAD SPUR
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 1.06
Highest 2000 0.01 2.03
The lowest and highest experimental surface finish values for double margin drill bit used in drilling of BMS 8-276 form 3 material is tabulated in Table 7.7. Similarly the neural network data and DOE predicted data are in Table 7.8 and 7.9 respectively.
70 TABLE 7.7
EXPERIMENTAL DATA FOR DOUBLE MARGIN
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.001 1.93
Highest 2000 0.006 2.75
TABLE 7.8
NEURAL NETWORK DATA FOR DOUBLE MARGIN
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.001 1.93
Highest 2000 0.004 2.79
TABLE 7.9
DOE PREDICTED DATA FOR DOUBLE MARGIN
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.001 1.95
Highest 2000 0.06 2.67
The lowest and highest experimental surface finish values for conventional drill bit used in drilling of BMS 8-276 form 3 material is tabulated in Table 7.10. Similarly the neural network data and DOE predicted data are in Table 7.11 and 7.12 respectively.
The lowest and highest experimental surface finish values for ST1257B drill bit used in drilling of BMS 8-276 form 3 material is tabulated in Table 7.13. Similarly the neural network data and DOE predicted data are in Table 7.14 and 7.15 respectively.
71 TABLE 7.10
EXPERIMENTAL DATA FOR CONVENTIONAL
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 0.56
Highest 2000 0.01 1.23
TABLE 7.11
NEURAL NETWORK DATA FOR CONVENTIONAL
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 0.54
Highest 2000 0.01 1.18
TABLE 7.12
DOE PREDICTED DATA FOR CONVENTIONAL
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 0.57
Highest 2000 0.01 1.23
TABLE 7.13
EXPERIMENTAL DATA FOR ST1257B
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 1.65
Highest 2000 0.01 2.98
72 TABLE 7.14
NEURAL NETWORK DATA FOR ST1257B
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 1.56
Highest 2000 0.01 2.84
TABLE 7.15
DOE PREDICTED DATA FOR ST1257B
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 1.67
Highest 2000 0.01 2.96
TABLE 7.16
EXPERIMENTAL DATA FOR ST1255G
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 0.96
Highest 2000 0.01 1.88
TABLE 7.17
NEURAL NETWORK DATA FOR ST1255G
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 0.91
Highest 2000 0.01 1.80
73 TABLE 7.18
DOE PREDICTED DATA FOR ST1255G
Speed (rpm) Feed (ipr) Surface Finish (µm)
Lowest 5000 0.004 0.96
Highest 2000 0.01 1.86
The lowest and highest experimental surface finish values for ST1255G drill bit used in drilling of BMS 8-276 form 3 material is tabulated in Table 7.16. Similarly the neural network data and DOE predicted data are in Table 7.17 and 7.18 respectively.
It is evident by observing the experimental data that surface finish value increases with
feed rate at given cutting speed.
The behavior of surface finish with respect to different cutting speed values could not be
established.
The percentage error with which the optimum networks were able to predict surface
finish values is as follows,
Error %
A. SCFL material 4.1%
B. BMS 8-276 form-3
1. Brad spur drill bit 5.05 %
2. Double margin drill bit 3.45 %
3. Conventional drill bit 2.59 %
4. ST1257B 3.86 %
5. ST1255G 4.75 %
74 The Analysis of Variance was done to verify the behavior of the data. It is in agreement with the Neural Network output.
These results clearly state the relationship of Surface finish with feed rate at given cutting speed. Use of high speed (around 5,000 rpm) with a adequate low feed rate (0.004 ipr) could result in a specimen with reasonable surface finish with efficient drilling economics.
The pictorial representation of the surface of drilled specimens gives a better insight into
the results obtained. The magnified pictures of the drilled surface are shown in Appendix C.
From observation of these pictures it can be seen that surfaces have uniform surface irregularities
at low feed-rates while they show some unusual deformity at higher feed rates. These results
provide a precise platform for further experimental investigation.
75 CHAPTER 8
CONCLUSION
• Analysis of the experimental data clearly signifies that surface finish deteriorates with
increase in feed rate at a given cutting speed.
• A relationship of surface finish with respect to cutting speed could not be established.
• Other factors such as drill geometry, work-piece properties and machining conditions
definitely have influence on the hole quality, but no experimental investigation was done.
• The results obtained from Neural network analysis are in good agreement with the
experimental results.
• The ANOVA on experimental data supports the fact that feed rate is the significant factor
in the experiment.
• The network provides a platform for future experiments to be conducted.
76 CHAPTER 9
LIMITATIONS
• Input factors considered that affected the output (Surface finish value) were only speed
and feed.
• Other governing factors such as drill geometry, material properties and machine
inaccuracies were not considered.
• Unavailability of drill specifications and composite material properties narrowed the
overall analysis.
77 CHAPTER 10
FUTURE WORK
• Study other factors that affect surface finish other than feed-rate and speed.
• There are other factors that determine the overall hole quality, such as roundness, actual
diameter which also can be investigated.
• Different learning and activation functions can be tested for the same data to investigate
the network behavior.
• Relationship of hole quality with respect to cutting force and torque can be investigated.
• Experiments with different drill bits on the same material can be done and comparison
with the current experiments can be carried out.
78
LIST OF REFERENCES
79 LIST OF REFERENCES
[1] “Metal Cutting Tool Handbook ,” 1954, published by, Metal Cutting Tool Institute, pp3- 45, 3 rd Edition.
[2] Mel, Schwartz., 1992, “Composite Material Handbook,” 2 nd Edition.
[3] Brian, Lambert K., 1979, “Prediction of Force, Torque, and Burr Length in Drilling Titanium-Composite Materials,” SME Technical Paper , 3p.
[4] Chandrashekaran, V., Kapoor, S.G., Devor, R.E., November 1995, “A Mechanistic Approach to Predicting the Cutting Forces in Drilling: With Application to Fiber- Reinforced Composite Materials,” Journal of Engineering for Industry , 117 , pp. 559-570.
[5] Tosun, Gul., Muratoglu Mehtap., August 2004, “The drilling of AL/SICP Metal-Matrix Composites, Part II: Work piece Surface Integrity,” Journal of Composites Science and Technology , 64 , pp. 1413-1418.
[6] Enemuoh, Ugo E., El, Gizawy A., Okafor, Chukwujekwu A., 1999, “Neural Network Based Sensor Fusion for On-line Prediction of Delamination and Surface Roughness in Drilling AS4/PEER Composites,” SME Technical Paper , pp. 187.1 – 187.6.
[7] Davim, J.P., Monteiro, Baptista A., 2001, “Cutting Force, Tool Wear and Surface Finish in Drilling Metal Matrix Composites,” Proceedings of the Institution of Mechanical Engineers, Part E:Journal of Process Mechnical Engineering , 215 pp. 177-183.
[8] Tagliaferri, V., Caprino, G., Diterlizzi, A., 1990, “Effect of Drilling Parameters on the Finish and Mechanical Properties of GFRP Composites,” International Journal of Machine Tools & Manufacture , 30 , pp. 77-84.
[9] Caprino, G., Diterlizzi, A., Tagliaferri, V., May 1988, “Advancing with Composites,” International Journal of Machine Tools and Manufacturing , 28 , 4.
[10] Koboevic, Niksa., Miskovic, Ante., 2002, “Ways of Ensuring Hole Fabrication Quality During Drilling Process of Composite Materials,” CIM 2002 and High Speed Machining –8th International Conference on Production Engineering, Brijuni, Croatia , pp. 1073- 1080
[11] Sunil, Elanayar V.T., and Shin, Yung C., 1990 “Machining Condition Monitoring for Automation Using Neural Networks,” ASME Winter meeting, Dallas, Texas, 44 , pp.85- 100.
[12] Enemuoh, Ugo E., El-Gizawy, A., Shenf., Okafor, Chukwujekwu A., May 1998, “Machinability Characterization in Drilling Graphite Fiber Reinforced Composites,” Technical paper of the NAMRI XXVI of SME , pp.45 – 50.
80 [13] Vagish, Hoskote., Fall 1999, “Study the effects of drilling parameters on surface finish,”Thesis, Department of Mechanical Engineering, Wichita State University.
[14] Vivek, Moinikunta., Spring 1994, “Tool wear estimation using Neural Networks,” Thesis, Department of Industrial Engineering, Wichita State University.
[15] Okafor, Chukwujekwu, A., Marcus, M., Tipirneni, R., October 1991, “Multiple sensor integration via neural networks for estimating surface roughness and bore tolerance in circular end milling – Part 1: Time domain,” Journal of Condition Monitoring and Diagnostic Technology , 2, pp. 49-57.
[16] Vijay, Palani., Spring 2006, “Finite Element Simulation of 3D Drilling of Unidirectional Composite,” Thesis, Department of Mechanical Engineering, Wichita State University.
81
APPENDICES
82 APPENDIX A
OPTIMUM ARTIFICIAL NEURAL NETWORKS
Figure A.1. Neural network showing two hidden layers with five nodes each
Figure A.2. Neural network showing one hidden layer with four nodes each
83 APPENDIX B
OUTPUT DATA FROM DOE AND NEURAL NETWORK ANALYSIS
TABLE B.1
OUTPUT DATA FROM DOE AND NEURAL NETWORK ANALYSIS FOR SFCL
Speed (rpm) Feed (ipr) DOE Sur.Finish (µm) ANN Sur.Finish (µm) 5000 0.001 1.033333 1.030617 5000 0.002 1.216667 1.116811 5000 0.004 1.33 1.290615 5000 0.006 1.54 1.474671 5000 0.008 1.633333 1.649249 5000 0.01 1.73333 1.820094 4500 0.001 0.913333 1.016725 4500 0.002 1.276667 1.101726 4500 0.004 1.32 1.273175 4500 0.006 1.523333 1.456223 4500 0.008 1.683333 1.63311 4500 0.01 1.716667 1.804131 4000 0.001 1.006667 1.003551 4000 0.002 1.253333 1.086215 4000 0.004 1.296667 1.256279 4000 0.006 1.5 1.438014 4000 0.008 1.65 1.616943 4000 0.01 1.716667 1.787775 3500 0.001 0.94 0.991108 3500 0.002 1.296667 1.070471 3500 0.004 1.186667 1.240016 3500 0.006 1.45 1.420166 3500 0.008 1.646667 1.600985 3500 0.01 1.663333 1.771111 3000 0.001 0.886667 0.979393 3000 0.002 1.34 1.054718 3000 0.004 1.19 1.224458 3000 0.006 1.44 1.402793 3000 0.008 1.643333 1.585451 3000 0.01 1.78 1.754237 2500 0.001 0.973333 0.968393 2500 0.002 1.27 1.03919 2500 0.004 1.213333 1.209658 2500 0.006 1.383333 1.385999 2500 0.008 1.663333 1.570503 2500 0.01 1.653333 1.737263 2000 0.001 0.976667 0.958078
84 TABLE B.1 (cont)
OUTPUT DATA FROM DOE AND NEURAL NETWORK ANALYSIS FOR SFCL
2000 0.002 1.26 1.024099 2000 0.004 1.236667 1.195652 2000 0.006 1.34 1.369872 2000 0.008 1.65 1.556249 2000 0.01 1.72 1.720307
TABLE B.2
OUTPUT DATA FROM NEURAL NETWORK FOR SCFL WITH LEAST RMS ERROR VALUE (OUTPUT FOR 1 HIDDEN LAYER -4 NODES. RMS ERROR= 0.169401)
INPUT OUTPUT ERROR INPUT OUTPUT ERROR 1.28 1.116811 0.026631 1.67 1.600985 0.004763 1.21 1.116811 0.008684 1.55 1.600985 0.002599 1.16 1.116811 0.001865 1.72 1.600985 0.014165 1.59 1.649249 0.00351 1.22 1.054718 0.027318 1.61 1.649249 0.00154 1.37 1.054718 0.099403 1.7 1.649249 0.002576 1.43 1.054718 0.140837 1.22 1.101726 0.013989 1.54 1.585451 0.002066 1.32 1.101726 0.047644 1.66 1.585451 0.005558 1.29 1.101726 0.035447 1.73 1.585451 0.020894 1.64 1.63311 4.75E-05 1.18 1.03919 0.019827 1.69 1.63311 0.003236 1.37 1.03919 0.109435 1.72 1.63311 0.00755 1.26 1.03919 0.048757 1.19 1.086215 0.010771 1.59 1.570503 0.00038 1.3 1.086215 0.045704 1.79 1.570503 0.048179 1.27 1.086215 0.033777 1.61 1.570503 0.00156 1.61 1.616943 4.82E-05 1.32 1.024099 0.087557 1.75 1.616943 0.017704 1.19 1.024099 0.027523 1.59 1.616943 0.000726 1.27 1.024099 0.060467 1.3 1.070471 0.052684 1.65 1.556249 0.008789 1.18 1.070471 0.011997 1.74 1.556249 0.033764 1.41 1.070471 0.11528 1.56 1.556249 1.41E-05
TABLE B.3
OUTPUT DATA FROM NEURAL NETWORK FOR BRAD SPUR WITH LEAST RMS ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.169615)
INPUT OUTPUT ERROR 1.89 1.598431 0.085012 1.85 1.598431 0.063287 1.85 1.598431 0.063287 1.33 1.280777 0.002423
85 TABLE B.3 (cont)
OUTPUT DATA FROM NEURAL NETWORK FOR BRAD SPUR WITH LEAST RMS ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.169615)
1.35 1.280777 0.004792 1.37 1.280777 0.007961 1.23 1.14388 0.007417 1.26 1.14388 0.013484 1.25 1.14388 0.011261
TABLE B.4
OUTPUT DATA FROM NEURAL NETWORK FOR DOUBLE MARGIN WITH LEAST RMS ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.102468)
INPUT OUTPUT ERROR 2.55 2.423081 0.016108 2.55 2.423081 0.016108 2.52 2.423081 0.009393 2.27 2.167738 0.010458 2.28 2.167738 0.012603 2.25 2.167738 0.006767 2.13 2.052708 0.005974 2.15 2.052708 0.009466 2.14 2.052708 0.00762
TABLE B.5
OUTPUT DATA FROM NEURAL NETWORK FOR CONVENTIONAL WITH LEAST RMS ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.090178)
INPUT OUTPUT ERROR 0.95 0.891971 0.003367 0.9 0.891971 6.45E-05 0.92 0.891971 0.000786 0.75 0.728379 0.000467 0.8 0.728379 0.00513 0.78 0.728379 0.002665 0.78 0.65496 0.015635 0.81 0.65496 0.024037 0.8 0.65496 0.021037
86 TABLE B.6
OUTPUT DATA FROM NEURAL NETWORK FOR ST1257B WITH LEAST RMS ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.115093)
INPUT OUTPUT ERROR 2.39 2.353373 0.001342 2.35 2.353373 1.14E-05 2.37 2.353373 0.000276 2.03 1.902937 0.016145 2.01 1.902937 0.011462 1.99 1.902937 0.00758 1.89 1.712011 0.03168 1.85 1.712011 0.019041 1.89 1.712011 0.03168
TABLE B.7
OUTPUT DATA FROM NEURAL NETWORK FOR ST1255G WITH LEAST RMS ERROR VALUE (OUTPUT FOR 2 HIDDEN LAYERS -5 NODES. RMS ERROR= 0.146705)
INPUT OUTPUT ERROR 1.65 1.430033 0.048385 1.64 1.430033 0.044086 1.65 1.430033 0.048385 1.28 1.165676 0.01307 1.25 1.165676 0.007111 1.25 1.165676 0.007111 1.15 1.048799 0.010242 1.12 1.048799 0.00507 1.15 1.048799 0.010242
87 APPENDIX C
PICTORIAL REPRESENTATION OF DRILLED SURFACES
Drilled Surface Picture of SCFL material
Figure C.1. 5,000 rpm, 0.001 ipr, Figure C.2. 5,000 rpm, 0.01 ipr, Surface Finish : 1.1 µm Surface Finish : 1.72 µm
Figure C.3. 2,500 rpm, 0.002 ipr, Figure C.4. 2,500 rpm, 0.008 ipr, Surface Finish : 1.18 µm Surface Finish : 1.59 µm
Drilled Surface Pictures of Conventional Drill Bit
Figure C.5. 3,000 rpm, 0.004 ipr, Figure C.6. 3,000 rpm, 0.01 ipr, Surface Finish : 0.59 µm Surface Finish : 0.99 µm
88 Drilled Surface Pictures of Brad spur Drill Bit
Figure C.7. 2,000 rpm, 0.004 ipr, Figure C.8. 2,000 rpm, 0.01 ipr, Surface Finish : 1.05 µm Surface Finish : 1.49 µm
Drilled Surface Pictures of Double Margin Drill Bit
Figure C.9. 3,000 rpm, 0.004 ipr, Figure C.10. 3,000 rpm, 0.01 ipr, Surface Finish : 2.00 µm Surface Finish : 2.53 µm
Drilled Surface Pictures of ST1257B Drill Bit
Figure C.11. 2,000 rpm, 0.004 ipr, Figure C.12. 2,000 rpm, 0.01 ipr, Surface Finish : 1.65 µm Surface Finish : 2.05 µm
89 Drilled Surface Pictures of ST1255G Drill Bit
Figure C.13. 3,000 rpm, 0.004 ipr, Figure C.14. 3,000 rpm, 0.01 ipr, Surface Finish : 0.98 µm Surface Finish : 1.55 µm
Magnified Pictures Showing Fiber Pullout and Zone Damage
Figure C.15. Fiber pullout
Figure C.16. Damaged zone
90
Figure C.17. Fiber pullout
Figure C.18. Magnified damage zone
91