Advanced automation techniques in adaptive material processing

Xiaoqi Chen, Rajagopalan Devanathan, Aik Meng Fong9810249020, 9789810249021

This volume presents the editors’ research as well as related recent findings on the applications of modern technologies in electrical and electronic engineering to the automation of some of the common manufacturing processes that have traditionally been handled within the mechanical and material engineering disciplines.
In particular, the book includes the latest research results achieved through applied research and development projects over the past few years at the Gintic Institute of Manufacturing Technology, Singapore. It discusses advanced automation technologies such as in-process sensors, laser vision systems, and laser strobe vision, as well as advanced techniques such as sensory signal processing, adaptive process control, fuzzy logic, neural networks, expert systems, laser processing control, etc. The methodologies and techniques are applied to some important material processing applications, including grinding, polishing, machining, and welding. Practical automation solutions, which are complicated by part distortions, tool wear, process dynamics, and variants, are explained.
The research efforts featured in the book are driven by industrial needs. They combine theoretical research with practical automation considerations. The techniques developed have been either implemented in the factory or prototyped in the laboratory.

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Table of contents :
Contents……Page 10
Preface……Page 6
1. Constrained and Non-Constrained Material Processing……Page 19
2. Multi-Facet Mechatronic Automation……Page 20
3.1 Measurands in Material Processing……Page 22
3.2 Types of Sensors……Page 25
3.3 Microsensors and Soft Sensors……Page 26
4.1 Conventional Computer Numerical Control……Page 28
4.2 Sensor Based Machine Tool Control……Page 30
4.3 Open Architecture and Distributed Control……Page 31
4.4 Intelligent Control and Computing Techniques……Page 33
4.5 Human-Machine Interface……Page 34
References……Page 35
1. Introduction……Page 37
2.1 Superalloy Components and Manual Blending……Page 39
2.2 CNC Milling……Page 43
2.3 Wheel Grinding……Page 45
3. Force Control in Material Removal……Page 47
3.1 Robot Holding Tool……Page 48
3.2 Robot Holding Workpiece……Page 49
4. Model-Based Robotic Machining……Page 53
5. Part Variations and Process Dynamics……Page 55
6.1 A Mechatronic Approach……Page 58
6.2 Device and Process……Page 59
6.3 Knowledge-Based Process Control (KBPC)……Page 60
6.5 System Layout and Working Cycle……Page 61
7.1 Grinding/Polishing Process Parameters……Page 63
7.2 Tool Path Optimisation……Page 64
7.3 Tool Wear Compensation……Page 67
8. Concluding Remarks……Page 69
References……Page 70
1. Introduction……Page 73
2.2 Self-Aligned End Effector……Page 75
2.3 Control Interface……Page 77
3.1 Off-Line versus In-Situ Approach……Page 78
3.2 Sensor Techniques……Page 79
3.3 Coordinate Transform……Page 82
4.1 Template Generation……Page 83
4.2 Profile Fitting Requirements……Page 85
4.3 A Fast Converging Minimisation Algorithm……Page 88
4.4 Software Development……Page 91
5.1 Definition of Tool Path……Page 94
5.2 Derivation of End-Effector Orientation……Page 95
5.3 Generation of Tool Path……Page 97
6. Implementation of SMART 3D Blending System……Page 99
7.1 Dimension of Finish Profile……Page 101
7.2 Surface Roughness and Finish Quality……Page 103
7.3 Wall Thickness……Page 105
8. Concluding Remarks……Page 106
References……Page 107
1. Introduction……Page 109
2. Sensors in Machining Process Monitoring……Page 111
2.1 Motor Current & Power……Page 112
2.2 Force/Torque……Page 113
2.3 Vibration/Acceleration Signals……Page 115
2.4 Optical and Vision System……Page 116
3. Acoustic Emission Sensing……Page 117
3.1 Acoustic Emission Mechanism……Page 118
3.2 Acoustic Emission in Machining……Page 119
3.3 Acoustic Emission Sensors……Page 122
4. Advanced Signal Processing Techniques……Page 125
4.1 Time Domain Analysis……Page 127
4.2 Time Series Modelling……Page 130
4.3 Frequency Domain Analysis……Page 131
4.4 Time-Frequency Domain Analysis……Page 132
4.5 Wavelet Analysis……Page 134
5. Conclusions……Page 139
References……Page 140
1.2 What is Welding?……Page 143
1.4 Need for Seam Tracking……Page 145
2.1 Survey of Existing Methods……Page 146
2.2 System Overview……Page 150
2.3 Data Acquisition……Page 151
2.4 Signal Processing……Page 153
2.5 Robotic Welding System……Page 154
2.6.1 The Algorithm……Page 156
2.7 Plant Identification and Control……Page 157
3.1 The Technology……Page 161
3.1.2 Measurement Range and Accuracy……Page 163
3.2 Vision Based Seam Tracking Systems……Page 164
3.2.1 System Architecture……Page 165
3.2.4 Laser 3D Vision Sensor……Page 166
3.2.6 Laser Camera……Page 167
3.2.7 Fanuc’s MIG EYE……Page 168
3.3.1 Hardware-System Layout……Page 169
3.3.2 Sensor Hardware……Page 170
3.3.3 Mounting of Sensor Head……Page 171
3.3.4 Data Acquisition System……Page 173
3.3.5 Software Overview……Page 174
3.4.1 Structured Light Approach……Page 175
3.4.2 Capturing the Image……Page 176
3.4.3 Image Preprocessing……Page 177
3.4.5 Blobs Tool Control……Page 178
3.5 Seam Detection Algorithms……Page 179
3.6.1 Evaluating Accuracy of Seam Tracking……Page 180
4. Concluding Remarks……Page 182
References……Page 183
1. Introduction……Page 185
2.1.2 Theoretical Models……Page 187
2.1.3 Pool Oscillation Detection……Page 189
2.1.5 Disadvantages……Page 190
2.2.1 Contact Transducer……Page 191
2.2.2 Laser Array and EMAT Ultrasonic Measurement……Page 192
2.3.1 Theoretical Foundations……Page 195
2.3.3 Results……Page 196
2.4.2 Sensing Technology……Page 198
2.4.3 Image Processing……Page 199
2.4.5 Disadvantages……Page 200
3.1 Controlled Welding Process……Page 201
3.3 Set-Up of Weld Pool Vision and Control System……Page 202
4.2 Edge Detection……Page 204
4.3 Connectivity Analysis……Page 205
4.4 Relationship between Weld Pool Dimensions and Welding Parameters……Page 206
5.1 Fuzzy Logic Control……Page 207
5.2 Neurofuzzy Logic System……Page 208
5.3 System Optimisation and Integration……Page 211
6.2 Closed-Loop Control of Welding Speed……Page 212
References……Page 215
1. Introduction – The Automatic Welding of High Performance Alloys……Page 219
2.1 GTAW for Titanium……Page 220
2.2 Features of an Intelligent Welding System……Page 222
2.3 Subsystems and Components……Page 224
3.1 Selection of Axes……Page 225
3.2 The Experimental and Demonstration Welding Manipulator……Page 227
3.3.1 Assigning Coordinate Frames……Page 228
3.3.2 Kinematic Simulation……Page 231
3.3.3 Inverse Kinematics……Page 234
3.3.4 Trajectory by Decomposition of Tool Transformation Matrix……Page 235
4. Process Control……Page 237
4.1 Critical Process Parameters……Page 238
4.3 Other Procedures……Page 240
4.4 Sensing and Monitoring……Page 241
4.6 Use of AI in Automatic Welding……Page 243
5. CNC and Low-Level Control……Page 245
6.1 Visual Monitoring……Page 247
6.2.1 Modelling of Camera Views……Page 249
6.2.2 Modelling of Solid Objects……Page 251
6.2.3 Implementation in Simulation Software……Page 252
6.2.4 Integration of Models and Welding Workpiece Images……Page 255
7. Conclusions……Page 257
References……Page 258
1.1 Laser Equipmentm……Page 261
1.2 Applications of Laser Material Processing……Page 263
1.3 Automation of Laser Material Processing……Page 264
1.3.1 In-Process Monitoring……Page 265
1.3.2 In-Process Control……Page 267
2. Survey of Real-Time Laser Welding Quality Monitoring……Page 268
2.1 Acoustic Emission……Page 269
2.2 Audible Sound……Page 270
2.3 Infrared Sensing……Page 271
2.4 Ultraviolet Sensing……Page 272
2.4.2 Ultraviolet Signal Analysis……Page 273
3. Analysis of Optical and Acoustic Signals Emitted from Plasma and Sensor Design……Page 275
3.1 Laser-Induced Plasma During Welding of Thin Metal Sheets……Page 276
3.2 Optical Emission from Laser-Induced Plasma……Page 278
3.3 Waves in Plasma……Page 279
3.4 Design of Optical and Acoustic Sensors……Page 281
4. Signal Processing through FFT and Wavelet Analysis……Page 282
4.1.1 Frequency Characteristics of Optical and Acoustic Signals Using Magnetically Restrained Discharge Laser……Page 283
4.1.2 Frequency Characteristics of Optical and Acoustic Signals of Differnet Defects……Page 285
4.2 Wavelet Analysis of Audible Acoustic Emission Signals……Page 293
4.2.1 Wavelet Decomposition of AE Signals……Page 294
4.2.2 Results of Wavelet Analysis of AE Signal……Page 296
4.3.1 Definition of Detection Curve……Page 298
4.3.2 Example……Page 299
5.1.1 Construction of Features……Page 303
5.1.2 Improvement of Features……Page 305
5.1.3 BP Network Parameters and Effect of Different Features……Page 306
5.2 Performance of the Neural Network……Page 309
6. Conclusions……Page 311
References……Page 312
INDEX……Page 315