Interdisciplinary Treatment to Arc Welding Power Sources (eBook)

Dr. S. Arungalai Vendan is an associate professor at the Industrial Automation and Instrumentation Division, VIT University, Vellore, India. He has been working on advanced welding processes since 2006. He received his Ph.D. degree from the National Institute of Technology (Institute of national importance), Tiruchirappalli, India in 2010. He has received several fellowships and awards for his technical contributions by various government agencies. He has successfully completed government funded research projects and industrial consultancy projects, and has published more than 70 research papers in international journal and conference proceedings.  He has associations with top manufacturing industries and Research and Development centers under various capacities. His research interests mainly focus on the interdisciplinary science which has confluence of terminologies from electrical/mechanical/metallurgical/ materials and magnetic technologies.Prof. Liang Gao received his Ph.D. degree in Mechatronic Engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2002. He is currently a professor at the Department of Industrial and Manufacturing System Engineering, School of Mechanical Science and Engineering, HUST and the vice director of the State Key Lab of Digital Manufacturing Equipment & Technology. His chief research interests include optimization in design and manufacturing, and he has published more than 150 academic papers,. He is currently an associate editor for Swarm and Evolutionary Computation and the Journal of Industrial and Production Engineering, and an editorial board member of the European Journal of Industrial Engineering and Operations Research Perspectives.Dr. Akhil Garg is an associate professor at the Ministry of Education’s Intelligent Manufacturing Key Laboratory, Shantou University, China. He has been working on sustainable manufacturing processes and optimization methods since 2011. He received his doctoral degree from Nanyang Technological University (NTU), Singapore in 2014. He has published over 50 SCI-indexed articles in the areas of manufacturing and optimization.Dr. P. Kavitha is an associate professor at the School of Electrical Engineering, VIT University, Vellore, India. Her research interests include control systems, analog and digital circuits, advanced control theory, process automation and process control.Dr. G. Dhivyasri is an assistant professor at the School of Electrical Engineering, VIT University, Vellore, India. Dr. Dhivyasri’s research interests include, control system, MEMS, sensors and signal conditioning, as well as analog & digital communication systems.Dr. Rahul SG is an assistant professor at the School of Electrical Engineering, VIT University, Vellore, India. His research interests include control systems, industrial instrumentation, analytical instrumentation, programmable logic controller (PLC), and digital electronics.  

Contents 5
1 Welding an Overview 11
1.1 Basics of Arc Welding 12
1.1.1 Electric Charge 13
1.1.2 Electric Current 13
1.1.3 Voltage 13
1.1.4 Electrical Resistance 14
1.1.5 Heat Input 14
1.1.6 Power 15
1.2 Equivalent Electrical Analogous Representation of Arc Welding 15
1.3 Arc Welding as a Confluence of Several Disciplines 16
1.4 Applications of Arc Welding 16
References 24
2 Insight into Arc Welding Power Source Terminologies 25
2.1 Critical Arc Power Source Terminologies for Welding 25
2.1.1 Arc Plasma 25
2.1.2 Arc Efficiency 26
2.1.3 Arc Stability 26
2.1.4 Arc Blow 27
2.1.5 Pinch Effect 27
2.1.6 Arc Shielding 27
2.2 Influence of Power Source Parameters on Weldment 28
2.2.1 Open-Circuit Voltage (OCV) 28
2.2.2 Arc Voltage 29
2.2.3 Welding Current 30
2.2.4 Electrode Polarity 31
2.2.5 Power Factor 31
2.2.6 Duty Cycle and Current Rating 32
2.2.7 Class of Insulation 32
2.3 Impact of Power Source Characteristics on Weldments 33
2.3.1 Static Characteristics 33
2.3.2 Dynamic Characteristics 35
2.4 Classification of Arc Welding Power Sources 35
2.4.1 Static Types 35
2.4.2 Rotating Types 39
2.5 Power Sources Components Briefing 41
2.5.1 Diode 41
2.5.2 BJT 42
2.5.3 MOSFET 44
2.5.4 Insulated Gate Bipolar Transistor (IGBT) 46
2.5.5 Silicon-Controlled Rectifier (SCR) 46
2.5.6 Pulse Width Modulators (PWM) 48
2.5.7 Microprocessor 49
2.5.8 Microcontroller 49
2.5.9 Field-Programmable Gate Arrays (FPGAs) 49
2.6 Evolution of Arc Welding Power Sources 50
2.7 Switch-Based Techniques Adopted for Welding Power Sources 53
2.8 Literature Addressing Power Source Parameters 71
References 77
3 Control Terminologies and Schemes for Arc Welding Processes 81
3.1 Control System Terminologies 81
3.1.1 Process 81
3.1.2 System 82
3.1.3 Control System 82
3.1.4 Parameters/Variables 82
3.1.5 Control 83
3.1.6 Disturbances 83
3.1.7 Setpoint 83
3.1.8 Feedback 83
3.1.9 Error 84
3.1.10 Transfer Function 84
3.1.11 Open Loop System 84
3.1.12 Closed-Loop System 85
3.2 Control System Analysis 86
3.2.1 Order of the System 86
3.2.2 Zeroth Order System 86
3.2.3 First-Order System 87
3.2.4 Second-Order System 87
3.2.5 Linearity 88
3.2.6 Sensitivity 88
3.3 Introduction to Fundamental Controllers 88
3.4 Stability Analysis 89
3.5 Significance of Control System 90
3.6 Control System for Arc Welding 90
3.6.1 Sensing System 91
3.6.2 Control Strategy and Algorithms 91
3.6.3 Desired Gating Signals 92
3.7 Controller Schemes Adopted for Welding Power Sources 92
3.8 Process Parametric Influences on Weld Quality 94
3.9 Real-Time Sample Reports on Formulating Adaptive Control Scheme for Cold Metal Transfer for JoiningAA6061 129
3.9.1 Objective 129
3.9.2 Implementation 129
3.9.3 Controller Results 131
3.9.4 MRAC Controller Response 132
References 135
4 Power Sources and Challenges for Different Arc Welding Processes 137
4.1 Power Sources in Manual Metal Arc Welding (MMA) 137
4.2 Power Sources in Shielded Metal Arc Welding (SMAC) 137
4.3 Power Sources in Gas Tungsten Arc Welding (GTAW)/Tungsten Inert Gas Arc Welding (TIG) 138
4.4 Power Sources in Gas Metal Arc Welding/Metal Inert Gas Welding (GMAW/MIG) 139
4.5 Power Sources in Submerged Arc Welding (SAW) 140
4.6 Major Challenges in Power Sources 140
4.6.1 Harmonics 140
4.6.2 Effects of Magnetic Field in Arc Welding 142
4.6.3 Protection of Power Sources 144
4.6.4 Cooling System 144
References 147
5 Sensors for Welding Data Acquisition 149
5.1 Data Acquisition System 149
5.1.1 What Are Sensors and Transducers? 150
5.1.2 Signals 151
5.1.3 What Is a DAQ Hardware? 154
5.2 Physical Principles of Sensing 154
5.2.1 Characteristics of Different Sensor Types 155
5.2.2 Basic Terminologies 156
5.2.3 Choosing a Sensor 156
5.3 Key Measurement Components of a DAQ Device 157
5.3.1 Signal Conditioning 157
5.3.2 Analog-to-Digital Converter (ADC) 158
5.3.3 Computer Bus 158
5.4 Role of Computer in a DAQ System 158
5.4.1 Application Software 158
5.4.2 Driver Software 161
5.5 Data Acquisition in Arc Welding Processes 161
5.5.1 Measuring Current and Voltage 161
5.5.2 Wire Feed Speed 163
5.5.3 Shielding Gas Flow 165
5.5.4 Temperature 165
5.5.5 Sensors for Geometrical Parameters 167
5.5.6 Arc Sensors 170
5.5.7 Typical Sensors and Their Outputs 171
5.6 Parameters of Arc Welding Sensors for Various Applications 171
5.7 Data Acquisition Using LabVIEW 171
5.7.1 Physical Input/Output Signals 173
5.7.2 DAQ Device/Hardware 173
5.7.3 Driver Software 173
5.7.4 Application Software 174
5.7.5 Measurement and Automation Explorer 174
5.7.6 DAQ Assistant 175
5.8 Case Study 1: Measurement of Temperature During Joining of 316L Stainless Steel by CMT Process 176
5.8.1 Process Details 176
5.8.2 Description of DAQ Unit 177
5.8.3 Experimental Data 178
5.8.4 Temperature Plots 178
5.9 Case Study 2: Characterization of Gas Metal Arc Welding System Using DAQ 179
5.9.1 Description 179
5.9.2 Welding Procedure 180
5.10 Results 182
References 189
6 Optimization in Arc Welding Process 190
6.1 Introduction to Optimization 190
6.1.1 Constructing a Model 190
6.1.2 System Identification in Arc Welding 191
6.2 Significance of Optimization in Welding 191
6.3 ANN-Based Optimization Techniques to Arc Welding Processes 192
6.3.1 Introduction to ANN 192
6.3.2 Backpropagation Neural Network (BP-NN) 195
6.4 Development of PSO-Based Backpropagation Neural Network 197
6.4.1 Particle Swarm Optimization 197
6.4.2 Development of BP-NN Using PSO Algorithm 198
6.5 Development of Levenberg–Marquardt (LM) Algorithm-Based Backpropagation Neural Network 201
6.5.1 Introduction to LM Algorithm 201
6.5.2 Computing the Jacobian Matrix 201
6.5.3 Steps in Levenberg–Marquardt Algorithm 202
6.6 Genetic Algorithm for Tuning the Neural Network 203
6.7 Case Study 1: Optimization of Flux Cored Arc Welding Parameters Using GA 205
6.7.1 Objective 205
6.7.2 Experimentation 205
6.7.3 Optimization 207
6.8 Case Study 2: Optimization and Prediction of Hardness and Shear Strength Using PSO Based ANN in FSW of AA6061 Alloys 209
6.8.1 Objective 209
6.8.2 Experimentation 209
6.8.3 Implementation 210
6.9 Case Study 3: LM Algorithm-Based ANN Model to Predict Strength and Joint Resistance of Al-Cu Alloys Joined by Ultrasonic Welding Process 212
6.9.1 Objective 212
6.9.2 Experimentation 213
6.9.3 Implementation 213
References 216
7 Codes and Safety Standards During Welding 217
7.1 Risk Management Process 217
7.1.1 Identifying the Potential Hazards 217
7.1.2 Assessment of Risk 218
7.1.3 Risk Control 218
7.2 Specific Hazards and Control Measures 219
7.2.1 Airborne Contaminants 219
7.2.2 Radiation 220
7.2.3 Electrical Risks 221
7.2.4 Risks Due to Electromagnetic Fields 223
7.2.5 Exposure to Heat and Burns 223
7.2.6 Compressed and Liquefied Gases 224
7.2.7 Personal Protective Equipment (PPE) 224
7.2.8 Health Monitoring 224
7.3 Standard Operating Procedures During Arc Welding 228
7.3.1 Engine Power Equipment 228
7.3.2 In Presence of Electric and Magnetic Fields 229
7.3.3 During Handling Cylinders 230
7.3.4 While Handling Shielding Gases 230
7.4 Welding Codes: American Welding Society (AWS) 230
7.5 Quality Assurance and Quality Management 232
7.5.1 En ISO 15609 232
7.5.2 En ISO 15614-1 232
7.5.3 EN ISO 15614-2 234
7.5.4 EN ISO 15610 234
7.5.5 EN ISO 5817 and ISO 10042 235
References 237