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Intelligentized Methodology for Arc Welding Dynamical Processes: Visual Information Acquiring, Knowledge Modeling and Intelligent Control 2009 ed. [Kõva köide]

  • Formaat: Hardback, 278 pages, kõrgus x laius: 235x155 mm, kaal: 623 g, 502 Illustrations, black and white; XXIV, 278 p. 502 illus., 1 Hardback
  • Sari: Lecture Notes in Electrical Engineering 29
  • Ilmumisaeg: 04-Nov-2008
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540856412
  • ISBN-13: 9783540856412
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  • Formaat: Hardback, 278 pages, kõrgus x laius: 235x155 mm, kaal: 623 g, 502 Illustrations, black and white; XXIV, 278 p. 502 illus., 1 Hardback
  • Sari: Lecture Notes in Electrical Engineering 29
  • Ilmumisaeg: 04-Nov-2008
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540856412
  • ISBN-13: 9783540856412
Teised raamatud teemal:
Welding handicraft is one of the most primordial and traditional technics, mainly by manpower and human experiences. Weld quality and ef ciency are, therefore, straitly limited by the welders skill. In the modern manufacturing, automatic and robotic welding is becoming an inevitable trend. However, it is dif cult for au- matic and robotic welding to reach high quality due to the complexity, uncertainty and disturbance during welding process, especially for arc welding dynamics. The information acquirement and real-time control of arc weld pool dynamical process during automatic or robotic welding always are perplexing problems to both te- nologist in weld ?eld and scientists in automation. This book presents some application researches on intelligentized methodology in arc welding process, such as machine vision, image processing, fuzzy logical, neural networks, rough set, intelligent control and other arti cial intelligence me- ods for sensing, modeling and intelligent control of arc welding dynamical process. The studies in the book indicate that the designed vision sensing and control s- tems are able to partially emulate a skilled welders intelligent behaviors: observing, estimating, decision-making and operating, and show a great potential and prom- ing prospect of arti cial intelligent technologies in the welding manufacturing.
1 Introduction 1
1.1 Development of Welding and Manufacturing Technology
1
1.2 Sensing Technology for Arc Welding Process
3
1.3 Visual Sensing Technology for Arc Welding Process
3
1.3.1 Active Visual Sensing
4
1.3.2 Passive Direct Visual Sensing
6
1.3.3 Image Processing Methods
9
1.4 Modeling Methods for Arc Welding Process
13
1.4.1 Analytical Model
13
1.4.2 Identification, Fuzzy Logic and Neural Network Models
14
1.4.3 Rough Set Model
18
1.5 Intelligent Control Strategies for Arc Welding Process
19
1.6 The Organized Framework of the Book
23
References
23
2 Visual Sensing Systems for Arc Welding Process 35
2.1 Description of the Real-Time Control Systems with Visual Sensing of Weld Pool for the Pulsed GTAW Process
35
2.2 The Visual Sensing System and Images of Weld Pool During Low Carbon Steel Pulsed GTAW
38
2.2.1 Analysis of the Sensing Conditions for Low Carbon Steel
38
2.2.2 Capturing Simultaneous Images of Weld Pool in a Frame from Two Directions
38
2.2.3 Capturing Simultaneous Images of Weld Pool in a Frame from Three Directions
43
2.3 The Visual Sensing System and Images of Weld Pool During Aluminium Alloy Pulsed GTAW
44
2.3.1 Analysis of the Sensing Conditions for Aluminium Alloy
44
2.3.2 Capturing Simultaneous Images of Weld Pool in a Frame from Two Directions
47
2.3.3 Capturing Simultaneous Images of Weld Pool in a Frame from Three Directions
51
2.4 The
Chapter Conclusion Remarks
54
References
55
3 Information Acquirement of Arc Welding Process 57
3.1 Acquiring Two Dimensional Characteristics from Weld Pool Image During Pulsed GTAW
57
3.1.1 Definition of Weld Pool Shape Parameters
58
3.1.2 The Processing and Characteristic Computing of Low Carbon Steel Weld Pool Images
59
3.1.3 The Processing and Characteristic Computing of Aluminium Alloy Weld Pool Image
69
3.2 Acquiring Three Dimensional Characteristics from Monocular Image of Weld Pool During Pulsed GTAW
78
3.2.1 Definition of Topside Weld Pool Height
78
3.2.2 Extracting Surface Height of the Weld Pool from Arc Reflection Position
79
3.2.3 Extracting Surface Height of the Weld Pool by Shape from Shading
81
3.3 The Software of Image Processing and Characteristic Extracting of Weld Pool During Pulsed GTAW
101
3.3.1 The Framework and Function of the Software System
101
3.3.2 The Directions for Using the Software System
102
3.4 The
Chapter Conclusion Remarks
110
References
111
4 Modeling Methods of Weld Pool Dynamics During Pulsed GTAW 113
4.1 Analysis on Welding Dynamics
113
4.1.1 Transient Responses with Pulse Duty Ratio Step Changes
115
4.1.2 Transient Responses with Welding Velocity Step Changes
116
4.1.3 Transient Responses with Peak Current Step Changes
116
4.1.4 Transient Responses with Wire Feeding Velocity Step Changes
117
4.2 Identification Models of Weld Pool Dynamics
118
4.2.1 Linear Stochastic Models of Aluminium Alloy Weld Pool Dynamics
118
4.2.2 Nonlinear Models of Low Carbon Steel Weld Pool Dynamics
123
4.3 Artificial Neural Network Models of Weld Pool Dynamics
126
4.3.1 BWHDNNM Model for Predicting Backside Width and Topside Height During Butt Pulsed GTAW
127
4.3.2 BNNM Model for Predicting Backside Width During Butt Pulsed GTAW
130
4.3.3 BHDNNM Model for Predicting Backside Width and Topside Height During Butt Pulsed GTAW Based on Three-Dimensional Image Processing
131
4.3.4 SSNNM Model During Butt Pulsed GTAW
133
4.4 Knowledge Models of Weld Pool Dynamical Process
137
4.4.1 Extraction of Fuzzy Rules Models of Weld Pool Dynamical Process
137
4.4.2 Knowledge Models Based-on Rough Sets for Weld Pool Dynamical Process Based on Classic Theory
139
4.4.3 A Variable Precision Rough Set Based Modeling Method for Pulsed GTAW
150
4.5 The
Chapter Conclusion Remarks
161
References
161
5 Intelligent Control Strategies for Arc Welding Process 163
5.1 Open-Loop Experiments
163
5.2 PID Controller for Weld Pool Dynamics During Pulsed GTAW
165
5.2.1 PID Control Algorithm
165
5.2.2 Welding Experiments with PID Controller
166
5.3 PSD Controller for Weld Pool Dynamics During Pulsed GTAW
168
5.3.1 PSD Controller Algorithms
168
5.3.2 Welding Experiments with PSD Controller
170
5.4 NN Self-Learning Controller for Dynamical Weld Pool During Pulsed GTAW
172
5.4.1 FNNC Control Algorithm
173
5.4.2 Experiment of FNNC Control Scheme
178
5.5 Model-Free Adaptive Controller for Arc Welding Dynamics
182
5.5.1 Preliminary of Model-Free Adaptive Control (MFC)
184
5.5.2 The Improved Model-Free Adaptive Control with G Function Fuzzy Reasoning Regulation
186
5.5.3 Realization and Simulation of Improved Control Algorithm
188
5.5.4 Controlled Experiments on Pulsed GTAW Process
190
5.6 Composite Intelligent Controller for Weld Pool Dynamics During Pulsed GTAW
194
5.6.1 FNNC- Expert System Controller for Low Carbon Steel During Butt Welding
195
5.6.2 FNNC- Forward Feed Controller for Low Carbon Steel During Butt Welding with Gap Variations
206
5.6.3 Compensated Adaptive- Fuzzy Controller for Aluminiun, Alloy During Butt Welding
205
5.6.4 Adaptive-Fuzzy Controller Based on Nonlinear Model for Low Carbon Steel During Butt Welding with Wire Filler
210
5.7 The
Chapter Conclusion Remarks
218
References
220
6 Real-Time Control of Weld Pool Dynamics During Robotic GTAW 221
6.1 Real-Time Control of Low Carbon Steel Weld Pool Dynamics by PID Controller During Robotic Pulsed GTAW
221
6.1.1 Welding Robot Systems with Vision Sensing and Real-Time Control of Arc Weld Dynamics
223
6.1.2 Weld Pool Image Processing During Robotic Pulsed GTAW
225
6.1.3 Modeling of Dynamic Welding Process
231
6.1.4 Real-Time Control of Low Carbon Steel Welding Pool by PID Regulator During Robotic Pulsed GTAW
234
6.2 Real-Time Control of Weld Pool Dynamics and Seam Forming by Neural Self-Learning Controller During Robotic Pulsed GTAW
236
6.2.1 Neuron Self-Learning PSD Controller for Low Carbon Steel Weld Pool
236
6.2.2 Adaptive Neural PID Controller for Aluminium Alloy Welding Pool
239
6.3 Vision-Based Real-Time Control of Weld Seam Tracking and Weld Pool Dynamics During Aluminium Alloy Robotic Pulsed GTAW
244
6.3.1 Welding Robotic System
245
6.3.2 Image Processing During the Robot Seam Tracking
250
6.3.3 Seam Tracking Controller of the Welding Robot
256
6.3.4 Experiment Results of Seam Tracking and Monitoring During Robotic Welding
258
6.4 Compound Intelligent Control of Weld Pool Dynamics with Visual Monitoring During Robotic Aluminium Alloy Pulsed GTAW
261
6.4.1 The Robotic Welding Systems with Visual Monitoring During Pulsed GTAW
261
6.4.2 Image Obtaining and Processing for Weld Pool During Robotic Welding
262
6.4.3 Modeling and Control Scheme for Welding Robot System
265
6.4.4 Penetration Control Procedure and Results by Robotic Welding
269
6.5 The
Chapter Conclusion Remarks
271
References
271
7 Conclusion Remarks 275
Index 277