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Key Technologies of Intelligentized Welding Manufacturing: Visual Sensing of Weld Pool Dynamic Characters and Defect Prediction of GTAW Process 2021 ed. [Kõva köide]

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  • Formaat: Hardback, 95 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 70 Illustrations, color; 17 Illustrations, black and white; XIII, 95 p. 87 illus., 70 illus. in color., 1 Hardback
  • Ilmumisaeg: 15-Jul-2020
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811564906
  • ISBN-13: 9789811564901
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Key Technologies of Intelligentized Welding Manufacturing: Visual Sensing of Weld Pool Dynamic Characters and Defect Prediction of GTAW Process 2021 ed.Suurem pilt
  • Formaat: Hardback, 95 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 70 Illustrations, color; 17 Illustrations, black and white; XIII, 95 p. 87 illus., 70 illus. in color., 1 Hardback
  • Ilmumisaeg: 15-Jul-2020
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811564906
  • ISBN-13: 9789811564901
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811564901.html
This book describes the application of vision-sensing technologies in welding processes, one of the key technologies in intelligent welding manufacturing. Gas tungsten arc welding (GTAW) is one of the main welding techniques and has a wide range of applications in the manufacturing industry. As such, the book also explores the application of AI technologies, such as vision sensing and machine learning, in GTAW process sensing and feature extraction and monitoring, and presents the state-of-the-art in computer vision, image processing and machine learning to detect welding defects using non-destructive methods in order to improve welding productivity. Featuring the latest research from ORNL (Oak Ridge National Laboratory) using digital image correlation technology, this book will appeal to researchers, scientists and engineers in the field of advanced manufacturing.
1 Introduction
1(12)
1.1 Background
1(1)
1.2 Research on Visualizing Dynamic Molten Pool Characters in GTAW
2(4)
1.2.1 Weld Pool Image Segmentation
3(1)
1.2.2 Monitoring 3D Weld Pool Geometry
4(2)
1.3 Research on Welding Defect Detection Using Vision Sensing Method
6(3)
1.4 Contribution
9(4)
References
10(3)
2 Monitoring Weld Pool Surface with Active Vision Image
13(12)
2.1 Visual Sensing System Design
13(2)
2.2 Weld Pool Characters in Active Vision Image
15(1)
2.3 Weld Pool Image Segmentation
16(2)
2.4 Experiment Result
18(1)
2.5 Discussion
19(4)
2.5.1 Weld Pool Depression
19(1)
2.5.2 Welding Penetration
20(2)
2.5.3 Undercut Defect in High-Speed Welding Condition
22(1)
2.6 Conclusions
23(2)
References
24(1)
3 Visual Sensing of 3D Weld Pool Geometry with Passive Vision Image
25(22)
3.1 Description of 3D Weld Pool Geometry for Bead on Plate Welding
25(1)
3.2 Passive Vision Image Acquisition
26(2)
3.3 2D Weld Pool Geometry Measurement with Adaptive Passive Vision Method
28(5)
3.3.1 Conventional Image Segmentation Method
28(1)
3.3.2 Software Framework of Adaptive Passive Vision Method
29(1)
3.3.3 Landmarks Detection
29(2)
3.3.4 Camera Exposure Time Determination Based on SVM
31(1)
3.3.5 Experiment Validation
32(1)
3.4 Monitoring Weld Pool Surface from Reversed Electrode Image
33(8)
3.4.1 Acquisition of Reversed Electrode Image During GTAW
34(1)
3.4.2 Reflection Model of Weld Pool Surface
35(2)
3.4.3 Algorithm of Weld Pool Surface Height Calculation
37(1)
3.4.4 Experimental Validation of SH Measurement
38(3)
3.5 Validation of 3D Weld Pool Geometry Measurement
41(4)
3.6 Conclusion
45(2)
References
45(2)
4 Penetration Prediction with Machine Learning Models
47(14)
4.1 Definition of Welding Penetration
47(2)
4.2 Data Collection
49(1)
4.3 Evaluation Criteria
49(1)
4.4 Linear Regression for Penetration Prediction
50(3)
4.4.1 Linear Regression Model
50(1)
4.4.2 Feature Selection
51(2)
4.5 Penetration Prediction Using Artificial Neural Network
53(2)
4.6 Bagging Tree Model Prediction
55(2)
4.7 Penetration Prediction on Butt Joint Welding
57(2)
4.8 Conclusion
59(2)
References
60(1)
5 Penetration Control of Bead on Plate Welding
61(10)
5.1 Framework
61(1)
5.2 Modeling Welding Dynamic Behavior
62(2)
5.2.1 Dynamic Modeling Identification
62(1)
5.2.2 Simulation
63(1)
5.3 Penetration Control on Uniform Thickness Plate
64(2)
5.4 Penetration Control on Different Thickness Plate
66(2)
5.4.1 System Modeling
66(2)
5.4.2 Experiment
68(1)
5.5 Conclusion
68(3)
6 Penetration Detection of Narrow U-Groove Welding
71(10)
6.1 Welding Parameters
71(2)
6.1.1 Welding Joint Design
71(1)
6.1.2 EstablishmentofDataba.se
72(1)
6.2 Image Characters of Root Pass Welding
73(3)
6.2.1 Images Character of Multi-optical Sensing Condition
73(1)
6.2.2 Acquire Images with Different Welding Conditions
73(3)
6.3 Training of Prediction Model
76(2)
6.3.1 Classification Based on the Extracted Features
76(1)
6.3.2 Backside Width Prediction with Bag Tree Model
77(1)
6.4 Experiment Validation
78(2)
6.5 Conclusions
80(1)
7 Lack of Fusion Detection Inside Narrow U-Groove
81(12)
7.1 Design of Multi-pass Welding Experiments
81(2)
7.2 Experimental Observations
83(5)
7.2.1 Weld Bead Geometry
83(2)
7.2.2 Characters of Passive Vision Images
85(2)
7.2.3 Features Extraction from Passive Vision Image
87(1)
7.3 Predict Lack of Fusion with Data-Driven Model
88(2)
7.4 Software Integration
90(1)
7.5 Conclusions
90(3)
References
91(2)
8 Conclusions and Recommendations
93
8.1 Conclusions
93(2)
8.2 Future Work
95
Dr. Zongyao Chen received his B.S. and M.S. in Electrical Engineering in 2009 and 2012 from Shanghai Jiaotong University. He received his Ph.D. from the University of Tennessee in November 2018. During his Ph.D. study, he worked with the welding and joining research group at oak ridge national lab and focused on applying artificial intelligence and computer vision technology to welding manufacturing and proposed the Reversed Electrode Image (REI) method to extract the features and penetration of welding pool during GTAW process in his doctoral dissertation. He is currently working as a Research Scientist at the R&D Centre of Air Liquide in Delaware, USA.  Dr. Chens research interests include robotics, welding automation, computer vision machine learning and other AI applications in industry.Dr. Feng is a Group Leader of Materials Processing and Joining Group and a Distinguished R&D Staff of Oak Ridge National Laboratory. He manages a diverse R&D portfolio aimed at addressingthe materials processing and joining needs from automotive, aerospace, nuclear, petrochemical and power generation industries. His primary interest is in thermalmechanicalmetallurgical behaviors of materials during processing and joining. Most recent work included integrated computational welding engineering (ICWE), proactive weld residual stress control and management, friction stir welding and processing, characterization of weld by advanced neutron and synchrotron scattering, and novel solid-state joining processes of dissimilar metals. Dr. Feng received his Ph.D. in Welding Engineering from the Ohio State University. He is a Fellow of the American Welding Society, a Joint Faculty ProfessorDepartment of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, and Guest Professor of Tsinghua University. Dr. Feng has broad interactions with industry and extensive experience in solving critical industry problems.Dr. Feng is currently one of Editors-in-Chief Transactions on Intelligent Welding Manufacturing (TIWM)  authorized by Springer for periodical publication of research papers from 2017.





Dr. Jian Chen is a Research Staff in Materials Processing and Joining Group at Oak Ridge National Laboratory. He has significant experimental and analytical experiences in developing advanced materials joining and processing technologies and the associated control and monitoring techniques. His current R&D focuses on advanced welding and joining techniques, intelligent welding process monitoring and control, non-destructive weld quality inspection and high-performance welding simulation. Dr. Chen received his doctoral degree in Industrial Engineering from the Ohio State University. He is a member of American Welding Societys Technical Papers Committee and 2nd Vice Chair of American Welding Societys North East Tennessee Section.