Key Technologies of Intelligentized Welding Manufacturing - Visual Sensing of...

asnt2012

Key Technologies of Intelligentized Welding Manufacturing - Visual Sensing of Weld Pool Dynamic Characters and Defect Prediction of GTAW Process - Springer 2021

Zongyao Chen
Air Liquide Delaware Research
and Technology Center
Newark, DE, USA
Jian Chen
Oak Ridge National Laboratory (ORNL)
Oak Ridge, TN, USA
Zhili Feng
Oak Ridge National Laboratory (ORNL)
Oak Ridge, TN, USA
ISBN 978-981-15-6490-1 ISBN 978-981-15-6491-8 (eBook)
https://doi.org/10.1007/978-981-15-6491-8
© Springer Nature Singapore Pte Ltd. 2021


1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research on Visualizing Dynamic Molten Pool Characters
in GTAW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Weld Pool Image Segmentation . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Monitoring 3D Weld Pool Geometry . . . . . . . . . . . . . . . . 4
1.3 Research on Welding Defect Detection Using Vision Sensing
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Monitoring Weld Pool Surface with Active Vision Image . . . . . . . . . 13
2.1 Visual Sensing System Design . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Weld Pool Characters in Active Vision Image . . . . . . . . . . . . . . . 15
2.3 Weld Pool Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5.1 Weld Pool Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5.2 Welding Penetration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5.3 Undercut Defect in High-Speed Welding Condition . . . . . . 22
2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3 Visual Sensing of 3D Weld Pool Geometry with Passive Vision
Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1 Description of 3D Weld Pool Geometry for Bead on Plate
Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2 Passive Vision Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . 26


4 Penetration Prediction with Machine Learning Models . . . . . . . . . . 47
4.1 Definition of Welding Penetration . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3 Evaluation Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Linear Regression for Penetration Prediction . . . . . . . . . . . . . . . . 50
4.4.1 Linear Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4.2 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.5 Penetration Prediction Using Artificial Neural Network . . . . . . . . . 53
4.6 Bagging Tree Model Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.7 Penetration Prediction on Butt Joint Welding . . . . . . . . . . . . . . . . 57
4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5 Penetration Control of Bead on Plate Welding . . . . . . . . . . . . . . . . . 61
5.1 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Modeling Welding Dynamic Behavior . . . . . . . . . . . . . . . . . . . . . 62
5.2.1 Dynamic Modeling Identification . . . . . . . . . . . . . . . . . . . 62
5.2.2 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3 Penetration Control on Uniform Thickness Plate . . . . . . . . . . . . . 64
5.4 Penetration Control on Different Thickness Plate . . . . . . . . . . . . . 66
5.4.1 System Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.4.2 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68


6 Penetration Detection of Narrow U-Groove Welding . . . . . . . . . . . . 71
6.1 Welding Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.1.1 Welding Joint Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.1.2 Establishment of Database . . . . . . . . . . . . . . . . . . . . . . . . 72
6.2 Image Characters of Root Pass Welding . . . . . . . . . . . . . . . . . . . 73
6.2.1 Images Character of Multi-optical Sensing Condition . . . . . 73
6.2.2 Acquire Images with Different Welding Conditions . . . . . . 73
6.3 Training of Prediction Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.3.1 Classification Based on the Extracted Features . . . . . . . . . 76
6.3.2 Backside Width Prediction with Bag Tree Model . . . . . . . 77
6.4 Experiment Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7 Lack of Fusion Detection Inside Narrow U-Groove . . . . . . . . . . . . . 81
7.1 Design of Multi-pass Welding Experiments . . . . . . . . . . . . . . . . . 81
7.2 Experimental Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
7.2.1 Weld Bead Geometry. . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
7.2.2 Characters of Passive Vision Images . . . . . . . . . . . . . . . . . 85
7.2.3 Features Extraction from Passive Vision Image . . . . . . . . . 87
7.3 Predict Lack of Fusion with Data-Driven Model . . . . . . . . . . . . . 88
7.4 Software Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
8 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.2 Future Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

ntlcg

谢谢分享Key Technologies of Intelligentized Welding Manufacturing - Visual Sensing of Weld Pool Dynamic Characters and Defect Prediction of GTAW Process - Springer 2021

lich_0312

谢谢分享Key Technologies of Intelligentized Welding Manufacturing - Visual Sensing of Weld Pool Dynamic Characters and Defect Prediction of GTAW Process - Springer 2021

a8872094

谢谢大佬分享好资料

qldch2306

还有一篇，姊妹篇吧，请参考
https://www.doc88.com/p-00199963760387.html

yuwen

Key Technologies of Intelligentized Welding Manufacturing - Visual Sensing of Weld Pool Dynamic Characters and Defect Prediction of GTAW Process - Springer 2021

MSDYG

感谢分享智能焊接制造的书籍

MSDYG

qldch2306 发表于 2021-4-7 08:26
还有一篇，姊妹篇吧，请参考
https://www.doc88.com/p-00199963760387.html

要是能搞到一并分享就好了

菜头

感谢分享好资料！！！

wuqitao0000

Key Technologies of Intelligentized Welding Manufacturing - Visual Sensing of