Prediction of welding properties of solder joints based on machine learning

Yumeng Du; Lei Dong; Shouxin Ruan; Yiwen Zhang; Yan Gao; Zhenhui Li; Zengyu Lin; Xiaofeng Wang; Hainan Li; Guoxin Song

doi:10.1117/12.3014864

4 March 2024 Prediction of welding properties of solder joints based on machine learning

Yumeng Du, Lei Dong, Shouxin Ruan, Yiwen Zhang, Yan Gao, Zhenhui Li, Zengyu Lin, Xiaofeng Wang, Hainan Li, Guoxin Song

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129812Z (2024) https://doi.org/10.1117/12.3014864
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

To greatly improve the design efficiency and the prediction of the welding properties of on-site solder joints in the welding workshop, an intelligent calculation algorithm based on CATIA. API for secondary development of the solder joint level is designed. This algorithm, based on different interference collision radiation radii, calculates the correlation accuracy between different solder joints and BIW products, and extracts the BIW product information data related to the solder joints, to realize the automatic generation technology of digital and intelligent welding attribute BOM. On this basis, we use Python to write the linear regression algorithm in machine learning to train the welding attribute parameters of the solder joints.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yumeng Du, Lei Dong, Shouxin Ruan, Yiwen Zhang, Yan Gao, Zhenhui Li, Zengyu Lin, Xiaofeng Wang, Hainan Li, and Guoxin Song "Prediction of welding properties of solder joints based on machine learning", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129812Z (4 March 2024); https://doi.org/10.1117/12.3014864

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