Machine learning-based algorithm for the classification and prediction of multi-type spot weld quality-Reference-Cited by-同舟云学术

Machine learning-based algorithm for the classification and prediction of multi-type spot weld quality

Published:2023 Issue:6 Volume:17 Page:JAMDSM0074-JAMDSM0074
ISSN:1881-3054
Container-title:Journal of Advanced Mechanical Design, Systems, and Manufacturing
language:en
Short-container-title:JAMDSM

Author:

JIA Pengzhen¹,SHENG Buyun¹,ZHAO Guangde²

Affiliation:

1. School of Mechanical and Electrical Engineering, Wuhan University of Technology

2. Dongfeng Motor Corporation Passenger Vehicle Company

Publisher

Japan Society of Mechanical Engineers

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Link

https://www.jstage.jst.go.jp/article/jamdsm/17/6/17_2023jamdsm0074/_pdf

Reference21 articles.

1. B, Wang., Ultrasonic non-destructive testing of the quality of resistance spot welds on vehicle body parts, Wuhan University of Technology, (2018). (in Chinese)

2. Chang, F., et al, A service-oriented dynamic multi-level maintenance grouping strategy based on prediction information of multi-component systems, Journal of Manufacturing Systems, Vol.53, (2019), pp.49–61.

3. G, Chen., B, Sheng., R, Luo., and P, Jia., A parallel strategy for predicting the quality of welded joints in automotive bodies based on machine learning, Journal of Manufacturing Systems, (2022), p. 62.

4. G, Liu., X. Gao, D, You., and N, Zhang., Prediction of high power laser welding status based on pca and svm classification of multiple sensors, Journal of Intelligent Manufacturing, Vol.30(2019), pp. 821-832.

5. Hong, KM. and Shin, YC., Prospects of laser welding technology in the automotive industry: a review, Journal of Materials Processing Technology, Vol.245(2017), pp. 46–69.