Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding

Metals

Jiyoung Yu¹  Insung Hwang¹  Sehyeon Kim¹  Young-Min Kim¹  Dong-Yoon Kim¹  Munjin Kang¹ 

[1] Advanced Joining & Additive Manufacturing R&D Department, Korea Institute of Industrial Technology, 156 Gaetbeol-ro, Yeonsu-Gu, Incheon 21999, Korea;

关键词: dynamic resistance;    electrode displacement;    logistic regression model;    polynomial regression model;    resistance spot welding;    welding quality prediction;   

DOI  :  10.3390/met11091459

来源: DOAJ

【 摘 要 】

An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that can predict the geometrical and physical properties of a spot-welded joint and evaluate weld quality based on quality acceptance criteria. To this end, four statistical models that predict the main geometrical and physical properties of a spot-welded joint, including tensile shear strength, indentation depth, expulsion occurrence, and failure mode, were estimated based on material information, dynamic resistance, and electrode displacement signals. The significance of the estimated models was then verified through an analysis of variance. The prediction accuracies of the models were 94.3%, 93.4%, 97.5%, and 85.0% for the tensile shear strength, indentation depth, expulsion occurrence, and failure modes, respectively. A weld quality evaluation methodology that can predict the properties of a spot-welded joint and evaluate the overall quality requirements based on authorized welding standards was proposed using the four statistical models.

【 授权许可】

Unknown