Open AccessQuality Assessment of RSW Based on Transfer Learning and Imbalanced Multi-Class Classification Algorithm
Author(s) -
Peijin Guo,
Qinmiao Zhu,
Jingran Kang,
Yuhui Wang,
Wenqiang Hu
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3212410
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In automobile manufacturing, the quality assessment of resistance spot welding (RSW) plays a decisive role in the quality and safety of products. Recently, it has become very popular to use machine learning to evaluate the quality of welding nuggets. However, there are two obstacles: data imbalance caused by limited defective samples, and data shortage due to expensive time and labor costs. This paper proposes a novel method. On one hand, the self-paced ensemble (SPE) algorithm for binary classification is improved to handle imbalanced multi-class classification of quality levels. On the other hand, an instance-based ensemble transfer learning approach is proposed to predict the tensile-shear strength of RSW for precise control of the weld quality. In detail, a quality level identification model is formulated with the process and material parameters as the input at first. Secondly, an explainable algorithm SHapley Additive exPlanations (SHAP) was introduced to anatomize the impacts of welding parameters on the welding quality predictions. Finally, a hybrid dataset including actual historic production data and 454 spot-welding cases is constructed, and then the eXtreme Gradient Boosting (XGBoost) is introduced as the base learner of TrAdaBoost.R2 to train the prediction model. Compared with conventional methods, the SPE provides the greatest macro geometric-mean score of 0.923, and the proposed regression model yields superior accuracy $R^{2}$ of 0.952, which shows the potential of assisting welding process design.