Online defect detection method for resistance spot welding based on multi-source information fusion network

Resistance Spot Welding (RSW), a crucial joining technique widely used in various industries, is especially significant in automotive sheet metal assembly. However, the erratic disruptions in fast-paced automotive production lines present challenges to weld spot quality detection and control. In this study, a multi-source information fusion network (MSIFN) that takes the energy and mechanical feature vector extracted based on expert knowledge as inputs is developed for online resistance spot welding (RSW) defect detection. The network comprises three parts: a single-source data high-dimensional feature extraction module based on attention multi-layer perceptron (MLP), a multi-source feature interaction module based on the mutual attention mechanism (MAM), and a feature mixing and enhancement module based on multi-head bilinear fusion. To address the limited and imbalanced nature of real-world data, Focal Loss is used as the training objective. The model classifies four types of welding outcomes: normal weld, cold weld, burn-through, and shrinkage. Compared with the models using only energy features or mechanical features, the proposed MSIFN achieved the highest average classification accuracy (96.3 %), demonstrating the complementarity of the two feature types and can enrich the characterization of welding quality. Meanwhile, compared with other commonly used artificial intelligence classification models, the MSIFN also demonstrated the highest average classification accuracy and better performance in terms of the false negative rate and false positive rate for each defect category. These experiments have verified the applicability of the proposed method with high precision and robustness in online RSW defect detection and can enhance the understanding of RSW process.