Research on quality prediction of resistance spot welding based on knowledge graph

Welding data has a dual dependence on time and space. A welding defect prediction model based on Graph Convolutional Neural Network (GCN) and Long Short Term Memory Network (LSTM) is proposed to address the issue of insufficient spatiotemporal feature extraction in previous welding defect prediction. Taking real-time welding data from automobile factories as the research object, combined with knowledge of welding fragmentation and diversity, a welding defect knowledge graph based on root cause analysis was established for the first time. Then, use graph convolutional neural network GCN to capture the spatial relationships of each input node, and use LSTM to capture the temporal changes of welding data. Fusion features are used to predict welding defects. Compared with classical models such as GCN and LSTM, the proposed GCN-LSTM model improves accuracy and performs better in evaluation metrics such as accuracy, ROC curve, AUC, and recall curve. This study has reference significance for optimizing welding processes and improving welding quality.