Physics-Aware Graph Neural Networks for Real-Time Defect Detection and Environmental Impact Mitigation in Industrial Welding

Abstract

Resistance Spot Welding (RSW) is a basic and energy-consuming technology in industrial production, where the interaction of electrical, thermal, and mechanical variables usually hides the connection between the quality of the process and its environmental impact. We introduce a new Spatio-Temporal Graph Neural Network (STGNN) framework solver optimistic of the twin-objective of real-time defect detection and environmental emission reduction. Through the theoreticalization of the welding process as a dynamic graph with voltage, current and force sensor nodes taking the place of nodes, we use Graph Temporal Transformers and Graph Attention Networks (GATv2) architecture to decode the transient cross-channel relationships throughout the welding cycle. This methodology generates a Physics-Aware latent space which models the spatial dynamics of the electrodes as well as the time dynamics of the weld nugget. Extensive benchmarking on seven variants of deep learning and ensemble techniques shows that our framework attains near-perfect stability on regression, where the highest score of and the MAPE of represent the best result in emission proxy modeling. Although there was an extreme imbalance on the industrial class (4% defect interactions), the suggested architecture was able to isolate defect signatures in a high-contrast 3D feature space (Deep Blue for Optimal vs. Crimson Red for Defective). Numerical validation supports ultra-low inference latencies down to the range of ms/sample allowing integration into high rate production systems without problems. This study gives a clear direction of the so-called Zero-Defect green manufacturing, as it proves that the graph-based reasoning can successfully decouple the industrial productivity and environmental externalities.