Physics-Aware Graph Neural Networks for Real-Time Defect Detection and Environmental Impact Mitigation in Industrial Welding
Clicks: 83
ID: 312638
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
24.6
/100
83 views
21 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #8 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
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.
| Reference Key |
imported_1777055668_69ebb7b4a34ad
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Ziyuan Kang |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2026 |
| DOI |
10.56979/1002/2026/1266
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.