Abstract

The automotive industry is adopting the Industry 4.0 model to reduce failures in electrical resistance spot welding by using automated non-destructive testing systems. This study presents a real-time machine vision system that has been implemented on an automotive cabin assembly line to detect weld defects in door frames. The system extracts the physical parameters of each spot weld, including nugget diameter and heat-affected zone, to identify failing robots and welding points and prioritise maintenance actions (maintenance-free, predictive, preventive or corrective). Machine learning models were trained using images from 674 cabins collected over 30 continuous hours. YOLOv8 was used for spot weld detection and feature extraction. A neural network with linear discriminant analysis was applied for defect classification, achieving 95.80% accuracy, 95.61% precision, 96.00% recall and 95.80% F1-score. Additionally, a convolutional neural network was developed for maintenance prediction, achieving 94.72% precision, 95.87% accuracy, 93.97% F1-score and 94.42% recall. The results demonstrate the effectiveness of real-time defect detection and reliable maintenance prediction, supporting informed decision-making and efficient resource management.

IPC Classification

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