Welding defect detection: coping with artifacts in the production line

Visual quality inspection for defect detection is one of the main processes in modern industrial production facilities. In the last decades, artificial intelligence solutions took the place of classic computer vision techniques in the production lines and specifically in tasks that, for their complexity, were usually demanded to human workers yet obtaining similar or greater performance of their counterparts. This work exploits a Deep Neural Network for a smart monitoring system capable of performing accurate quality checks to detect welding defects in fuel injectors during the production stage. The contribution focuses on a novel approach to cope with unforeseen changes in production quality introduced by the alteration of a particular machine or process. Results suggest that pre-filtering could avoid the retraining of custom-designed networks. Moreover, the introduction of a weighting strategy on the confusion matrix allows obtaining good performance estimations even in the case of small and unbalanced datasets. Concerning a specific demanding case of an imbalanced dataset with very few positive examples, the system displayed a 96.30% accuracy on defect classification.