Unveiling Hidden Anomalies
A Hybrid Approach for Surface Mounted Electronics
Amir Ghorbani Ghezeljehmeidan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Willem Dirk van Driel (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Justin Dauwels (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Industrial assembly lines are the heartbeat of modern manufacturing, where precision and efficiency are paramount. This paper introduces a novel hybrid Explainable artificial intelligence (XAI) approach to enhance monitoring and analysis in industrial assembly. By fusing the power of vision anomaly detection models with the clarity of the gradient tree boosting algorithm, this framework not only boosts defect detection accuracy but also provides transparent, actionable insights. This synergy transforms how operators and engineers interact with AI, fostering trust and enhancing operational excellence.