Using AI For Fault Detection And Classification In Manufacturing

Machine learning can enhance fault detection and classification in manufacturing by analyzing data from sensors to identify outliers and patterns. The article discusses the limitations of supervised fault detection and classification (FDC) and the importance of unsupervised FDC in reducing scrap and predicting failures. Jon Herlocker, CEO of Tignis (now part of Cohu), emphasizes the need for unsupervised FDC to improve yield. This article is part of a series on AI in manufacturing.