Supervised vs. unsupervised FDC and its impact on yield.
Third in a seven-part series: Classic fault detection and classification has some classic problems. It’s reactive, time-consuming to set up, and any product change involves significant man-hours. Even then, it still misses a lot of problems, which result in scrap. This is where machine learning can excel, because it can sift through huge amounts of data from thousands of sensors and find outliers and patterns. But there’s a big difference between supervised FDC and unsupervised. Jon Herlocker, vice president and general manager of software analytics at Cohu, talks about the limitations of supervised FDC, which relies on previous faults, and why unsupervised FDC is necessary to reduce scrap and predict where failures will occur. This is the third part in a seven-part series on AI in manufacturing. Part 1 is here, part 2 is here.
Complete series:
 |
 |
 |
 |
 |
 |
 |  |
 |  |
 |
 |
Leave a Reply