Machine learning enables real-time PCB defect detection using a FOMO model on a Raspberry Pi. Learn how this approach helps manufacturers catch errors early and reduce defects.
In this article, we explore how machine learning can improve PCB manufacturing by detecting defects such as missing holes, open circuits, and short circuits. Using the FOMO algorithm, a model is trained and deployed on a Raspberry Pi 4 with a camera module to perform real-time inspection. This approach can help manufacturers catch issues early, reduce errors, and move closer to zero-defect production.
Overview
To reduce the number of defects on PCBs, a number of inspections are carried out at various points in modern manufacturing assembly lines. With an increased number of PCB manufacturers and developers wanting more compact circuit layouts, companies have developed advanced inspection systems, but sometimes defects can still go unnoticed.
This project aims to look at three defects on a PCB and demonstrate how machine learning can be used to identify them. One of the defects is missing holes, which can be caused by faulty tooling or excessive processing. This hinders components…
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