ML-TN-003 — AI at the edge: visual inspection of assembled PCBs for defect detection — Part 1
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HistoryEdit
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | March 2021 | First public release |
IntroductionEdit
In [ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1 this series] of articles, different embedded platforms suited for building "[Edge AI]" solutions are compared from the point of view of inferencing capabilities/features, development tools, etc. In principle, such platforms can drive a bunch of different applications in the industrial world and other fields as well.
This series of Technical Notes illustrates a feasibility study regarding a common problem in the manufacturing realm that supposedly Machine Learning (ML) algorithms can address effectively: defect detection by automatic visual inspection. More specifically, this study deals with the inspection of assembled Printed Circuit Boards (PCBs). The aim is to determine if it would be possible to design innovative machines exploiting ML algorithms and able to ouperform traditional devices employed for this task.
Useful linksEdit
- Young-Gyu Kim, Tae-Hyoung Park, SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions, July 2020.