Abstract
Today, the process of producing a printed circuit board assembly (PCBA) is growing rapidly, and this process requires cutting-edge debugging and testing of the boards. The Automatic Optical Inspection (AOI) process detects defects in the boards, components, or solder pads using image processing and machine learning (ML) algorithms. Although state-of-the-art approaches for identifying defects are well developed, due to three main issues, the ML algorithms and datasets are incapable of fully integrating into industrial plants. These issues are privacy limitations for sharing data, the distribution shifts in the PCBA industry, and the absence of a degree of freedom for reproducible and modifiable synthetic datasets.
This paper addresses these challenges and introduces “ReProInspect”, a comprehensive framework designed to meet these requirements. ReProInspect uses fabrication files from the designed PCBs in the manufacturing line to automatically generate 3D models of the PCBAs. By incorporating various techniques, the framework introduces controlled defects into the PCBA, thereby creating reproducible and differentiable defect datasets. The quality data produced by this framework enables an improved detection and classification scenario for AOI in industrial applications. The initial results of ReProInspect are demonstrated and discussed through detailed instances. Finally, the paper also highlights future work to improve the current state of the framework.
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Notes
- 1.
MarketWatch, The Prospects of Printed Circuit Board (PCB) Market 2023: Industry Trends and Challenges till 2030.
- 2.
Available (last seen on 18.08.2023): http://defectsdatabase.npl.co.uk/.
- 3.
Available: https://www.kicad.org/.
- 4.
Available (last seen on 18.08.2023):https://github.com/dmitrystu/Nucleo2USB.
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Thüringer Aufbaubank (TAB, 2021 FE 9036) provided financial support for this study.
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Rezaei, A., Nau, J., Streitferdt, D., Schambach, J., Vangelov, T. (2024). ReProInspect: Framework for Reproducible Defect Datasets for Improved AOI of PCBAs. In: Kofroň, J., Margaria, T., Seceleanu, C. (eds) Engineering of Computer-Based Systems. ECBS 2023. Lecture Notes in Computer Science, vol 14390. Springer, Cham. https://doi.org/10.1007/978-3-031-49252-5_16
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