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Transparency and Traceability for AI-Based Defect Detection in PCB Production

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Modelling and Development of Intelligent Systems (MDIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1761))

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Abstract

Automatic Optical Inspection (AOI) is used to detect defects in PCB production and provide the end-user with a trustworthy PCB. AOI systems are enhanced by replacing the traditional heuristic algorithms with more advanced methods such as neural networks. However, they provide the operators with little or no information regarding the reasoning behind each decision.

This paper explores the research gaps in prior PCB defect detection methods and replaces these complex methods with CNN networks. Next, it investigates five different Cam-based explainer methods on eight selected CNN architectures to evaluate the performance of each explainer. In this paper, instead of synthetic datasets, two industrial datasets are utilized to have a realistic research scenario. The results evaluated by the proposed performance metric demonstrate that independent of the dataset, the CNN architectures are interpretable using the same explainer methods. Additionally, the Faster Score-Cam method performs better than other methods used in this paper.

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Acknowledgments

Financial support for this study was provided by Thüringer Aufbaubank (TAB, 2021 FE 9036).

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Authors and Affiliations

  1. Technische Universität Ilmenau, Helmholtzplatz 5, 98693, Ilmenau, Germany

    Ahmad Rezaei, Johannes Richter, Johannes Nau, Detlef Streitferdt & Michael Kirchhoff

  2. GÖPEL Electronic GmbH, Jena, Germany

    Johannes Richter

Authors
  1. Ahmad Rezaei
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  2. Johannes Richter
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  3. Johannes Nau
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  4. Detlef Streitferdt
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  5. Michael Kirchhoff
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Corresponding author

Correspondence to Ahmad Rezaei .

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Editors and Affiliations

  1. Lucian Blaga University of Sibiu, Sibiu, Romania

    Dana Simian

  2. Lucian Blaga University of Sibiu, Sibiu, Romania

    Laura Florentina Stoica

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Rezaei, A., Richter, J., Nau, J., Streitferdt, D., Kirchhoff, M. (2023). Transparency and Traceability for AI-Based Defect Detection in PCB Production. In: Simian, D., Stoica, L.F. (eds) Modelling and Development of Intelligent Systems. MDIS 2022. Communications in Computer and Information Science, vol 1761. Springer, Cham. https://doi.org/10.1007/978-3-031-27034-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-27034-5_4

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-27034-5

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