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Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels

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  • Minyoung Lee

    (Korea University)

  • Joohyoung Jeon

    (Korea University)

  • Hongchul Lee

    (Korea University)

Abstract

The deep learning (DL) model has performed successfully in various fields, including manufacturing. DL models for defect image data analysis in the manufacturing field have been applied to multiple domains such as defect detection, classification, and localization. However, DL models require trade-offs in accuracy and interpretability. We use explainable artificial intelligence techniques to analyze the predicted results of the defect image classification model, which is considered as a “black-box” model, to produce human-understandable results. We visualize defects using layer-wise relevance propagation-based methods, fit the model into a decision tree, and convert prediction results into human-interpretable text. Our research complements the interpretation of prediction results for the classification model. The domain expert can obtain the reliability and explanatory ability for the defect classification of TFT–LCD panel data of the DL model through the results of the proposed analysis.

Suggested Citation

  • Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01758-3
    DOI: 10.1007/s10845-021-01758-3
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    References listed on IDEAS

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    1. Wangzhe Du & Hongyao Shen & Jianzhong Fu & Ge Zhang & Xuanke Shi & Quan He, 2021. "Automated detection of defects with low semantic information in X-ray images based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 141-156, January.
    2. Konstantakopoulos, Ioannis C. & Barkan, Andrew R. & He, Shiying & Veeravalli, Tanya & Liu, Huihan & Spanos, Costas, 2019. "A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure," Applied Energy, Elsevier, vol. 237(C), pages 810-821.
    3. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    4. Fangwei Ning & Yan Shi & Maolin Cai & Weiqing Xu, 2020. "Various realization methods of machine-part classification based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2019-2032, December.
    5. Sebastian Lapuschkin & Stephan Wäldchen & Alexander Binder & Grégoire Montavon & Wojciech Samek & Klaus-Robert Müller, 2019. "Unmasking Clever Hans predictors and assessing what machines really learn," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    6. Chengbao Liu & Jie Tan & Xuelei Wang, 2020. "A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 833-845, April.
    7. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    8. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
    9. Sebastian Bach & Alexander Binder & Grégoire Montavon & Frederick Klauschen & Klaus-Robert Müller & Wojciech Samek, 2015. "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-46, July.
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