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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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    Cited by:

    1. Joseph Cohen & Xun Huan & Jun Ni, 2024. "Shapley-based explainable AI for clustering applications in fault diagnosis and prognosis," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4071-4086, December.
    2. Abhilash Puthanveettil Madathil & Xichun Luo & Qi Liu & Charles Walker & Rajeshkumar Madarkar & Yukui Cai & Zhanqiang Liu & Wenlong Chang & Yi Qin, 2024. "Intrinsic and post-hoc XAI approaches for fingerprint identification and response prediction in smart manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4159-4180, December.
    3. Chen Luo & Tingxiao Fan & Yan Xia & Yijun Zhou & Lei Jia & Baocheng Hui, 2025. "Deep learning-based conductive particle inspection for TFT-LCDs inspired by parametric space envelope," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 209-219, January.

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