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Visual coating inspection framework via self-labeling and multi-stage deep learning strategies

Author

Listed:
  • Changheon Han

    (Purdue University)

  • Jiho Lee

    (Purdue University)

  • Martin B. G. Jun

    (Purdue University
    Purdue University)

  • Sang Won Lee

    (Sungkyunkwan University)

  • Huitaek Yun

    (Korea Advanced Institute of Science and Technology (KAIST))

Abstract

An instantaneous and precise coating inspection method is imperative to mitigate the risk of flaws, defects, and discrepancies on coated surfaces. While many studies have demonstrated the effectiveness of automated visual inspection (AVI) approaches enhanced by computer vision and deep learning, critical challenges exist for practical applications in the manufacturing domain. Computer vision has proven to be inflexible, demanding sophisticated algorithms for diverse feature extraction. In deep learning, supervised approaches are constrained by the need for annotated datasets, whereas unsupervised methods often result in lower performance. Addressing these challenges, this paper proposes a novel deep learning-based automated visual inspection (AVI) framework designed to minimize the necessity for extensive feature engineering, programming, and manual data annotation in classifying fuel injection nozzles and discerning their coating interfaces from scratch. This proposed framework comprises six integral components: It begins by distinguishing between coated and uncoated nozzles through gray level co-occurrence matrix (GLCM)-based texture analysis and autoencoder (AE)-based classification. This is followed by cropping surface images from uncoated nozzles, and then building an AE model to estimate the coating interface locations on coated nozzles. The next step involves generating autonomously annotated datasets derived from these estimated coating interface locations. Subsequently, a convolutional neural network (CNN)-based detection model is trained to accurately localize the coating interface locations. The final component focuses on enhancing model performance and trustworthiness. This framework demonstrated over 95% accuracy in pinpointing the coating interfaces within the error range of ± 6 pixels and processed at a rate of 7.18 images per second. Additionally, explainable artificial intelligence (XAI) techniques such as t-distributed stochastic neighbor embedding (t-SNE) and the integrated gradient substantiated the reliability of the models.

Suggested Citation

  • Changheon Han & Jiho Lee & Martin B. G. Jun & Sang Won Lee & Huitaek Yun, 2025. "Visual coating inspection framework via self-labeling and multi-stage deep learning strategies," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2461-2478, April.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02372-9
    DOI: 10.1007/s10845-024-02372-9
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    References listed on IDEAS

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    1. Huitaek Yun & Hanjun Kim & Young Hun Jeong & Martin B. G. Jun, 2023. "Autoencoder-based anomaly detection of industrial robot arm using stethoscope based internal sound sensor," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1427-1444, March.
    2. Foivos Psarommatis & João Sousa & João Pedro Mendonça & Dimitris Kiritsis, 2022. "Zero-defect manufacturing the approach for higher manufacturing sustainability in the era of industry 4.0: a position paper," International Journal of Production Research, Taylor & Francis Journals, vol. 60(1), pages 73-91, January.
    3. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    4. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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