Efficient surface defect detection in industrial screen printing with minimized labeling effort

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Efficient surface defect detection in industrial screen printing with minimized labeling effort. / Krassnig, Paul Josef; Haselmann, Matthias; Kremnitzer, Michael et al.
In: Integrated computer-aided engineering, Vol. 32.2025, No. 1, 18.10.2024, p. 1-21.

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Krassnig, Paul Josef ; Haselmann, Matthias ; Kremnitzer, Michael et al. / Efficient surface defect detection in industrial screen printing with minimized labeling effort. In: Integrated computer-aided engineering. 2024 ; Vol. 32.2025, No. 1. pp. 1-21.

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@article{fb9e70e77ed34e5d86339ed1102604cb,
title = "Efficient surface defect detection in industrial screen printing with minimized labeling effort",
abstract = "As part of the evolving Industry 4.0 landscape, machine learning-based visual inspection plays a key role in enhancing production efficiency. Screen printing, a versatile and cost-effective manufacturing technique, is widely applied in industries like electronics, textiles, and automotive. However, the production of complex multilayered designs is error-prone, resulting in a variety of defect appearances and classes. These defects can be characterized as small in relation to large sample areas and weakly pronounced. Sufficient defect visualization and robust defect detection methods are essential to address these challenges, especially considering the permitted design variability. In this work, we present a novel automatic visual inspection system for surface defect detection on decorated foil plates. Customized optical modalities, integrated into a sequential inspection procedure, enable defect visualization of production-related defect classes. The introduced patch-wise defect detection methods, designed to leverage less labeled data, prove effective for industrial defect detection, meeting the given process requirements. In this context, we propose an industry-applicable and scalable data preprocessing workflow that minimizes the overall labeling effort while maintaining high detection performance, as known in supervised settings. Moreover, the presented methods, not relying on any labeled defective training data, outperformed a state-of-the-art unsupervised anomaly detection method in terms of defect detection performance and inference speed.",
author = "Krassnig, {Paul Josef} and Matthias Haselmann and Michael Kremnitzer and Gruber, {Dieter Paul}",
year = "2024",
month = oct,
day = "18",
doi = "10.3233/ICA-240742",
language = "English",
volume = "32.2025",
pages = "1--21",
journal = " Integrated computer-aided engineering",
issn = "1069-2509",
publisher = "John Wiley & Sons Inc.",
number = "1",

}

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TY - JOUR

T1 - Efficient surface defect detection in industrial screen printing with minimized labeling effort

AU - Krassnig, Paul Josef

AU - Haselmann, Matthias

AU - Kremnitzer, Michael

AU - Gruber, Dieter Paul

PY - 2024/10/18

Y1 - 2024/10/18

N2 - As part of the evolving Industry 4.0 landscape, machine learning-based visual inspection plays a key role in enhancing production efficiency. Screen printing, a versatile and cost-effective manufacturing technique, is widely applied in industries like electronics, textiles, and automotive. However, the production of complex multilayered designs is error-prone, resulting in a variety of defect appearances and classes. These defects can be characterized as small in relation to large sample areas and weakly pronounced. Sufficient defect visualization and robust defect detection methods are essential to address these challenges, especially considering the permitted design variability. In this work, we present a novel automatic visual inspection system for surface defect detection on decorated foil plates. Customized optical modalities, integrated into a sequential inspection procedure, enable defect visualization of production-related defect classes. The introduced patch-wise defect detection methods, designed to leverage less labeled data, prove effective for industrial defect detection, meeting the given process requirements. In this context, we propose an industry-applicable and scalable data preprocessing workflow that minimizes the overall labeling effort while maintaining high detection performance, as known in supervised settings. Moreover, the presented methods, not relying on any labeled defective training data, outperformed a state-of-the-art unsupervised anomaly detection method in terms of defect detection performance and inference speed.

AB - As part of the evolving Industry 4.0 landscape, machine learning-based visual inspection plays a key role in enhancing production efficiency. Screen printing, a versatile and cost-effective manufacturing technique, is widely applied in industries like electronics, textiles, and automotive. However, the production of complex multilayered designs is error-prone, resulting in a variety of defect appearances and classes. These defects can be characterized as small in relation to large sample areas and weakly pronounced. Sufficient defect visualization and robust defect detection methods are essential to address these challenges, especially considering the permitted design variability. In this work, we present a novel automatic visual inspection system for surface defect detection on decorated foil plates. Customized optical modalities, integrated into a sequential inspection procedure, enable defect visualization of production-related defect classes. The introduced patch-wise defect detection methods, designed to leverage less labeled data, prove effective for industrial defect detection, meeting the given process requirements. In this context, we propose an industry-applicable and scalable data preprocessing workflow that minimizes the overall labeling effort while maintaining high detection performance, as known in supervised settings. Moreover, the presented methods, not relying on any labeled defective training data, outperformed a state-of-the-art unsupervised anomaly detection method in terms of defect detection performance and inference speed.

U2 - 10.3233/ICA-240742

DO - 10.3233/ICA-240742

M3 - Article

VL - 32.2025

SP - 1

EP - 21

JO - Integrated computer-aided engineering

JF - Integrated computer-aided engineering

SN - 1069-2509

IS - 1

ER -