Efficient surface defect detection in industrial screen printing with minimized labeling effort
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In: Integrated computer-aided engineering, Vol. 32.2025, No. 1, 18.10.2024, p. 1-21.
Research output: Contribution to journal › Article › Research › peer-review
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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 -