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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. [ABSTRACT FROM AUTHOR]

The detection of the contact area formed between a human 23 July 2021 finger and a counter surface is of great interest because it is the key parameter for various interaction parameters. Adhesional friction forces and the thermal contact conductance critically depend on the contact area, further influencing the tactile sensation of stickiness and warmth. The contact area is also of concern regarding safety issues. Injuries caused by objects slipping out of our hands might be prevented by optimizing the contact area and the concomitant grip through appropriate surface structures and material choice. Until now the contact area is mainly studied on smooth and transparent materials. The contact area is recorded optically and rule-based image processing methods can be used for detection. These methods might be insufficient for rough surfaces where the contact area is optically unclear due to light scattering. In this paper we demonstrate the successful analysis of such optically unclear contact area images via convolutional neural networks to identify the fingerprint ridges in contact with structured surfaces. The proposed method relies on the generation of synthetic contact images that provide the pixelwise ground truth for the efficient training of a segmentation pipeline based on convolutional neural networks. [ABSTRACT FROM AUTHOR]