Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser

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... hy by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods. INDEX TERMS Active thermal imaging, laser thermography, spot-welded joints, convolutional neural network, classification, data preprocessing. • Proposal of CNN-based welding quality assessment method to classify welding quality from thermal images that are not distinguishable by human vision inspection. • Proposal of methods to generate a feasible training dataset from thermal images by analyzing the underlying physics and generating filters accordingly. • Evaluation of different data augmentation methods and their effect on thermal datasets. • Performance evaluation of three State-of-the-Art neural network architectures. The paper is structured as follows. Sec. II begins with the theoretical foundations utilized in our approach. The methodology including the overall concept and the implementation of each module, is presented in Sec III. Sec. IV presents the VOLUME 9, 2021 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 48304 VOLUME 9, 2021