TY - JOUR A1 - Huo, Wenjie A1 - Bakir, Nasim A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, Katinka T1 - Detection of solidification crack formation in laser beam welding videos of sheet metal using neural networks N2 - AbstractLaser beam welding has become widely applied in many industrial fields in recent years. Solidification cracks remain one of the most common welding faults that can prevent a safe welded joint. In civil engineering, convolutional neural networks (CNNs) have been successfully used to detect cracks in roads and buildings by analysing images of the constructed objects. These cracks are found in static objects, whereas the generation of a welding crack is a dynamic process. Detecting the formation of cracks as early as possible is greatly important to ensure high welding quality. In this study, two end-to-end models based on long short-term memory and three-dimensional convolutional networks (3D-CNN) are proposed for automatic crack formation detection. To achieve maximum accuracy with minimal computational complexity, we progressively modify the model to find the optimal structure. The controlled tensile weldability test is conducted to generate long videos used for training and testing. The performance of the proposed models is compared with the classical neural network ResNet-18, which has been proven to be a good transfer learning model for crack detection. The results show that our models can detect the start time of crack formation earlier, while ResNet-18 only detects cracks during the propagation stage. KW - Artificial Intelligence KW - Software PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-586116 DO - https://doi.org/10.1007/s00521-023-09004-y SN - 0941-0643 VL - 35 IS - 34 SP - 24315 EP - 24332 PB - Springer Science and Business Media LLC AN - OPUS4-58611 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Huo, Wenjie A1 - Schmies, Lennart A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, Katinka T1 - Prediction of mean strain from laser beam welding images and detection of defects via strain curves based on machine learning N2 - With the advancement of machine learning, many predictions and measurements in visual tasks can be achieved by convolutional neural networks (CNNs). Solidification hot cracking is a significant defect in laser beam welding, commonly encountered in practical applications. Existing theories indicate that the formation of cracks is closely related to strain accumulation near the solidification front. In this paper, we first leverage supervised Regression networks to design CNNs that achieve real-time average strain estimation for each frame in the collected welding videos. Two different architectures are proposed and compared: the first model stacks two frames at a set interval and feeds them into the network, while the second model extracts image features individually and predicts the results by calculating the correlation between them. Each network has its own advantages in Terms of computational efficiency and accuracy. Finally, we further train a multilayer perceptron (MLP) classification model that can detect the occurrence of cracks based on the predicted strain behaviors. KW - Laser beam welding KW - Mean strain prediction KW - Solidification cracking detection Convolutional neural networks KW - Convolutional neural networks PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-644495 DO - https://doi.org/10.1016/j.optlastec.2025.113975 SN - 0030-3992 VL - 192, Part F SP - 1 EP - 8 PB - Elsevier Ltd. AN - OPUS4-64449 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Huo, Wenjie A1 - Schmies, Lennart A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, Katinka T1 - An illumination based backdoor attack against crack detection systems in laser beam welding N2 - Deep neural networks (DNNs) have been wildly used in engineering and have achieved state-of-the-art performance in prediction and measurement tasks. A solidification crack is a serious fault during laser beam welding and it has been proven to be successfully detected using DNNs. Recently, research on the security of DNNs is receiving increasing attention because it is necessary to explore the reliability of DNNs to avoid potential security risks. The backdoor attack is a serious threat, where attackers aim to inject an inconspicuous pattern referred to as trigger into a small portion of training data, resulting in incorrect predictions in the reference phase whenever the input contains the trigger. In this work, we first generate experimental data containing actual cracks in the welding laboratory for training a crack detection model. Then, targeting this scenario, we design a new type of backdoor attack to induce the model to predict the crack as a normal state. Considering the stealthiness of the attack, a common phenomenon during the welding process, illumination, is used as the backdoor trigger. Experimental results demonstrate that the proposed method can successfully attack the crack detection system and achieve over 90% attack success rate on the test set. T2 - 8th ML4CPS 2025 – Machine Learning for Cyber-Physical Systems CY - Berlin, Germany DA - 06.03,2025 KW - System security KW - Welding crack detection KW - Backdoor attack KW - Deep neural networks PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-651357 DO - https://doi.org/10.24405/20021 VL - 2025 SP - 12 EP - 21 PB - Universitätsbibliothek der HSU/UniBw H CY - Hamburg AN - OPUS4-65135 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -