TY - JOUR A1 - Huo, W. A1 - Bakir, Nasim A1 - Gumenyuk, Andrey A1 - Rethmeier, Michael A1 - Wolter, K. T1 - Strain Prediction Using Deep Learning during Solidification Crack Initiation and Growth in Laser Beam Welding of Thin Metal Sheets JF - Applied sciences N2 - The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage is that the calculation takes a long time, limiting its suitability to real-time applications. Recently, convolutional neural networks (CNNs) have made impressive achievements in computer vision. To build a good prediction model, the network structure and dataset are two key factors. In this paper, we first create the training and test sets containing welding cracks using the controlled tensile weldability (CTW) test and obtain the real strain fields through the Lucas–Kanade algorithm. Then, two new networks using ResNet and DenseNet as encoders are developed for strain prediction, called StrainNetR and StrainNetD. The results show that the average endpoint error (AEE) of the two networks on our test set is about 0.04, close to the real strain value. The computation time could be reduced to the millisecond level, which would greatly improve efficiency. KW - Convolutional neural network KW - Strain fields prediction KW - Laser beam welding KW - Solidification cracking PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-570565 DO - https://doi.org/10.3390/app13052930 VL - 13 IS - 5 SP - 1 EP - 15 PB - MDPI AN - OPUS4-57056 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 JF - Neural Computing and Applications 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 - CONF A1 - Rethmeier, Michael A1 - Artinov, Antoni A1 - Meng, Xiangmeng A1 - Bakir, Nasim A1 - Üstündag, Ömer A1 - Bachmann, Marcel A1 - Gumenyuk, Andrey T1 - The bulging effect and its relevance in high power laser beam welding N2 - The present work deals with the recently confirmed widening of the weld pool interface, known as a bulging effect, and its relevance in high power laser beam welding. A combined experimental and numerical approach is utilized to study the influence of the bulge on the hot cracking formation and the transport of alloying elements in the molten pool. A technique using a quartz glass, a direct-diode laser illumination, a high-speed camera, and two thermal imaging cameras is applied to visualize the weld pool geometry in the longitudinal section. The study examines the relevance of the bulging effect on both, partial and complete penetration, as well as for different sheet thicknesses ranging from 8 mm to 25 mm. The numerical analysis shows that the formation of a bulge region is highly dependent on the penetration depth and occurs above 10 mm penetration depth. The location of the bulge correlates strongly with the cracking location. The obtained experimental and numerical results reveal that the bulging effect increases the hot cracking susceptibility and limits the transfer of alloying elements from the top of the weld pool to the weld root. T2 - 1st Annual Assembly and Conference of The Welding Federation of Africa (TWF-Africa) CY - Cairo, Egypt DA - 14.03.2023 KW - Laser beam welding KW - Melt pool dinamics PY - 2023 AN - OPUS4-58695 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -