@article{66541, abstract = {{Expulsion in resistance spot welding (RSW) causes weld quality fluctuations and increases quality-control effort in high-volume manufacturing. Existing data-driven studies have mainly addressed post-occurrence expulsion detection, process-end classification, or the identification of influencing factors, whereas online monitoring requires short-term risk estimation before the event occurs. In this study, expulsion prediction is formulated as a sliding-window-based pre-expulsion risk estimation task for the currently welded spot. A physics-guided hybrid GRU-XGBoost ensemble is developed to combine temporal learning from dynamic resistance and electrode-force signals with process-physics-related scalar features describing heat input, resistance state, and force response. The framework was evaluated on 2730 valid welds, including 588 expulsion and 2142 non-expulsion welds, using weld-grouped five-fold cross-validation with fold-level working-point selection. The ensemble achieved an area under the ROC curve of 0.945 ± 0.004 and a weld-level recall of 90.6 ± 3.7% at an average false alarm rate of 9.8 ± 0.2%, outperforming both individual branches. For the 533 correctly warned expulsion welds, the median early-warning lead time was 56 ms. These results indicate that online, physically interpretable pre-expulsion risk prediction is feasible under low-false-alarm constraints within the investigated RSW configuration and provide a basis for future adaptive monitoring and control studies.}}, author = {{Yang, Keke and Li, Chong and Beck, Robert and Hein, David and Meschut, Gerson}}, issn = {{1526-6125}}, journal = {{Journal of Manufacturing Processes}}, keywords = {{Resistance spot welding, Expulsion prediction, Physics-guided machine learning, Hybrid ensemble modelling, Process monitoring}}, pages = {{135--153}}, publisher = {{Elsevier BV}}, title = {{{A physics-guided hybrid framework for online pre-expulsion prediction in resistance spot welding}}}, doi = {{10.1016/j.jmapro.2026.07.042}}, volume = {{174}}, year = {{2026}}, } @article{67569, abstract = {{ The increasing use of aluminium alloys and ultra-high-strength steels, such as CP1000, in automotive lightweight design imposes stringent requirements on mechanical joining technologies. Self-piercing riveting (SPR) is widely used in multi-material structures; however, conventional rivet systems are limited ranges of material and sheet thickness. To address this limitation, Kappe et al. introduced versatile self-piercing riveting (V-SPR), which combines an extended punch actuator with a multi-range-capable rivet to enable joining of materials with tensile strengths up to 600 MPa without tool changes. 1 This study presents a comparative investigation of a multi-range-capable rivet for V-SPR and conventional standard rivets in CP1000–aluminium and aluminium–aluminium joints. The analysis focuses on joint formation and mechanical performance under shear-tensile and cross-tensile loading. Metallographic cross-sections are used to characterise interlock formation, minimum die-side material thickness, and failure modes. Mechanical performance is evaluated using quasi-static tests, with emphasis on maximum load and failure mode. The experiments establish a demonstrated joinability matrix for the investigated sheet-thickness combinations using adapted punch and die motions. The results demonstrate the applicability of V-SPR to ultra-high-strength steel–aluminium and aluminium–aluminium joints. For the most industrially relevant combination investigated, St1.5–Al2.0 (CP1000–EN AW-6014), V-SPR achieves mechanical performance comparable to or higher than that of standard SPR while substantially reducing tool complexity through a single multi-range-capable rivet, a reduced die configuration, and fewer stack-specific changeovers. However, the independently driven inner and outer punches and active die increase actuator and control-system complexity. }}, author = {{Kaimann, Pia Katharina and Sarris, J and Beck, Robert and Bobbert, Mathias and Lechner, M and Meschut, Gerson}}, issn = {{1464-4207}}, journal = {{Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications}}, publisher = {{SAGE Publications}}, title = {{{Comparative study of multi-range-capable rivet and standard self-piercing rivets in aluminium-aluminium and ultra-high-strength steel-aluminium assemblies}}}, doi = {{10.1177/14644207261497691}}, year = {{2026}}, } @article{57742, author = {{Böhnke, Max and Bielak, Christian Roman and Beck, Robert and Bobbert, Mathias and Meschut, Gerson}}, issn = {{2223-7690}}, journal = {{Friction}}, publisher = {{Tsinghua University Press}}, title = {{{Development of a friction model for the numerical simulation of clinching processes}}}, doi = {{10.26599/frict.2025.9441052}}, year = {{2024}}, } @inproceedings{22225, author = {{Ivanjko, Martin and Beck, Robert and Meschut, Gerson and Zweck, Jonas and Richter, Thomas}}, location = {{Bad Nauheim}}, title = {{{Development of an adaptive, sensor-based FDS-robot-system}}}, year = {{2021}}, } @article{20678, author = {{Bielak, Christian Roman and Böhnke, Max and Beck, Robert and Bobbert, Mathias and Meschut, Gerson}}, journal = {{Journal of Advanced Joining Processes. }}, keywords = {{Clinching, process simulation, FEM, pre-straining, sensitivity analysis}}, publisher = {{Elsevier}}, title = {{{Numerical analysis of the robustness of clinching process considering the pre-forming of the parts }}}, doi = {{https://doi.org/10.1016/j.jajp.2020.100038}}, year = {{2020}}, }