Autor/innen
Bouwe Verkens, 1) KU Leuven, Department of Mechanical Engineering, Jan Pieter de Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium; 2) FlandersMake@KU Leuven, Belgium; Pei-Pei Li, Department of Civil Engineering, Beijing University of Technology, Beijing 100124, China; L. Bogaerts, 1) KU Leuven, Department of Mechanical Engineering, Jan Pieter de Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium; 2) FlandersMake@KU Leuven, Belgium; Matthias Faes, Chair for Reliability Engineering, TU Dortmund University, Leonhard-Euler-Straße 5, Dortmund 44227, Germany; Patrick Van Rymenant, KU Leuven, Department of Mechanical Engineering, Jan Pieter de Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium; David Moens, LMSD, Department of Mechanical Engineering, KU Leuven, Heverlee 3001, Belgium
Über dieses Buch
Accurate multi-physics finite element modelling of resistance spot welding (RSW) remains challenging due to the uncertainties in contact and material parameters that shape the coupled thermal–electrical–mechanical process. This study introduces a Bayesian calibration framework, in contrast to the deterministic parameter optimisation approaches commonly adopted in existing RSW literature, which neglect the inherent variability of the welding process. The proposed framework performs calibration using multivariate model outputs, combining weld nugget geometry with geometrical and temporal attributes (El Ouafi et al., 2012) extracted from process signals such as dynamic resistance and electrode displacement. These features provide physically interpretable information on process evolution while reducing redundancy and correlation relative to full time-series data, enabling more effective parameter identifiability than nugget-only calibration. The methodology is applied to a 2D axisymmetric finite element model of RSW implemented in Simufact. In addition to the electrical and thermal contact parameters, a modified thermal conductivity formulation is introduced and treated as an unknown parameter to be inferred. Bayesian inference is performed using the Streamlined Bayesian Active Learning Cubature (SBALC) method (Li et al., 2025), with experimental multi-physics data (including nugget diameter and height, as well as process signals) used to infer posterior distributions for key modelling input variables.
Veröffentlicht
21.08.2026
Copyright (c) 2026 Matthias Faes, Michael Beer, Julian Behrend, Marco Broccardo, Guohai Chen, Hanshu Chen, Jieyu Chen, Fang Cong, Henrik Ebel, Peter Eberhard, Andrea Franchini, Zhuojia Fu, Yanzhao Gu, Michael Hanss, Hui Huang, Md Maidul Islam, Jafar Jafari-Asl, Balsa Jovanovic, Tom Könecke, Olga Kosheleva, Vladik Kreinovich, Hao-Ming Li, Pei-Pei Li, Qinghe Li, Ruikun Li, Stefano Marelli, Rafi L. Muhanna, Robert L. Mullen, Chiara Nardin, Lukáš Novák, Shenghao Piao, Mario Rosenfelder, Styfen Schär, Jan Schneider, Wei Shi, Panagiotis Spyridis, Bruno Sudret, Ruben Van Coile, Bo-Yu Wang, Dixiong Yang, Xuan-Yi Zhang, Zi-Xin Zhang, Bo-Qi Zhao, Yan-Gang Zhao, Zhibao Zheng, Baihong Zhong, Zhaowei Zhou, Marcos A. Valdebenito, Chao Dang, Lan-Xin Li, Marius Bittner, Parth Tambat, Marco Behrendt, Zhao Liu, Ping Zhu, Zlatan Dimitrov, Petar O. Hristov, Meng-Ze Lyu, Yang-Yi Liu, Jian-Bing Chen, Qitian Lu, Miroslav Vořechovský, Ekaterina Auer, Lorenz Gillner, Julien Alexandre dit Sandretto, Binod K. Yadav, Ghifari Adam Faza, Hans Hallez, David Moens, Reza Naeimaei, Steffen Schön, Elisabeth Ötsch, Hans Neuner, Zhanhua Liang, Jingwen Song, Michael Desch, Mehdi Modares, Valentin Nikolov, Paulo Lucas Figueiredo, José Torres Farinha, Hugo Raposo, António J. Marques Cardoso, Alice do Carmo Duarte Rodrigues, Paula Gonçalves, Peihan Chen, Xinyu Zhu, Pengfei Wei, Kaiwen Li, Mansoureh Shahabi, Y. F. Cui, Johannes O. Royset, Salvatore Russotto, Mario Di Paola, Antonina Pirrotta, Til Lux, Johannes Sundheim, Tania Feiri, Udo Wiens, Marcus Ricker, F. Niklas Schietzold, Felix Harazin, Yoshi Diepelt, Wolfgang Graf, Michael Kaliske, Rongyao Song, Changcong Zhou, Xuanyi Zhao, Weiping Zhang, Juntao Hu, Yu Chen, Edoardo Patelli, Tingting Sun, Jianbing Chen, Bouwe Verkens, L. Bogaerts, Patrick Van Rymenant, Maximilian Schweizer, Marc Fina, Werner Wagner, Steffen Freitag, George Stefanou, Dimitros Savvas, Panagiotis Gavalas, Luigi Schiano, Razhan S. Ahmed, Oleksandr Al-Shboul, David Ringeloth, Vladislav Gudžulić, Stefanie Schoen, Jelena Bijeljić, Ernst Niederleithinger, Iurie Curoşu, Gerrit E. Neu, Jia-Hui Fu, Yi Luo, De-Cheng Feng, Xiao-Qiu Ai, Luyi Li, Hao Wang, Tairan Wang, Sifeng Bi, Emily Zeller (Kapitelautor/in)
Zitationsvorschlag
Bayesian calibration of resistance spot welding models using temporal process features and streamlined active learning. (2026). In
REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World (pp. 392-405). TUDObooks.
https://doi.org/10.17877/tudobooks-11.171
