@article{64985,
abstract = {{Modern industrial development has necessitated a wide range of joining technologies. Self-pierce riveting has become a prevalent technique for sheet metal assembly, especially in automotive applications. Achieving proper joint geometry and adequate load-bearing capacity depends on appropriate tool selection and precise process control. Material properties and condition also play a significant role in process performance. To accommodate the inevitable variations in component characteristics during production, a robust and stable joining process is essential. The study focuses on investigating the influence of preformed joining partners on the joining process and the joint's load capacity. An EN AW-6014 in T4 condition, as well as an HCT590X, are used as materials for this study. For this purpose, an exemplary process chain consisting of the steps of performing, joining, and shear load testing is studied. Each process step is implemented using an FE model to predict the outcome of subsequent steps. For analysis of the influence of pre-strain, an optimisation software is used to plan and execute variations of the process. These variations are used to create a meta-model that can describe the relationships between pre-forming and characteristic parameters of subsequent process steps. The resulting model is validated by comparing simulation and experimental data. Finally, in a novel approach, the robustness of the presented process chain is analyzed in terms of a tolerable performance level for the joining partners.}},
author = {{Ludwig, Jean-Patrick and Tolke, Emil and Schlichter, Malte Christian and Bobbert, Mathias and Meschut, Gerson}},
issn = {{2666-3309}},
journal = {{Journal of Advanced Joining Processes}},
keywords = {{Self-pierce riveting, FE modelling, Plastic pre-deformation, Meta modelling}},
publisher = {{Elsevier BV}},
title = {{{Numerical analysis of the robustness of self-pierce riveting with pre-formed joining partners}}},
doi = {{10.1016/j.jajp.2026.100391}},
volume = {{13}},
year = {{2026}},
}
@inproceedings{59876,
abstract = {{Abstract. Clinching is a conventional mechanical joining process used in Multi-Material Design in the automotive sector. To receive the desired geometrical characteristics in clinch joints, correct process design is required. To reduce the cost of finding fitting process parameters, numerical simulation of the joining process can be used to predict the geometrical characteristics, such as interlock, instead of an experimental approach. These numerical simulation models consume computational resources and time. In this paper machine learning is used to find correlations between features of the joining process and geometrical characteristics in the joint. This serves the purpose of predicting the joint’s target values more resource-efficiently. Modelling with machine learning requires a structured dataset with sufficient parameter variation. To create this data base the following procedure was used. For joining partners, a HC340LA steel alloy with 2 mm material thickness was used punch-sided and an EN AW 5182 aluminum alloy with 1.5 mm thickness was used die-sided. For this combination a suitable tool combination and punch distance was experimentally identified. A finite element model was created to reproduce the joining process. For the modelling of the material of both joining partners flow curves determined by Vallaster et al. were used [1]. The punch and die were recreated digitally by opto-electronic measurements and transformed into a mesh suitable for numerical simulation. The model was validated by comparing process values like the maximum force applied by the punch and geometrical values in the joints cross section. Additionally, a process window for suitable punch distances was experimentally determined. Afterwards a variation of 70 different process designs was conducted with variants inside and outside the process window. The results were used for training, testing and validating various machine learning models. All models competed against each other to find the must suited model to predict every geometric value. To ensure good model performances and prevent the model from overfitting, a tenfold cross validation was used for validating the models. Analysis of the results gives the following key findings: i) Good predictability is reached for the interlock and sheet thickness of the joint. ii) Prediction neck thickness showed low error values, but also low correlation. iii)The prediction of those key values for evaluating clinch joint characteristics by machine learning models positively impacts needed resources in comparison to numerical models.}},
author = {{Ludwig, Jean-Patrick and Tsi-Nda Lontsi, Seraphin and Neumann, Jonas and Kappis, Lukas and Scharr, Christian and Flügge, Wilko and Merklein, Marion and Meschut, Gerson}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Data driven prognosis of clinch joints in multi-material design}}},
doi = {{10.21741/9781644903599-157}},
volume = {{54}},
year = {{2025}},
}
@inproceedings{59878,
abstract = {{Abstract. In the development of advanced lightweight automotive solutions, self-piercing riveting (SPR) offers the possibility of joining multi-material structures to fulfil a wide variety of requirements. With regard to the entire process chain, production-related pre-deformations of the parts to be joined can influence the geometric shape and load capacity of SPR joints. Various studies have investigated the influence of pre-stretched sheet materials, in the sense of pre-drawing processes, on the formation of SPR joints. The impact of pre-stretching sheet metals on the formation of their geometrical characteristics and the shear-tensile strength of SPR processes was observed [1]. Pre-rolled semi-finished products are also joined together in mixed material automotive structures, e.g. tailor rolled blanks. This work aims to investigate the influence of pre-rolled joining parts on the geometric formation and load-carrying capacity of SPR joints. For this purpose, sheets of metal are cold-formed using a rolling process to induce a defined strain-hardening state in the material and then joined in various combinations. As the degree of deformation increases, the rolling of samples can lead to minimal accumulation of damage in the sheet materials, which can influence the joint behaviour. The rolling process, as well as the subsequent joining process, are also investigated by FEM. The influence of pre-rolled semi-finished products on the strength of the SPR joints is investigated.}},
author = {{Schlichter, Malte Christian and Harabati, Özcan and Ludwig, Jean-Patrick and Böhnke, Max and Bielak, Christian Roman and Bobbert, Mathias and Meschut, Gerson}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Experimental and numerical investigation of the influence of rolling-induced sheet metal deformation on SPR joints}}},
doi = {{10.21741/9781644903599-148}},
volume = {{54}},
year = {{2025}},
}
@inproceedings{60977,
abstract = {{In the development of advanced lightweight automotive solutions, self-piercing riveting (SPR) offers the possibility of joining multi-material structures to fulfil a wide variety of requirements. With regard to the entire process chain, production-related pre-deformations of the parts to be joined can influence the geometric shape and load capacity of SPR joints. Various studies have investigated the influence of pre-stretched sheet materials, in the sense of pre-drawing processes, on the formation of SPR joints. The impact of pre-stretching sheet metals on the formation of their geometrical characteristics and the shear-tensile strength of SPR processes was observed [1]. Pre-rolled semi-finished products are also joined together in mixed material automotive structures, e.g. tailor rolled blanks. This work aims to investigate the influence of pre-rolled joining parts on the geometric formation and load-carrying capacity of SPR joints. For this purpose, sheets of metal are cold-formed using a rolling process to induce a defined strain-hardening state in the material and then joined in various combinations. As the degree of deformation increases, the rolling of samples can lead to minimal accumulation of damage in the sheet materials, which can influence the joint behaviour. The rolling process, as well as the subsequent joining process, are also investigated by FEM. The influence of pre-rolled semi-finished products on the strength of the SPR joints is investigated.}},
author = {{Schlichter, Malte Christian and Harabati, Özcan and Ludwig, Jean-Patrick and Böhnke, Max and Bielak, Christian Roman and Bobbert, Mathias and Meschut, Gerson}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Experimental and numerical investigation of the influence of rolling-induced sheet metal deformation on SPR joints}}},
doi = {{10.21741/9781644903599-148}},
volume = {{54}},
year = {{2025}},
}
@inproceedings{59237,
abstract = {{Batch and process fluctuations during the fabrication of sheet metal components result in discrepancies in the resulting component properties, affecting subsequent process steps and potentially leading to production rejects. Consequently, the identification of deviations and knowledge of the effects of fluctuations are crucial for achieving consistently high product quality, reducing waste and thus increasing resource efficiency of production processes through countermeasures derived from this. The approach presented to address this is the use of data-driven metamodeling to map entire process chains and predict process parameters in order to compensate for process and batch fluctuation. The investigated process chain consists of the sub-processes deep drawing, clamping and clinching. For each process step, relevant input and output variables are identified, numerical simulation models are created, and subsequently validated. Variant simulations of the sub-processes are conducted and evaluated to generate a database for the metamodeling of the individual process steps. Machine learning techniques are utilized for the automated selection and optimization of learning methods to create models that depict the relationships between input and output variables. Finally, the models for the sub-processes are linked together to form a superordinate metamodel for the entire process chain, with the aim to make inline-process adaptations possible.