@article{65620,
abstract = {{Abstract
The design of clinch joints is a cost- and time-intensive iterative process due to the complex relationships between tool and process parameters and the resulting joint properties. To address this, this contribution proposes a novel hybrid workflow that combines knowledge- and data-based approaches. Relationships are categorized based on their knowledge quality and the need for a quantitative prediction. Well-established, generalizable relationships are formalized in an ontology as design guidelines (no quantification required) or SWRL rules (quantification required) to model expert knowledge. In contrast, hard-to-formalize or not-fully-understood relationships are treated with regression models for continuous or classification models for binary criteria. These approaches are combined in a generic user interface (GUI), where the ontology can be accessed using predefined SPARQL queries to select and adapt parameters using expert knowledge. These parameters are then used as input for the metamodels. The developed workflow is evaluated on two exemplary joining tasks to illustrate, how designers can retrieve similar prior joints, adapt parameters using the encoded design rules and predict resulting joint properties under varying process conditions. In summary, the combination of ontology and metamodels facilitates the transition of trial and error into an efficient, documentable design process.}},
author = {{Einwag, Jonathan-Markus and Wiemer, Maximilian and Wartzack, Sandro and Goetz, Stefan}},
issn = {{2731-6564}},
journal = {{Discover Mechanical Engineering}},
number = {{1}},
publisher = {{Springer Science and Business Media LLC}},
title = {{{A hybrid knowledge based and data based approach for efficient clinch joint design}}},
doi = {{10.1007/s44245-026-00230-x}},
volume = {{5}},
year = {{2026}},
}
@article{61524,
author = {{Einwag, Jonathan-Markus and Steinfelder, Christian and Wartzack, Sandro and Brosius, Alexander and Goetz, Stefan}},
issn = {{1526-6125}},
journal = {{Journal of Manufacturing Processes}},
pages = {{179--191}},
publisher = {{Elsevier BV}},
title = {{{From simulation to metamodel to experiment: Evaluating the prediction accuracy of polynomial regression models for clinch joint properties}}},
doi = {{10.1016/j.jmapro.2025.09.059}},
volume = {{154}},
year = {{2025}},
}
@inproceedings{60198,
abstract = {{Abstract. The growing significance of lightweight design, reveals drawbacks of conventional joining processes such as welding, which are known to consume a considerable amount of energy. This fosters the use of mechanical joining processes including clinching. However, the lack of universally applicable design methods results in a cost- and time-intensive design process. The utilization of machine learning methods can overcome these drawbacks. To ensure a reliable clinch joint design, inherent uncertainties of the design parameter such as tool deviations need to be considered in the design process. Varying distributions of design parameters, due to changes in the manufacturing process, can lead to high-computational effort in recalculating the resulting clinch joint properties with numerical simulations. Current metamodel-based methods for consideration of inherent uncertainties within the design parameters do not investigate the transferability of metamodels to different distributions of design parameters, which can lead to incorrect predictions. Therefore, this contribution investigates the performance of several metamodels on differently distributed design parameters. The obtained results indicate that metamodels demonstrate the best performance when training and evaluation distributions are identical and that polynomial regression models perform best on disparate distributions, when trained on uniform distributions.}},
author = {{Einwag, Jonathan-Markus and Mayer, Yannik and Goetz, Stefan and Wartzack, Sandro}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Impact of the parameter distribution on the predictive quality of metamodels for clinch joint properties}}},
doi = {{10.21741/9781644903551-35}},
volume = {{52}},
year = {{2025}},
}
@article{54797,
abstract = {{Focusing on upcoming challenges in lightweight design, such as increasing emission targets or novel multimaterial connections, versatile applicable and environmentally friendly production technologies are crucial. In this context, mechanical joining technology clinching offers a fast and energy-efficient procedure for assembling sheet metals, being a proper alternative to established joining methods, such as spot welding. However, the design of clinch points is a challenge, which is partly supported by numerical or data-based approaches for optimal tool dimensions assuring proper joint characteristics. While this is usually done for an ideal environment, real joining processes are characterized by multiple inevitably varying parameters, e.g. of the material, which have a significant impact on the quality of clinch points. Therefore, this contribution addresses the current gap by analyzing the effect of parameter variations or uncertainties on the resulting joint characteristics and studying the impact of the nominal tool design. Thus, an efficient meta-model-based variation simulation procedure is proposed and used for analyzing the effect of different tool design configurations and variation scenarios. Based on the results, it was found that varying process parameters have a strong impact on the resulting joint characteristics, whereby the effect significantly depends on the nominal tool design. This reveals the potential for a robust tool design and implies that the nominal tool design and the tolerancing of parameters should be done simultaneously for a reliable virtual joining point design without extensive iterations and physical tests.}},
author = {{Zirngibl, C and Goetz, S and Wartzack, S}},
issn = {{0954-4089}},
journal = {{Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering}},
publisher = {{SAGE Publications}},
title = {{{Influence of process variations on clinch joint characteristics considering the effect of the nominal tool design}}},
doi = {{10.1177/09544089241259347}},
year = {{2024}},
}
@inproceedings{56682,
author = {{Einwag, Jonathan-Markus and Goetz, Stefan and Wartzack, Sandro}},
booktitle = {{DS 133: Proceedings of the 35th Symposium Design for X (DFX2024)}},
publisher = {{The Design Society}},
title = {{{Approach for the Reliable and Virtual Design of Mechanical Joints in an Uncertain Environment}}},
doi = {{10.35199/dfx2024.23}},
year = {{2024}},
}
@article{58342,
author = {{Bode, Christoph and Goetz, Stefan and Wartzack, Sandro}},
issn = {{2212-8271}},
journal = {{Procedia CIRP}},
pages = {{151--156}},
publisher = {{Elsevier BV}},
title = {{{On the transferability of nominal surrogate models to uncertainty consideration of clinch joint characteristics}}},
doi = {{10.1016/j.procir.2024.10.027}},
volume = {{129}},
year = {{2024}},
}
@inproceedings{60304,
abstract = {{The focus towards multi-material and lightweight assemblies, driven by legal requirements on reducing emissions and energy consumptions, reveals important drawbacks and disadvantages of established joining processes, such as welding. In this context, mechanical joining technologies, such as clinching, are becoming more and more relevant especially in the automotive industry. However, the availability of only few standards and almost none systematic design methods causes a still very time- and cost-intensive assembly development process considering mainly expert knowledge and a considerable amount of experimental studies. Motivated by this, the presented work introduces a novel approach for the methodical design and dimensioning of mechanically clinched assemblies. Therefore, the utilization of regression models, such as machine learning algorithms, combined with manufacturing knowledge ensures a reliable estimation of individual clinched joint characteristics. In addition, the implementation of an engineering workbench enables the following data-driven and knowledge-based generation of high-quality initial assembly designs already in early product development phases. In a subsequent analysis and adjustment, these designs are being improved while guaranteeing joining safety and loading conformity. The presented results indicate that the methodological approach can pave the way to a more systematic design process of mechanical joining assemblies, which can significantly shorten the required number of iteration loops and therefore the product development time.}},
author = {{Zirngibl, Christoph and Martin, Sven and Steinfelder, Christian and Schleich, Benjamin and Tröster, Thomas and Brosius, Alexander and Wartzack, Sandro}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
keywords = {{Joining, Structural Analysis, Machine Learning}},
location = {{Erlangen-Nürnberg}},
publisher = {{Materials Research Forum LLC}},
title = {{{Methodical approach for the design and dimensioning of mechanical clinched assemblies}}},
doi = {{10.21741/9781644902417-23}},
volume = {{25}},
year = {{2023}},
}
@inproceedings{34415,
abstract = {{Challenges in the development of resource-efficient lightweight designs, such as emission and cost targets in production, lead to an increasing demand for environmentally friendly and fast joining processes. Therefore, cold-forming mechanical joining techniques provide an energy-efficient alternative in comparison to established processes, such as spot welding. However, to ensure a sufficient reliability of the product design, not only the selection of an appropriate manufacturing and joining method, but also the suitable dimensioning and validation of the entire joining process is a crucial step. In this context, thermal processes offer a large number of design principles while mechanical joining methods mainly require extensive experimental tests and the inclusion of expert knowledge. Although few contributions already investigated the data-based analysis of mechanical joints, a system for the requirement- and manufacturing-oriented dimensioning of joining components, such as different profiles and blanks, in combination with the estimation of joint properties is not available yet. Motivated by this lack, this contribution introduces an engineering workbench for the support of design engineers in the early development phases of the knowledge and data-based design of mechanical joining connections using clinching as an example. In this regard, the approach is demonstrated involving a similar material and sheet thickness combination with static loads.}},
author = {{Zirngibl, Christoph and Sauer, Christopher and Schleich, Benjamin and Wartzack, Sandro}},
booktitle = {{Volume 2: 42nd Computers and Information in Engineering Conference (CIE)}},
publisher = {{American Society of Mechanical Engineers}},
title = {{{Knowledge and Data-Based Design and Dimensioning of Mechanical Joining Connections}}},
doi = {{10.1115/detc2022-89172}},
year = {{2022}},
}
@article{34417,
abstract = {{Given strict emission targets and legal requirements, especially in the automotive industry, environmentally friendly and simultaneously versatile applicable production technologies are gaining importance. In this regard, the use of mechanical joining processes, such as clinching, enable assembly sheet metals to achieve strength properties similar to those of established thermal joining technologies. However, to guarantee a high reliability of the generated joint connection, the selection of a best-fitting joining technology as well as the meaningful description of individual joint properties is essential. In the context of clinching, few contributions have to date investigated the metamodel-based estimation and optimization of joint characteristics, such as neck or interlock thickness, by applying machine learning and genetic algorithms. Therefore, several regression models have been trained on varying databases and amounts of input parameters. However, if product engineers can only provide limited data for a new joining task, such as incomplete information on applied joining tool dimensions, previously trained metamodels often reach their limits. This often results in a significant loss of prediction quality and leads to increasing uncertainties and inaccuracies within the metamodel-based design of a clinch joint connection. Motivated by this, the presented contribution investigates different machine learning algorithms regarding their ability to achieve a satisfying estimation accuracy on limited input data applying a statistically based feature selection method. Through this, it is possible to identify which regression models are suitable to predict clinch joint characteristics considering only a minimum set of required input features. Thus, in addition to the opportunity to decrease the training effort as well as the model complexity, the subsequent formulation of design equations can pave the way to a more versatile application and reuse of pretrained metamodels on varying tool configurations for a given clinch joining task.}},
author = {{Zirngibl, Christoph and Schleich, Benjamin and Wartzack, Sandro}},
issn = {{2673-2688}},
journal = {{AI}},
keywords = {{Industrial and Manufacturing Engineering}},
number = {{4}},
pages = {{990--1006}},
publisher = {{MDPI AG}},
title = {{{Estimation of Clinch Joint Characteristics Based on Limited Input Data Using Pre-Trained Metamodels}}},
doi = {{10.3390/ai3040059}},
volume = {{3}},
year = {{2022}},
}
@article{34249,
abstract = {{The trend towards lightweight design, driven by increasingly stringent emission targets, poses challenges to conventional joining processes due to the different mechanical properties of the joining partners used to manufacture multi-material systems. For this reason, new versatile joining processes are in demand for joining dissimilar materials. In this regard, pin joining with cold extruded pin structures is a relatively new, two-stage joining process for joining materials such as high-strength steel and aluminium as well as steel and fibre-reinforced plastic to multi-material systems, without the need for auxiliary elements. Due to the novelty of the process, there are currently only a few studies on the robustness of this joining process available. Thus, limited statements on the stability of the joining process considering uncertain process conditions, such as varying material properties or friction values, can be provided. Motivated by this, the presented work investigates the influence of different uncertain process parameters on the pin extrusion as well as on the joining process itself, carrying out a systematic robustness analysis. Therefore, the methodical approach covers the complete process chain of pin joining, including the load-bearing capacity of the joint by means of numerical simulation and data-driven methods. Thereby, a deeper understanding of the pin joining process is generated and the versatility of the novel joining process is increased. Additionally, the provision of manufacturing recommendations for the forming of pin joints leads to a significant decrease in the failure probability caused by ploughing or buckling effects.}},
author = {{Römisch, David and Zirngibl, Christoph and Schleich, Benjamin and Wartzack, Sandro and Merklein, Marion}},
issn = {{2504-4494}},
journal = {{Journal of Manufacturing and Materials Processing}},
keywords = {{Industrial and Manufacturing Engineering, Mechanical Engineering, Mechanics of Materials}},
number = {{5}},
publisher = {{MDPI AG}},
title = {{{Robustness Analysis of Pin Joining}}},
doi = {{10.3390/jmmp6050122}},
volume = {{6}},
year = {{2022}},
}
@article{34414,
abstract = {{Given a steadily increasing demand on multi-material lightweight designs, fast and cost-efficient production technologies, such as the mechanical joining process clinching, are becoming more and more relevant for series production. Since the application of such joining techniques often base on the ability to reach similar or even better joint loading capacities compared to established joining processes (e.g., spot welding), few contributions investigated the systematic improvement of clinch joint characteristics. In this regard, the use of data-driven methods in combination with optimization algorithms showed already high potentials for the analysis of individual joints and the definition of optimal tool configurations. However, the often missing consideration of uncertainties, such as varying material properties, and the related calculation of their impact on clinch joint properties can lead to poor estimation results and thus to a decreased reliability of the entire joint connection. This can cause major challenges, especially for the design and dimensioning of safety-relevant components, such as in car bodies. Motivated by this, the presented contribution introduces a novel method for the robust estimation of clinch joint characteristics including uncertainties of varying and versatile process chains in mechanical joining. Therefore, the utilization of Gaussian process regression models is demonstrated and evaluated regarding the ability to achieve sufficient prediction qualities.}},
author = {{Zirngibl, Christoph and Schleich, Benjamin and Wartzack, Sandro}},
issn = {{0268-3768}},
journal = {{The International Journal of Advanced Manufacturing Technology}},
keywords = {{Industrial and Manufacturing Engineering, Computer Science Applications, Mechanical Engineering, Software, Control and Systems Engineering}},
publisher = {{Springer Science and Business Media LLC}},
title = {{{Robust estimation of clinch joint characteristics based on data-driven methods}}},
doi = {{10.1007/s00170-022-10441-7}},
year = {{2022}},
}
@article{30100,
abstract = {{Since the application of mechanical joining methods, such as clinching or riveting, offers a robust solution for the generation of advanced multi-material connections, the use in the field of lightweight designs (e.g. automotive industry) is steadily increasing. Therefore, not only the design of an individual joint is required but also the dimensioning of the entire joining connection is crucial. However, in comparison to thermal joining techniques, such as spot welding, the evaluation of the joints’ resistance against defined requirements (e.g. types of load, minimal amount of load cycles) mainly relies on the consideration of expert knowledge, a few design principles and a small amount of experimental data. Since this generally implies the involvement of several domains, such as the material characterization or the part design, a tremendous amount of data and knowledge is separately generated for a certain dimensioning process. Nevertheless, the lack of formalization and standardization in representing the gained knowledge leads to a difficult and inconsistent reuse, sharing or searching of already existing information. Thus, this contribution presents a specific ontology for the provision of cross-domain knowledge about mechanical joining processes and highlights two potential use cases of this ontology in the design of clinched and pin joints.}},
author = {{Zirngibl, Christoph and Kügler, Patricia and Popp, Julian and Bielak, Christian Roman and Bobbert, Mathias and Drummer, Dietmar and Meschut, Gerson and Wartzack, Sandro and Schleich, Benjamin}},
issn = {{0944-6524}},
journal = {{Production Engineering}},
keywords = {{Industrial and Manufacturing Engineering, Mechanical Engineering}},
publisher = {{Springer Science and Business Media LLC}},
title = {{{Provision of cross-domain knowledge in mechanical joining using ontologies}}},
doi = {{10.1007/s11740-022-01117-y}},
year = {{2022}},
}
@article{34244,
author = {{Kappe, Fabian and Zirngibl, Christoph and Schleich, Benjamin and Bobbert, Mathias and Wartzack, Sandro and Meschut, Gerson}},
issn = {{1526-6125}},
journal = {{Journal of Manufacturing Processes}},
keywords = {{Industrial and Manufacturing Engineering, Management Science and Operations Research, Strategy and Management}},
pages = {{1438--1448}},
publisher = {{Elsevier BV}},
title = {{{Determining the influence of different process parameters on the versatile self-piercing riveting process using numerical methods}}},
doi = {{10.1016/j.jmapro.2022.11.019}},
volume = {{84}},
year = {{2022}},
}
@article{30650,
abstract = {{Due to increasingly strict emission targets and regulatory requirements, especially for companies in the transport industry, the demand for multi-material-systems is continuously rising in order to lower energy consumption. In this context, mechanical joining processes offer an environmentally friendly and flexible alternative to established joining methods, especially in the field of lightweight design. For example, cold-formed cylindrical pin structures show high potentials in joining multi-material-systems without auxiliary elements. The pin structures are joined either by pressing them directly into the joining partner or by caulking with a pre-punched part. However, to evaluate the strength of the joint and to ensure the joining reliability for versatile processes, such as changing joining partners or batch variations, engineering designers currently have only limited design principles available compared to thermal joining processes. Consequently, the design of an optimal pin joint requires cost- and time-intensive experimental investigations and adjustments to design or process parameters. As a solution, data-driven methods offer procedures for structuring data and identifying dependencies between varying process parameters and resulting pin structure characteristics. Motivated by this, the paper presents an approach for the data-driven analysis of cold-formed pin structures and offers a deeper understanding of how versatile processes affect the pin characteristics. Therefore, the application of an intelligent design of experiment in combination with several machine learning methods enable the setup of a best-fitting meta-model. Resulting, the determination of a mathematical model provides the opportunity to accurately estimate the pin height considering only relevant geometrical and process parameters with a prediction quality of 95 %.}},
author = {{Römisch, D. and Zirngibl, C. and Schleich, B. and Wartzack, S. and Merklein, M.}},
journal = {{IOP Conference Series: Materials Science and Engineering}},
pages = {{012077}},
title = {{{Data-driven analysis of cold-formed pin structure characteristics in the context of versatile joining processes}}},
doi = {{10.1088/1757-899X/1157/1/012077}},
volume = {{1157}},
year = {{2021}},
}
@article{30696,
author = {{Zirngibl, C. and Schleich, B. and Wartzack, S.}},
journal = {{Proceedings of the Design Society}},
pages = {{521}},
title = {{{Approach for the automated and data-based design of mechanical joints}}},
doi = {{10.1017/pds.2021.52}},
volume = {{1}},
year = {{2021}},
}
@article{30700,
author = {{Zirngibl, C. and Dworschak, F. and Schleich, B. and Wartzack, S.}},
journal = {{Production Engineering}},
title = {{{Application of reinforcement learning for the optimization of clinch joint characteristics}}},
doi = {{10.1007/s11740-021-01098-4}},
year = {{2021}},
}
@article{30695,
abstract = {{Due to their cost-efficiency and environmental friendliness, the demand of mechanical joining processes is constantly rising. However, the dimensioning and design of joints and suitable processes are mainly based on expert knowledge and few experimental data. Therefore, the performance of numerical and experimental studies enables the generation of optimized joining geometries. However, the manual evaluation of the results of such studies is often highly time-consuming. As a novel solution, image segmentation and machine learning algorithm provide methods to automate the analysis process. Motivated by this, the paper presents an approach for the automated analysis of geometrical characteristics using clinching as an example. }},
author = {{Zirngibl, C. and Schleich, B.}},
journal = {{Key Engineering Materials}},
pages = {{105}},
title = {{{Approach for the automated analysis of geometrical clinch joint characteristics}}},
doi = {{10.4028/www.scientific.net/KEM.883.105}},
volume = {{883 KEM}},
year = {{2021}},
}
@article{30710,
author = {{Zirngibl, C. and Schleich, B. and Wartzack, S.}},
journal = {{Proceedings of the 31st Symposium Design for X (DFX2020)}},
title = {{{Potentiale datengestützter Methoden zur Gestaltung und Optimierung mechanischer Fügeverbindungen}}},
doi = {{10.35199/dfx2020.8}},
year = {{2020}},
}