An approach for a metamodel-based consideration of a process chain when mechanically joining sheet metal components - Neumann, Jonas; Kappis, Lukas; Lontsi, Seraphin Tsi-Nda; Ludwig, Jean-Patrick; Ramaiya, Umang Bharatkumar; Scharr, Christian; Vallaster, Eva; Flügge, Wilko; Meschut, Gerson; Merklein, Marion (15th Forming Technology Forum, 2024)
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.<br

Data driven prognosis of clinch joints in multi-material design - Ludwig, Jean-Patrick; Lontsi, Seraphin Tsi-Nda; Neumann, Jonas; Kappis, Lukas; Scharr, Christian; Flügge, Wilko; Merklein, Marion; Meschut, Gerson (ESAFORM2025, 2025)
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 model

Machine learning modeling of a deep drawing process for predicting resulting component properties after springback - Neumann, Jonas; Ramaiya, Umang Bharatkumar; Merklein, Marion (SheMet2025, 2025)
Abstract. Batch and process fluctuations in the fabrication of sheet metal components can lead to variations in product properties, potentially impacting subsequent manufacturing stages and increasing rejection rates. Identifying and analyzing these deviations is essential to maintain consistent product quality. To counteract such variations, a metamodel for a process chain, including deep drawing, clamping, and clinching, is proposed. This work presents a machine learning (ML) modeling approach for a deep drawing process with an s-rail tool to predict key properties such as effective plastic strain, maximum principal strain, von Mises stress, and residual sheet thickness after springback. The goal was to present a feasible approach for the ML modeling of the investigated deep drawing process and assess prediction qualities across different component sections. It was demonstrated that, with a moderate number of simulations, the predictive capabilities of the models for the forecasted parameters, as measured by the normalized root mean square error, averaged no more than just below 12 percent. However, certain regions, such as critical ends of the flange areas and transitions or straight sections near curved areas, had worse prediction quality compared to other sections. These regions were highlighted for future research, which should focus on incorporating additional features into the ML training process to improve predictions in these identified sections. In a broader scope, this ML modeling for the deep drawing process could be combined with ML modeling of subsequent processes, such as clamping and clinching, to develop a holistic metamodel for the entire process chai
Keywords: Sheet Metal; Deep Drawing; Machine Learning; ML Modeling