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

Effect of a strain rate dependent material modeling of a steel on the prediction accuracy of a numerical deep drawing process - Vallaster, Eva; Wiesenmayer, Sebastian; Merklein, Marion (Production Engineering, 2023)
In the production of sheet metal components, batch and process fluctuations cause deviations in the resulting component properties, which often lead to production rejects. To counteract this inline, the computing time for predicting the process result and optimizing the process parameters must be very short, which is why analytical models are advantageous. A large database is usually required for modeling, and numerical simulations are well suited for generating it. The stamping velocity is a process parameter possibly varying, but strain rate dependency of the material often is neglected in numerical simulations. The objective of this study is to analyze the effects of strain rate dependent material modeling on the simulation accuracy of a sheet metal forming process. Therefore, uniaxial tensile tests and layer compression tests at different strain rates are conducted on the steel HC340LA. Based on this, the material behavior is captured in a strain rate dependent material card, which is used for the numerical simulation of a deep drawing process of a geometry with complex shape. For the validation of the model, experiments are carried out and being compared with the computational results in terms of force–displacement curves and part geometry. Furthermore, numerical investigations are used to analyze if drawbead height and blankholder force have an influence on the strain rate distribution and whether this affects the process forc