TY - JOUR A1 - Lehrer, Tobias A1 - Kaps, Arne A1 - Lepenies, Ingolf A1 - Duddeck, Fabian A1 - Wagner, Marcus T1 - 2S-ML: A simulation-based classification and regression approach for drawability assessment in deep drawing JF - International Journal of Material Forming N2 - New structural sheet metal parts are developed in an iterative, time-consuming manner. To improve the reproducibility and speed up the iterative drawability assessment, we propose a novel low-dimensional multi-fidelity inspired machine learning architecture. The approach utilizes the results of low-fidelity and high-fidelity finite element deep drawing simulation schemes. It hereby relies not only on parameters, but also on additional features to improve the generalization ability and applicability of the drawability assessment compared to classical approaches. Using the machine learning approach on a generated data set for a wide range of different cross-die drawing configurations, a classifier is trained to distinguish between drawable and non-drawable setups. Furthermore, two regression models, one for drawable and one for non-drawable designs are developed that rank designs by drawability. At instantaneous evaluation time, classification scores of high accuracy as well as regression scores of high quality for both regressors are achieved. The presented models can substitute low-fidelity finite element models due to their low evaluation times while at the same time, their predictive quality is close to high-fidelity models. This approach may enable fast and efficient assessments of designs in early development phases at the accuracy of a later design phase in the future. KW - One-step KW - Deep drawing KW - Drawability KW - Meta-modeling KW - Machine learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-64664 N1 - Corresponding author: Tobias Lehrer VL - 16 SP - 1 EP - 17 PB - Springer ER - TY - JOUR A1 - Kaps, Arne A1 - Lehrer, Tobias A1 - Lepenies, Ingolf A1 - Wagner, Marcus A1 - Duddeck, Fabian T1 - Multi-fidelity optimization of metal sheets concerning manufacturability in deep-drawing processes JF - Structural and Multidisciplinary Optimization N2 - Multi-fidelity optimization, which complements an expensive high-fidelity function with cheaper low-fidelity functions, has been successfully applied in many fields of structural optimization. In the present work, an exemplary cross-die deep-drawing optimization problem is investigated to compare different objective functions and to assess the performance of a multi-fidelity efficient global optimization technique. To that end, hierarchical kriging is combined with an infill criterion called variable-fidelity expected improvement. Findings depend significantly on the choice of objective function, highlighting the importance of careful consideration when defining an objective function. We show that one function based on the share of bad elements in a forming limit diagram is not well suited to optimize the example problem. In contrast, two other definitions of objective functions, the average sheet thickness reduction and an averaged limit violation in the forming limit diagram, confirm the potential of a multi-fidelity approach. They significantly reduce computational cost at comparable result quality or even improve result quality compared to a single-fidelity optimization. KW - Multi-fidelity optimization KW - Efficient global optimization KW - Sheet metal forming KW - Deep drawing Y1 - 2023 U6 - https://doi.org/10.1007/s00158-023-03631-8 VL - 66 PB - Springer Nature ER -