TY - GEN A1 - Lehrer, Tobias A1 - Kaps, Arne A1 - Daiminger, Tobias A1 - Lepenies, Ingolf A1 - Duddeck, Fabian A1 - Wagner, Marcus T1 - Analysis of Global Sensitivities for One-Step and Multi-step Deep-Drawing Simulations T2 - 16th German LS-DYNA Forum : October 11-13, 2022, Bamberg, Germany and online : Book of Abstracts N2 - In practical use cases, simulation engineers are confronted with uncertainties in the simulation parameters. Normally, trust in a model is built from experience, practical assumptions, and parameter studies. This approach, though, is based on the assumption that few parameter combinations are sufficient to represent the whole design space. This lacks an appraisable mathematical basis. To get insights into which parameters most strongly affect the results, a global sensitivity analysis can be conducted [2, 3]. The results are utilized to rank the most influential parameters and to filter less relevant ones. This gives feedback which improved set of input data will lead to more certainty in the simulation results. To enable this in the framework of multi-fidelity analysis and optimization, we compare here global sensitivities and uncertainties of the implicit One-Step approach (low-fidelity) with those of the explicit multi-step deep drawing approach (high-fidelity). Y1 - 2022 UR - https://www.dynamore.de/de/fortbildung/konferenzen/vergangene/16-ls-dyna-forum-2022/2022-proceedings.pdf SN - 978-3-9816215-8-7 SP - 117 EP - 118 PB - DYNAmore GmbH CY - Stuttgart ER - TY - JOUR A1 - Lehrer, Tobias A1 - Kaps, Arne A1 - Lepenies, Ingolf A1 - Duddeck, Fabian A1 - Wagner, Marcus T1 - Classification and regression models for drawability assessment in deep drawing JF - International Journal of Material Forming Y1 - 2023 IS - Noch nicht erschienen PB - Springer ER - 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 - TY - JOUR A1 - Lehrer, Tobias A1 - Kaps, Arne A1 - Lepenies, Ingolf A1 - Raponi, Elena A1 - Wagner, Marcus A1 - Duddeck, Fabian T1 - Complementing Drawability Assessment of Deep-Drawn Components with Surrogate-Based Global Sensitivity Analysis JF - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering N2 - In the early-stage development of sheet metal parts, key design properties of new structures must be specified. As these decisions are made under significant uncertainty regarding drawing configuration changes, they sometimes result in the development of new parts that, at a later design stage, will not be drawable. As a result, there is a need to increase the certainty of experience-driven drawing configuration decisions. Complementing this process with a global sensitivity analysis can provide insight into the impact of various changes in drawing configurations on drawability, unveiling cost-effective strategies to ensure the drawability of new parts. However, when quantitative global sensitivity approaches, such as Sobol's method, are utilized, the computational requirements for obtaining Sobol indices can become prohibitive even for small application problems. To circumvent computational limitations, we evaluate the applicability of different surrogate models engaged in computing global design variable sensitivities for the drawability assessment of a deep-drawn component. Here, we show in an exemplary application problem, that both a standard kriging model and an ensemble model can provide commendable results at a fraction of the computational cost. Moreover, we compare our surrogate models to existing approaches in the field. Furthermore, we show that the error introduced by the surrogate models is of the same order of magnitude as that from the choice of drawability measure. In consequence, our surrogate models can improve the cost-effective development of a component in the early design phase. KW - sheet metal forming KW - deep drawing KW - global sensitivity analysis KW - variance-based sensitivity analysis KW - metamodeling Y1 - 2024 U6 - https://doi.org/10.1115/1.4065143 SN - 2332-9025 SP - 1 EP - 10 PB - ASME ER -