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 -