Complementing Drawability Assessment of Deep-Drawn Components With Surrogate-Based Global Sensitivity Analysis

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering, vol. 10

Abstract

Abstract 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 (GSA) 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 Gaussian process regression (GPR) model and an ensemble model can provide commendable results at a fraction of the computational cost. We compare our surrogate models to existing approaches in the field. Furthermore, by comparing drawability measures 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.

Authors 6

1. Tobias Lehrer

   Technical University of Munich

   Affiliation as printed

   TUM School of Engineering and Design, Technical University of Munich, Arcisstr. 21, Munich D-80333, Germany

   Technical University of Munich

2. Arne Kaps

   Technical University of Munich

   Affiliation as printed

   TUM School of Engineering and Design, Technical University of Munich, Arcisstr. 21, Munich D-80333, Germany

3. Ingolf Lepenies

   Technische Universität Dresden

   Affiliation as printed

   SCALE GmbH, Pohlandstr. 19, Dresden D-01309, Germany

4. Elena Raponi Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, CA Leiden 2333, Netherlands

   Leiden University

5. Marcus M. Wagner

   OTH Regensburg

   Affiliation as printed

   Department of Mechanical Engineering, Ostbayerische Technische Hochschule Regensburg, Galgenbergstr. 30, Regensburg D-93053, Germany

   Regensburg University of Applied Sciences

6. Fabian M. E. Duddeck

   Technical University of Munich

   Affiliation as printed

   TUM School of Engineering and Design, Technical University of Munich, Arcisstr. 21, Munich D-80333, Germany