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

  • 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.

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Metadaten
Author:Tobias LehrerORCiD, Arne KapsORCiD, Ingolf Lepenies, Elena RaponiORCiD, Marcus WagnerORCiD, Fabian DuddeckORCiD
DOI:https://doi.org/10.1115/1.4065143
ISSN:2332-9025
Parent Title (English):ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Publisher:ASME
Document Type:Article
Language:English
Year of first Publication:2024
Release Date:2024/04/23
Tag:deep drawing; global sensitivity analysis; metamodeling; sheet metal forming; variance-based sensitivity analysis
First Page:1
Last Page:10
Institutes:Fakultät Maschinenbau
Fakultät Maschinenbau / Labor Finite-Elemente-Methode (FEM)
Begutachtungsstatus:peer-reviewed
Kostenträger (Forschungsprojekt, Labor, Einrichtung etc.):Labor Finite-Elemente-Methode
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
research focus:Produktion und Systeme
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International