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2S-ML: A simulation-based classification and regression approach for drawability assessment in deep drawing

  • 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.

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Metadaten
Author:Tobias LehrerORCiD, Arne KapsORCiD, Ingolf Lepenies, Fabian DuddeckORCiD, Marcus WagnerORCiDGND
URN:urn:nbn:de:bvb:898-opus4-64664
DOI:https://doi.org/10.1007/s12289-023-01770-3
Parent Title (English):International Journal of Material Forming
Publisher:Springer
Document Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2023/09/18
Tag:Deep drawing; Drawability; Machine learning; Meta-modeling; One-step
Volume:16
Article Number:56
First Page:1
Last Page:17
Note:
Corresponding author: Tobias Lehrer
Institutes:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
research focus:Produktion und Systeme
OpenAccess Publikationsweg:Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Corresponding author der OTH Regensburg
Funding:DEAL Springer Nature
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG