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Comparison of Low- vs. High-Dimensional Machine Learning Approaches for Sheet Metal Drawability Assessment

  • Developing new deep-drawn sheet metal parts is a challenging task due to conflicting demands for low costs, durability, and crash properties. Ensuring manufacturability throughout geometrical changes adds to the complexity, leading engineers to rely on experience-driven iterative design changes that compromise requirements and lack reproducibility. Finite Element (FE) simulation models are employed to ensure manufacturability, albeit at the expense of high computational costs and delays in part development. To improve efficiency, a Machine learning (ML)-centered approach was proposed to ensure manufacturability. However, the limited availability of data raises uncertainty about whether a low- or high-dimensional ML approach is most suitable for drawability assessment. This work compares the accuracy of a low-dimensional, feature-based Linear Support Vector surrogate and an adapted high-dimensional PointNet model under different dataset sizes. The dataset is composed of parametrically generated, U-shaped structural sheet metal parts. We use a one-step simulation scheme and evaluate results with a Forming Limit Diagram (FLD) to label drawability. Results show the point of transition to be at about 500 training samples, from which onwards Deep learning is advantageous. Moreover, the generalizability of these models is tested on a second dataset with topologically similar components. This is to assess the potential for a geometrically more comprehensive evaluation. We discuss several influences on model performances and outline future potentials.

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Metadaten
Author:Tobias LehrerORCiD, Philipp StockerORCiD, Fabian DuddeckORCiD, Marcus WagnerORCiDGND
URN:urn:nbn:de:bvb:898-opus4-64772
DOI:https://doi.org/10.35096/othr/pub-6477
Parent Title (English):Third International Conference on Computational Science and AI in Industry (CSAI 2023), Trondheim, Norway, 28-30 August 2023
Publisher:International Centre for Numerical Methods in Engineering
Document Type:conference proceeding (presentation, abstract)
Language:English
Year of first Publication:2023
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2023/09/18
Institutes:Fakultät Maschinenbau
Begutachtungsstatus:begutachtet
research focus:Produktion und Systeme
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG