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Formability Assessment of Variable Geometries Using Machine Learning - Analysis of the Influence of the Database

Zimmerling, Clemens ORCID iD icon 1; Fengler, Benedikt 1; Kärger, Luise 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Surrogate modelling has proven to be an effective strategy for time-efficient analysis and optimisation of expensive functions such as manufacturing process simulations. However, most surrogate approaches generate problem-specific “one-off” models, which cannot be reused in other, even similar scenarios. Hence, variations of the problem, e.g. minor geometry changes, instantly invalidate the surrogate. Image-based machine learning (ML) techniques have been proposed as an option to train a surrogate for variable geometries. However, it is currently unclear how to construct a sufficiently diverse set of generic training geometries and what effect different databases have. This work investigates the effect of different databases on the prediction accuracy of an ML-assessment of component manufacturability. The considered use-case is textile forming (draping) of a woven fabric. Sampling plans generate different numbers of training geometries, which are in turn evaluated in draping simulations. An image-based ML-algorithm is trained on these process samples and evaluated on a set of validation geometries. Results show that the diversity of the training geometries has a greater impact on the prediction accuracy than the number of samples. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000151168
Veröffentlicht am 05.10.2022
Originalveröffentlichung
DOI: 10.4028/p-1o0007
Scopus
Zitationen: 3
Dimensions
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 08.2022
Sprache Englisch
Identifikator ISSN: 0252-1059, 1013-9826, 1662-9795, 1662-9809
KITopen-ID: 1000151168
Erschienen in Key Engineering Materials
Verlag Trans Tech Publications
Band 926
Seiten 2247–2257
Vorab online veröffentlicht am 22.07.2022
Nachgewiesen in Dimensions
Scopus
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