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Deep neural networks as surrogate models for time-efficient manufacturing process optimisation

Zimmerling, Clemens; Schindler, Patrick; Seuffert, Julian; Kärger, Luise

Abstract (englisch):

Manufacturing process optimisation usually amounts to searching optima in high-dimensional parameter spaces. In industrial practice, this search is most often directed by human-subjective expert judgment and trial-and-error experiments. In contrast, high-fidelity simulation models in combination with general-purpose optimisation algorithms, e.g. finite element models and evolutionary algorithms, enable a methodological, virtual process exploration and optimisation. However, reliable process models generally entail significant computation times, which often renders classical, iterative optimisation impracticable. Thus, efficiency is a key factor in optimisation. One option to increase efficiency is surrogate-based optimisation (SBO): SBO seeks to reduce the overall computational load by constructing a numerically inexpensive, data-driven approximation („surrogate“) of the expensive simulation. Traditionally, classical regression techniques are applied for surrogate construction. However, they typically predict a predefined, scalar performance metric only, which limits the amount of usable information gained from simulations. The advent of machine learning (ML) techniques introduces additional options for surrogates: in this work, a deep neural network (DNN) is trained to predict the full strain field instead of a single scalar during textile forming („draping“). ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000133782
Veröffentlicht am 11.06.2021
Originalveröffentlichung
DOI: 10.25518/esaform21.3882
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 04.2021
Sprache Englisch
Identifikator ISBN: 978-2-87019-002-9
KITopen-ID: 1000133782
Erschienen in ESAFORM 2021 - 24th International Conference on Material Forming
Veranstaltung 24th International Conference on Material Forming (ESAFORM 2021), Online, 14.04.2021 – 16.04.2021
Verlag ULiège Library
Vorab online veröffentlicht am 08.04.2021
Schlagwörter Neural Networks; Deep Learning; Machine Learning; Optimisation; Surrogate; Draping; Textile forming; Manufacturing; Production
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