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Estimating optimum process parameters in textile draping of variable part geometries - A reinforcement learning approach

Zimmerling, C.; Poppe, C.; Kärger, L.

Fine-tuning of manufacturing processes for optimum part quality requires many resource-intensive trial experiments in practice. To reduce the experimental effort, physics-based process simulations in conjunction with optimisation algorithms can be applied, e.g. finite-element-models and evolutionary algorithms. However, they generally require considerable numerical expertise and long computation times. Efficient optimisation of such expensive-to-evaluate models often employs surrogate-based optimisation (SBO). SBO constructs numerically inexpensive approximations of the original model, which guide the optimiser in the parameter space. This allows concentrating costly simulations on the most promising regions. While SBO significantly reduces the computational load in many cases, current SBO-strategies are inevitably problem-specific and cannot be reused in other, even similar situations. Consequently, subtle problem variations, e.g. minor geometry changes in material forming, require an entirely new optimisation and all previous numerical effort is in vain. Thus, surrogate techniques with generalised applicability are an open field of research. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000120489
Veröffentlicht am 24.06.2020
DOI: 10.1016/j.promfg.2020.04.263
Zitationen: 2
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2351-9789
KITopen-ID: 1000120489
Erschienen in Procedia manufacturing
Verlag Elsevier
Band 47
Seiten 847-854
Bemerkung zur Veröffentlichung 23rd International Conference on Material Forming, ESAFORM 2020, Cottbus, Germany, 4 - 8 April 2020
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