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Erschienen in: Computational Mechanics 4/2020

09.01.2020 | Original Paper

A surface-to-surface contact search method enhanced by deep learning

verfasst von: Atsuya Oishi, Genki Yagawa

Erschienen in: Computational Mechanics | Ausgabe 4/2020

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Abstract

This paper describes a new surface-to-surface contact search method for contact problems between curved surfaces defined by the non-uniform rational B-spline basis functions. The method consists of a global search method based on the hierarchical virtual axis aligned bounding boxes and a local contact search method enhanced by the deep learning. The artificial neural network is employed to simulate the mapping from the parameters of contacting surface to the contact state: whether contact occurs or not, where the contact occurs and how far the slave surface penetrates to the master surface. With the proposed method, the time-consuming local contact search is successfully performed independently from the dynamic contact analysis part, where its computational efficiency is quantitatively discussed through some numerical examples.

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Metadaten
Titel
A surface-to-surface contact search method enhanced by deep learning
verfasst von
Atsuya Oishi
Genki Yagawa
Publikationsdatum
09.01.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Computational Mechanics / Ausgabe 4/2020
Print ISSN: 0178-7675
Elektronische ISSN: 1432-0924
DOI
https://doi.org/10.1007/s00466-019-01811-2

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