Multi-directional design control of plastic crash components by means of domain-predictive feed-forward neural networks
The robust crash design of plastic components is of great interest in automotive engineering. For an efficient development, a prototype-free development is aimed at, whereby at the same time the test programme can be strongly reduced and in the future possibly completely omitted, if the virtual desi...
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Published in | Structural and multidisciplinary optimization Vol. 64; no. 6; pp. 4115 - 4128 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2021
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | The robust crash design of plastic components is of great interest in automotive engineering. For an efficient development, a prototype-free development is aimed at, whereby at the same time the test programme can be strongly reduced and in the future possibly completely omitted, if the virtual design delivers robust results. Based on the plastic component design of a head box, a possible procedure consisting of finite element calculation and artificial neural networks is described exemplarily. The head box is the load-bearing part of the head restraint of a car seat. It can be shown that, if these methods are used correctly, components can be designed very quickly to fulfil the specified objective functions. |
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ISSN: | 1615-147X 1615-1488 |
DOI: | 10.1007/s00158-021-03031-w |