期刊
COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING
卷 124, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compositesa.2019.05.027
关键词
Fabrics/textiles; Computational modelling; Statistical properties/methods; Forming; Machine learning
资金
- German Federal State Ministry of Science, Research and the Arts of Baden Wurttemberg (MWK)
- Vector Stiftung
Manufacturing continuous fibre reinforced components often involves a forming process of textiles. Process simulations using Finite Element (FE) techniques allow for an accurate virtual formability assessment, but are typically time-consuming, especially for iterative design optimisations. To provide remedy, this work proposes machine-learning (ML) techniques as easy-to-evaluate approximations of FE-forming results. While previous studies focus on adjusting process parameters to achieve manufacturability, this work investigates local geometry variations. Initially, an ML-model is trained on FE-based forming examples in order to relate geometric features to forming results. During component formability assessment, an image-based recognition approach identifies manufacturing-critical regions. Then, the ML-model estimates forming results for each region individually. The validity of local formability assessment for a minimum mutual distance is based on Saint-Venant's Principle and is supported by FE-based verification. The overall approach is validated on a complex shaped box-geometry. Moreover, time-efficient exploration of local design alternatives to improve manufacturability is demonstrated.
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