Numerical characterisation of uncured elastomers by a neural network based approach

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• 作者：C. Zopf ; M. Kaliske ; ^{michael.kaliske@tu-dresden.de}
• 关键词：Model-free material characterisation ; Recurrent neural network ; Micro-sphere approach ; Uncured rubber material ; Finite element forming simulation
• 刊名：Computers & Structures
• 出版年：2017
• 出版时间：1 April 2017
• 年：2017
• 卷：182
• 期：Complete
• 页码：504-525
• 全文大小：1722 K
• 卷排序：182

文摘

Model-free neural network approach captures large strain inelasicity. Inelastic model-free approach represented by recurrent neural network. Adaptive recurrent neural network represents different kinds of inelasticity (viscous, plastic and visco-plastic). Novel combination of neural network based material formulation and micro-sphere framework. Application of novel model-free approach to describe uncured rubber material. Different strategies to identify the parameters of the neural network. Performance demonstrated by numerical characterisation of uncured rubber. Analysis of a rubber forming process.