Recurrent Neural Networks and Three-Point Bending Test on the Identification of Material Hardening Parameters

The continuous evolution of metallic alloys in the automotive industry has led to the development of more advanced and flexible constitutive models that attempt to accurately describe the various fundamental properties and behavior of these materials. These models have become increasingly complex, i...

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書誌詳細

主要な著者:	Daniel J. Cruz, Manuel R. Barbosa, Abel D. Santos, Rui L. Amaral, Jose Cesar de Sa, Jose V. Fernandes
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2024-01-01
シリーズ:	Metals
主題:	machine learning material parameter identification three-point bending recurrent neural networks (RNNs) long short-term memory (LSTM)
オンライン･アクセス:	https://www.mdpi.com/2075-4701/14/1/84
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