Evaluating creep rupture life in austenitic and martensitic steels with soft-constrained machine learning

Machine learning is extensively utilized for predicting creep rupture of high-temperature steels. Recently, five soft-constrained machine learning algorithms (SCMLAs) have been developed to enhance the extrapolation capabilities of machine learning. These SCMLAs were applied to the austenitic steel...

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Detalhes bibliográficos

Principais autores:	Jun-Jing He, Rolf Sandström, Jing Zhang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2023-11-01
coleção:	Journal of Materials Research and Technology
Assuntos:	Creep rupture extrapolation Soft-constrained machine learning Soft-constrained neural network Stainless steels Error estimate
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2238785423026728
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