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Machine learning‐based genetic feature identification and fatigue life prediction
Author(s) -
Zhou Kun,
Sun Xingyue,
Shi Shouwen,
Song Kai,
Chen Xu
Publication year - 2021
Publication title -
fatigue and fracture of engineering materials and structures
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.887
H-Index - 84
eISSN - 1460-2695
pISSN - 8756-758X
DOI - 10.1111/ffe.13532
Subject(s) - genetic algorithm , artificial neural network , feature (linguistics) , artificial intelligence , identification (biology) , field (mathematics) , computer science , machine learning , engineering , pattern recognition (psychology) , structural engineering , mathematics , philosophy , linguistics , botany , pure mathematics , biology
Considering the nonlinear relationship between variables and fatigue life and the computational burden, a machine learning method integrating the artificial neural network (ANN) and partial least squares (PLS) algorithm was proposed as a framework to identify the genetic features through optimizing fatigue life prediction. Twenty‐seven specimens of 316LN stainless steel under uniaxial and multiaxial loadings were used as examples. As results, early fatigue data were proved to be informative for fatigue life prediction. Moreover, five genetic features were identified out of them, and a predicting model was developed. The predicted fatigue life of these samples using only these five genetic features were all located within the 1.5‐factor band. This framework can be easily extended to identify genetic features and to predict fatigue life of other materials under different loadings. Therefore, it provides an efficient option in this field to greatly reduce experimental time and cost.