Novel models for fatigue life prediction under wideband random loads based on machine learning

Autor:	Sun, Hong, Qiu, Yuanying, Li, Jing, Bai, Jin, Peng, Ming
Rok vydání:	2023
Předmět:	Condensed Matter - Materials Science Condensed Matter - Disordered Systems and Neural Networks Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Machine learning as a data-driven solution has been widely applied in the field of fatigue lifetime prediction. In this paper, three models for wideband fatigue life prediction are built based on three machine learning models, i.e. support vector machine (SVM), Gaussian process regression (GPR) and artificial neural network (ANN). The generalization ability of the models is enhanced by employing numerous power spectra samples with different bandwidth parameters and a variety of material properties related to fatigue life. Sufficient Monte Carlo numerical simulations demonstrate that the newly developed machine learning models are superior to the traditional frequency-domain models in terms of life prediction accuracy and the ANN model has the best overall performance among the three developed machine learning models.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.07114 Zobrazit plný text záznamu View this record from Arxiv