arXiv Open Access 2023

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

Hong Sun Yuanying Qiu Jing Li Jin Bai Ming Peng

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Abstrak

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.

Topik & Kata Kunci

cond-mat.mtrl-sci cond-mat.dis-nn cs.LG

Penulis (5)

Hong Sun

Yuanying Qiu

Jing Li

Jin Bai

Ming Peng

Format Sitasi

APA MLA BibTeX

Sun, H., Qiu, Y., Li, J., Bai, J., Peng, M. (2023). Novel models for fatigue life prediction under wideband random loads based on machine learning. https://arxiv.org/abs/2311.07114

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓