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Fatigue limit is well predicted by tensile strength or hardness, and the relationship is often analyzed by linear regression using the minimum squared approximation. However, the prediction of the number cycles to failure at a given stress amplitude, meaning the estimate of the S–N curve, has not been realized. Therefore, we aim to investigate the estimability of the S–N curve using the random forest method based on the data described in the NIMS fatigue data sheet. The random forest method is a machine learning algorithm and an ensemble learning algorithm that integrates weak learners of multiple decision tree models to improve generalization ability. It was clarified that the machine learning of the multiple decision tree model is excellent in fatigue limit prediction. The S–N curve can be accurately estimated by combining the prediction of fatigue limit and the number of cycles to failure at a given stress amplitude.

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• In Copyright
  

Keyword: fatigue, high-cycle fatigue, data-sheet, machine learning, random forest method

Date published: 2024-04-01

Publisher: Japan Institute of Metals

Journal:

• MATERIALS TRANSACTIONS (ISSN: 13475320) vol. 65 issue. 4 p. 428-433 MT-Z2023006

Funding:

• 日本学術振興会 20K04170 (大地震を模擬した高速ひずみ速度の極低サイクル繰り返し変形による疲労損傷の解析)

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.2320/matertrans.MT-Z2023006

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Updated at: 2024-12-27 16:30:58 +0900

Published on MDR: 2024-12-27 16:30:58 +0900