Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

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Published Mar 21, 2023
DOI https://doi.org/10.3221/IGF-ESIS.64.17

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Yangjinyu Li
Software Technology Institute, Dalian Jiaotong University, Dalian, China
Li Zou
Software Technology Institute, Dalian Jiaotong University, Dalian, China; Liaoning Key Laboratory of Welding and Reliability of Rail Transportation Equipment, Dalian Jiaotong University, Dalian, China; Dalian Key Laboratory of Welded Structures and Its Intelligent Manufacturing Technology (IMT) of Rail Transportation Equipment, Dalian Jiaotong University, China
Zhengjie Zhu
Software Technology Institute, Dalian Jiaotong University, Dalian, China

Abstract

S-N curve fatigue samples of titanium alloy welded joints have such a comparatively significant scatter, that results in the issue that the fatigue life prediction accuracy is not optimal. In this work, the titanium alloy welded joints' fatigue data is used as analysis data, and the neighborhood rough set reduction with improved firefly algorithm efficient method of fitting stress-life curves is set forth. The welded joint's fatigue decision system is built with fatigue data. The continuous iteration of the firefly algorithm is used as the search strategy, the neighborhood rough set is adopted to decrease attributes, and the major deciding elements of welded joints' fatigue life is identified. The fatigue characteristic domains are divided based on the neighborhood rough set reduction with improved firefly algorithm's key factor set, and the S-N curves can then be fitted to each domain individually. According to the goodness-of-fit analysis, the proposed approach can improve fatigue life accuracy and reduce sample scattering from fatigue.

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    Issue
    Vol. 17 No. 64 (2023): April 2023
    Section
    Fatigue

    How to Cite

    Li, Y., Zou, L. and Zhu, Z. (2023) “Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm”, Frattura ed Integrità Strutturale, 17(64), pp. 250–265. doi: 10.3221/IGF-ESIS.64.17.
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