自然杂志 ›› 2024, Vol. 46 ›› Issue (4): 247-260.doi: 10.3969/j.issn.0253-9608.2024.04.002

• 专题综述 • 上一篇    下一篇

疲劳寿命预测的机器学习模型研究进展

邓阳,戴春春,王瑞金,朱芳艳,冷建涛,张田忠   

  1. 上海大学 力学与工程科学学院,上海市应用数学和力学研究所,上海 200072
  • 收稿日期:2024-07-10 出版日期:2024-08-21 发布日期:2024-08-21
  • 基金资助:
    国家自然科学基金项目(11872238)

Advances in machine learning model for fatigue life prediction

DENG Yang, DAI Chunchun, WANG Ruijin, ZHU Fangyan, LENG Jiantao, CHANG Tienchong   

  1. Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, China
  • Received:2024-07-10 Online:2024-08-21 Published:2024-08-21

摘要: 疲劳寿命的准确预测一直是重大装备设计研发所面临的挑战性难题。近年来,人工智能模型为疲劳寿命预测提供了新的研究范式,特别是在人工智能模型中融入已有知识,可有效提升其训练和预测能力。文章综述了知识和数据融合驱动的疲劳寿命预测模型的国内外研究进展,分析了各自的优势与不足,并对未来发展趋势进行了展望。

关键词: 疲劳寿命预测, 机器学习, 数据驱动, 知识引导

Abstract: Accurate prediction of fatigue life has long been a critical challenge in the design and development of advanced equipment. In recent years, artificial intelligence (AI) models have introduced a new paradigm for fatigue life prediction, offering promising solutions. In particular, the integration of existing knowledge into AI models can significantly enhance their training and predictive capabilities. This paper provides a comprehensive review of the current state of research on knowledge-guided and data-driven models of fatigue life prediction, highlighting their respective strengths and weaknesses, and outlining fthe challenges and possible future perspectives.