物理模型与高斯过程融合驱动的残余应力疲劳状态评估

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摘要振动是金属构件疲劳失效的重要因素，残余应力可以表征金属构件疲劳状态。然而残余应力在构件疲劳过程中演化行为复杂，传统寿命模型通常适用于残余应力缓慢松弛过程，且较少考虑初始残余应力、冷作硬化、材料差异性等影响，评估误差大。论文融合Kodama物理模型和基于高斯过程的数据驱动方法，建立了物理模型和高斯过程融合驱动的疲劳状态评估模型。通过铝合金疲劳试验，证明了本文方法可减少试样材料、表面强化工艺等差异性对评估结果的影响，提高残余应力演化模型准确性。

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	梁天佑
	尹爱军
	陈平
	方杰

关键词 ：金属构件, 残余应力, 疲劳状态评估, 高斯过程, 融合驱动

Abstract：Vibration is an important factor in the fatigue failure of metal components, and residual stress can characterize the fatigue state of metal components. Residual stress is an important factor affecting the fatigue life of metal components. The introduction of residual compressive stress through surface strengthening techniques such as shot peening can improve the life of components. However, the evolution behavior of residual stress in the process of fatigue is complex. The traditional life model is usually suitable for the slow relaxation process of residual stress, and less consideration is given to the effects of initial residual stress, cold work hardening, material difference and so on. These models has large evaluation error. In this paper, the thesis fuses the physical model and the data-driven method based on Gaussian process, and establishes the component cycle prediction model driven by the fusion of Kodama physical model and Gaussian process (K-GP). The fatigue test of 2024 aluminum alloy shot peening specimens proves that the model proposed in this paper can reduce the influence of differences in specimen materials and surface strengthening processes on the evaluation results and improve the accuracy of the residual stress evolution model.

Key words： metal component residual stress fatigue state assessment Gaussian process fusion-driven

收稿日期: 2020-08-31 出版日期: 2022-01-28

引用本文:

梁天佑，尹爱军，陈平，方杰. 物理模型与高斯过程融合驱动的残余应力疲劳状态评估[J]. 振动与冲击, 2022, 41(2): 224-228.
LIANG Tianyou,YIN Aijun,CHEN Ping,FANG Jie. Residual stress fatigue state evaluation driven by the fusion of physical model and Gaussian process. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(2): 224-228.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I2/224

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