数据驱动的金属疲劳寿命模型研究进展

Fatigue life models are fundamental when assessing the integrity and reliability of engineering components made of metallic materials. Hence, a plethora of domain knowledge-driven models have been developed over the past centuries, pursuing the consistency with fatigue failure mechanisms and the rationality of mathematical expressions. They generally demonstrate physical significance and can describe the complex processes of fatigue damage evolution explicitly and comprehensively. However, with the increasing demand for the operational safety of critical components and high-performance structural materials emerging constantly, they are facing limitations in the aspects of predictive capability, application scope, and engineering practicality. As an alternative, data-driven models, under the impetus of Artificial Intelligence tide, have attained growing attention and found increasing applications in life-prediction issues under various loading patterns. Data-driven models feature their powerful ability to derive optimal explicit/implicit relationships between fatigue life with numerous influential factors, without suffering from human errors. Moreover, they can quickly discover the physical laws governing fatigue failure which are difficult to be clarified by domain knowledge-driven models. Nowadays, data-driven models are recognized as opening a new pathway for fatigue damage analysis and life prediction, being a hot spot in fatigue research. This paper reviews the progress of research in developing data-driven models for predicting the fatigue life of metallic materials. Different types of data-driven models, including pure data-driven models and knowledge informed data-driven models, are summarized, along with their distinct construction methodologies and application advantages. The future prospects and challenges in this field are also discussed.