摘要
疲劳S-N曲线是机械零部件强度设计的基础,但受限于贫样本或者小样本,使得诸多回归和预测模型都建立在各种假设之上。随着机械设备复杂程度的提高,零部件疲劳寿命多种影响因素间的耦合性和竞争性在不断增加;另一方面随着材料性能的发展,疲劳S-N曲线的结构也在改变,这些都使得已有模型无法满足强度设计需求。数据驱动方法能够对不同工况下的寿命数据能够进行深度挖掘,使得疲劳寿命数据的研究焕发新机。首先,对已有的确定性疲劳S-N曲线模型和小量/成组样本下的不确定疲劳S-N曲线模型进行了回顾,并对样本量大小引起的不确定性表达进行描述。其次,对由于材料性能改进导致的疲劳S-N曲线结构变化以及相应的改进模型进行了阐述。再次,介绍了疲劳S-N曲线模型的影响因素如平均应力、尺寸效应、应力集中、工作环境和表面完整性等,进而对数据驱动方法在多影响因素下的S-N曲线回归和预测进行了阐述。最后,对疲劳S-N曲线模型的发展趋势进行了讨论和展望。
Fatigue S-N curve is the basis of strength design of mechanical parts,but limited by poor or small samples which leads numerous regression and prediction models to be established on various assumptions.With the enhancement of the equipment complexity,the coupling and competitiveness among influence factors on fatigue life of components are also increasing.On the other hand,the structure of fatigue S-N curve also changes with the development of material properties.These make the existed models unable to meet the strength design requirements.However,data driven methods could deeply mine the life data under different working conditions,which makes the research of fatigue data glow with new vitality.Firstly,the existed deterministic fatigue S-N curve model and the uncertain fatigue S-N curve model under small/group samples were reviewed,and the uncertainty expressions caused by the number of samples were described.Secondly,the structural changes of fatigue S-N curve due to the improvement of material properties were analyzed,as well as the improved models.Thirdly,the influence factors such as mean stress,size effect,stress concentration,working environment and the surface integrity on the fatigue S-N curve models were introduced,and in further,regression and prediction of the fatigue S-N curve under multiple influencing factors were described based on the data driven methods.Finally,the development trend of the fatigue S-N curve model was discussed and prospected.
作者
张金豹
胡铮
张金乐
王成
邹天刚
ZHANG Jin-bao;HU Zheng;ZHANG Jin-le;WANG Cheng;ZOU Tian-gang(Science and Technology on Vehicle Transmission Laboratory,China North Vehicle Research Institute,Beijing 100072,China)
出处
《科学技术与工程》
北大核心
2023年第13期5390-5411,共22页
Science Technology and Engineering
基金
武器装备预先研究项目(ZQ20195208011)
基础加强计划(173计划)重点基础研究项目(2020-JCJQ-ZD-209-00)。
