摘要
纤维增强复合材料因其高的比强度和比刚度在航空航天、轨道交通、新能源汽车等国家重大工程领域有广阔的应用前景,但其非均质特性导致疲劳失效机理复杂,这对传统强度设计理论提出了新的挑战。目前对纤维增强复合材料疲劳性能的研究主要以试验为主,无损检测技术的发展使得试验研究方法由非原位试验向原位试验转变,以更深入地揭示其疲劳损伤机理。在疲劳理论模型研究方面,分析对比了传统寿命模型、唯象表征模型、渐进损伤模型各自的优缺点,旨在为进一步完善理论模型提供参考。由于试验分散性较大,加上理论模型构建和求解的过程中,不可避免地需要对复杂计算进行大量简化,这极大地限制了模型的准确度及应用范围。机器学习对复杂非线性问题的预测有非常高的准确度,且不涉及复杂的求解过程,为复合材料疲劳性能的表征提供了新的思路。
Due to its high specific strength and specific stiffness,the fiber reinforced polymer has had broad application prospects in major engineering fields such as aerospace,rail transit,new energy vehicles and so on,but the heterogeneous nature of composite materials leaded to complex fatigue failure mechanisms,which brought new challenges to traditional strength design theories.At present,the research on the fatigue performance of fiber reinforced polymer was mainly based on experiments.The development of non-destructive testing technology has made the experimental research method change from ex-situ test to in-situ test,so as to reveal its fatigue damage mechanism more deeply.In terms of research on fatigue theoretical models,the advantages and disadvantages of traditional life models,phenomenological representation models,and progressive damage models were analyzed and compared,in order to provide references for further improvement of theoretical models.Due to the large dispersion of the test and under the process of building and solving the theoretical model,it was inevitable to simplify the complex calculations,which greatly limited the accuracy and application range of the model.Machine learning had very high accuracy in the prediction of complex nonlinear problems,and did not involve complex solution processes,which provided a new idea for the characterization of fiber reinforced polymer fatigue performance.
作者
贾瀚翔
吴超
夏林祥
王小萌
JIA Hanxiang;WU Chao;XIA Linxiang;WANG Xiaomeng(Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province,School of Mechanics and Aerospace Engineering,Southwest Jiaotong University,Chengdu 610031,China;College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;AECC Guiyang Engine Design Institute,Guiyang 550081,China)
出处
《新技术新工艺》
2023年第8期5-9,共5页
New Technology & New Process
基金
中央高校基本科研业务费专项资金资助项目(2682022CX055)
四川省自然科学基金资助项目(2022NSFSC1926)。
关键词
纤维增强复合材料
疲劳
试验测试
失效
渐进损伤
人工智能
fiber reinforced polymers
fatigue
experimental testing
failure
progressive damage
artificial intelligence
作者简介
贾瀚翔(2001-),男,大学本科,主要从事复合材料力学等方面的研究;通信作者:王小萌。
