基于SVR的2195铝锂合金FSW接头疲劳裂纹扩展寿命预测
Prediction of Fatigue Crack Propagation Life of 2195 Aluminum-Lithium Alloy FSW Joints Based on SVR
魏岩 1王芳丽 2陈吉昌 1聂小华 3常亮 3童明波1
作者信息
- 1. 南京航空航天大学航空学院,南京 210016
- 2. 南京航空航天大学航空学院,南京 210016;金陵科技学院机电工程学院,南京 211169
- 3. 中国飞机强度研究所强度与结构完整性全国重点实验室,西安 710065;中国飞机强度研究所结构冲击动力学航空科技全国重点实验室,西安 710065
- 折叠
摘要
为了对搅拌摩擦焊(Friction stir welding,FSW)焊接结构进行快速准确的裂纹扩展寿命预测,提出了一种基于支持向量回归(Support vector regression,SVR)的 2195铝锂合金FSW接头疲劳裂纹扩展寿命预测方法.首先,通过疲劳裂纹扩展试验和有限元仿真得到Paris裂纹扩展模型常数及裂纹尖端应力强度因子数据集;然后基于SVR模型建立应力强度因子预测模型,并采用粒子群优化(Particle swarm optimization,PSO)算法优化SVR模型的超参数;最后,基于构建的应力强度因子预测模型和Paris模型进行裂纹扩展寿命预测.结果表明:优化后的PSO-SVR模型能够快速、准确地预测裂纹尖端应力强度因子,在测试集上的决定系数R2 可以达到0.999 5,高于未优化SVR模型的0.954;该方法裂纹扩展寿命预测结果与有限元仿真结果进行对比,最大误差小于5%,验证了方法的准确性.
Abstract
In order to efficiently and accurately predict the crack propagation life of friction stir welding(FSW)welded structures,a fatigue crack propagation life prediction method for 2195 aluminum-lithium alloy FSW joints based on support vector regression(SVR)is proposed.Firstly,the Paris crack propagation model constants and the crack tip stress intensity factor dataset are obtained by the fatigue crack growth test and the finite element simulation.Then a stress intensity factor prediction model is established based on the SVR model,and the hyperparameters of the SVR model are optimized by the particle swarm optimization(PSO)algorithm.Finally,the crack propagation life is predicted based on the stress intensity factor prediction model and the Paris model.The results show that the optimized PSO-SVR model can effectively predict the crack tip stress intensity factor,and the determination coefficient R2 on the test set can reach 0.999 5,which is higher than 0.954 of the unoptimized SVR model.The prediction results of crack propagation life by the proposed method are compared with those by the finite element simulation.The maximum error is less than 5%,verifying the accuracy of the method.
关键词
2195铝锂合金/搅拌摩擦焊/支持向量回归/裂纹扩展/寿命预测Key words
2195 aluminum-lithium alloy/friction stir welding(FSW)/support vector regression(SVR)/crack propagation/life prediction引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据