基于遗传算法的功能梯度材料参数的反演分析
Genetic algorithm based inverse analysis for functionally graded material parameters
-
摘要: 采用一种基于遗传算法结合响应面插值的反演方法, 利用微压痕实验和有限元模拟, 对功能梯度材料(FGM)的本构模型参数进行识别分析。此方法首先以解耦的方式对压痕试验进行有限元模拟计算, 然后利用三次拉格朗日插值函数构造荷载-位移响应面, 并将响应面上插值得到的相关结果传递给遗传算法以实现材料参数的反演辨识。对功能梯度材料参数的反演研究表明: 该方法在保证较高精度的同时能够极大地提高常规遗传算法的反演效率; 另外, 利用大、小压头组合的双压头模式对功能梯度材料进行压痕的反演分析, 较之单压头能够获得更为合理的结果。Abstract: To determine the model parameters of functionally graded material(FGM), an inverse analysis procedure based on genetic algorithm and response surface interpolation was introduced, in which the experimental records obtained from instrumented micro-indentation and their finite element analysis results were utilized. With an uncoupled manner, the finite element simulation of indentation was first conducted followed by the construction of a set of load-displacement response surfaces by a cubic Lagrange interpolation function, and then transferred to genetic algorithm for material parameter identification. This study shows that this approach inherits high accuracy from the general method based on genetic algorithm; however its solution efficiency is much higher since the large amounts of finite element calculations are substituted by interpolation on response surfaces. Numerical investigation also discloses that a double-indenter test mode can obtain a more reasonable result in comparison with the single-indenter mode for parameter identification of FGM.
下载: