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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。
An inversion method based on genetic algorithm and response surface interpolation was used to identify the constitutive model parameters of functional graded material (FGM) by means of micro-indentation test and finite element simulation. In this method, the indentation experiment is first simulated by finite element method. Then the Lagrange interpolation function of three times is used to construct the load-displacement response surface, and the correlation result of the interpolation on the response surface is passed to the genetic algorithm Inversion Identification of Material Parameters. The inversion of functionally graded material parameters shows that this method can greatly improve the inversion efficiency of conventional genetic algorithm while ensuring high precision. In addition, The indentation inversion analysis, compared with single head can get more reasonable results.