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随着计算机技术和数值模拟技术发展,CAE(computer aidded engineering)技术得到越来越广泛的应用,目前困扰数值模拟技术在相关领域内进一步应用的最主要障碍是如何获取选用模型的材料参数.给出了应用反分析方法识别材料参数的基本框架,该框架包括建立目标函数、构造优化算法及评价识别参数可靠性几部分,并以金泉林和海锦涛提出的描述金属流动和晶粒尺寸长大的超塑性本构模型在927℃下Ti-6Al-4V合金的参数识别为例,说明应用该框架识别材料参数的过程,其中目标函数为晶粒尺寸及流动应力计算值和实验值差值的加权平方和;优化算法为基于目标函数特性构造的吸收遗传算法、Levenberg-Marquardt算法和增广Gauss-Newton算法优点的混合全局优化算法;通过计算结果和实验结果的比较及参数识别值和理论值的比较评判参数识别结果的正确性.
With the development of computer technology and numerical simulation technology, computer aided engineering (CAE) technology has been more and more widely used. The most important obstacle to the further application of the numerical simulation technology in related fields at present is how to obtain the material parameters of the selected model. The basic framework for the identification of material parameters using back analysis is presented. The framework includes the establishment of objective functions, the construction of optimization algorithms, and the evaluation of the reliability of identification parameters. The framework of the proposed method for describing metal flow and grain size A large superplastic constitutive model is taken as an example to identify the parameters of Ti-6Al-4V alloy at 927 ℃. The objective function is to identify the material parameters by using the framework. The objective function is the difference between calculated value and experimental value of grain size and flow stress ; The optimization algorithm is a hybrid global optimization algorithm based on the genetic algorithm with Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm based on the characteristics of the objective function; by comparing the calculated results with the experimental results and the parameter identification value and theory The correctness of the result of identification of the comparative judgment value.