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Parameter identification of superplastic constitutive model based on heteroscedastic maximum likelihood estimator

机译:基于异方差最大似然估计的超塑性本构模型参数辨识

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摘要

In this paper, the material parameters of a macro–micro coupled superplastic constitutive model,nwhich considers grain growth and metal flow behaviour, are identified by inverse analysis. Basednon the heteroscedastic maximum likelihood estimator, the objective function is provided. Thenobjective function couples the information of stress–strain data, grain size–time data, strain ratensensitivity and a priori knowledge. And then, a hybrid optimisation method is developed and usednto identify the parameters. An optimisation method, which incorporates the strengths of geneticnalgorithm and the variable error polyhedron algorithm is developed. The difficulty of choosingnappropriate initial values for the parameters in the traditional optimisation technique is overcomenby applying the genetic algorithm and the shortcoming of the slow convergent speed of thengenetic algorithm is surmounted by applying the variable error polyhedron algorithm. The nichingnmethod is used to maintain the population diversity and to choose the initial value for the variablenerror polyhedron algorithm. A transition criterion between the genetic algorithm and the variablenerror polyhedron algorithm is proposed, through the improvement on the average objectivenfunction value of the chromosomes and the objective function value of the best chromosome innthe population. At last, taking Ti–6Al–4V as an example, a set of satisfactory material parametersnis obtained. The calculated results agree well with the experimental results.
机译:本文通过反分析确定了考虑晶粒生长和金属流动行为的宏观-微观耦合超塑性本构模型的材料参数。基于非随机最大似然估计,提供了目标函数。然后目标函数将应力-应变数据,晶粒尺寸-时间数据,应变率敏感性和先验知识的信息耦合在一起。然后,开发了一种混合优化方法并将其用于参数识别。提出了结合遗传算法和可变误差多面体算法优势的优化方法。应用遗传算法克服了传统优化技术中选择合适参数初始值的困难,克服了可变误差多面体算法克服遗传算法慢收敛速度的缺点。 nichingn方法用于维护总体多样性并为可变错误多面体算法选择初始值。通过对染色体平均目标函数值和最佳种群染色体目标函数值的改进,提出了遗传算法与可变错误多面体算法之间的转换准则。最后以Ti-6Al-4V为例,获得了一组令人满意的材料参数。计算结果与实验结果吻合良好。

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