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Numerical experiments for inverse analysis of material properties and size in functionally graded materials using the Artificial Bee Colony algorithm

机译：使用人工蜂群算法对功能梯度材料的材料特性和尺寸进行反分析的数值实验

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摘要

Functionally graded materials (FGMs) possess properties that vary gradually and are highly heat resistant. When incorporating these FGMs into a structure, the required distribution of material properties must be determined and produced to specification. Thus far, an inverse analysis method has been used for estimating a distribution of a Young's modulus of FGMs. However, minimizing an objective function to estimate a material property using the Davidon-Fletcher-Powell method did not converge under some initial conditions. Therefore, convergence of a solution depends on initial conditions. On the other hand, the Artificial Bee Colony (ABC) algorithm has drawn considerable interest in global optimization of a multimodal function. The objective of the present paper is to propose a method of numerical experimentation to conduct inverse analysis in FGMs with the ABC algorithm. A numerical experiment estimating both size and a graded index of an FGM beam that is based on measured stress values is presented. Next, determining a distribution of thermal conductivity in two-dimensional FGMs using measured steady state temperatures is carried out. The results of the numerical experiments demonstrate the effectiveness of the proposed method.
机译：功能梯度材料（FGM）具有逐渐变化的特性，并且具有很高的耐热性。将这些FGM纳入结构时，必须确定所需的材料特性分布并按照规范生产。迄今为止，已经使用逆分析方法来估计FGM的杨氏模量的分布。但是，在某些初始条件下，使用Davidon-Fletcher-Powell方法最小化估计材料性质的目标函数并没有收敛。因此，解决方案的收敛取决于初始条件。另一方面，人工蜂群算法（ABC）对多峰函数的全局优化引起了极大的兴趣。本文的目的是提出一种利用ABC算法对FGMs进行逆分析的数值实验方法。提出了基于测得的应力值估算FGM梁的尺寸和坡度指数的数值实验。接下来，使用所测量的稳态温度来确定二维FGM中的热导率分布。数值实验结果证明了该方法的有效性。

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