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Modeling of the microstructure variables in the isothermal compression of TC11 alloy using fuzzy neural networks

机译：TC11合金等温压缩过程中微观组织变量的模糊神经网络建模。

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摘要

The grain size and volume fraction of prior a phase in high temperature deformation appear highly nonlinear and fuzzy characteristic. The approach to model the grain size and volume fraction of prior a phase and to train the model structure is presented in terms of the fuzzy set and artificial neural networks method using BP learning algorithm. The experimental data of teacher's samples are prepared from the grain size and volume fraction of prior a phase after isothermal compression of TC11 alloy at the deformation temperatures ranging from 1023 to 1323 K with an interval of 20 K, the strain rates ranging from 0.001 to 10.0s~(-1), and the height reductions ranging from 50 to 70%. The predicted grain size and volume fraction are in a good agreement with the experimental results in the isothermal compression of TC11 alloy.
机译：高温变形前一相的晶粒尺寸和体积分数呈现出高度非线性和模糊的特征。提出了一种基于模糊集和人工神经网络的BP学习算法，对前一相的晶粒尺寸和体积分数进行建模并训练模型结构。教师样品的实验数据是由TC11合金等温压缩后的前一相的晶粒尺寸和体积分数得出的，其变形温度范围为1023至1323 K，间隔为20 K，应变速率为0.001至10.0 s〜（-1），高度降低幅度为50％至70％。预测的晶粒尺寸和体积分数与TC11合金的等温压缩实验结果吻合良好。

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