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首页> 外文期刊>Journal of Materials Engineering and Performance >Modeling Microstructura! Evolution During Dynamic Recrystallization of Alloy D9 Using Artificial Neural Network
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Modeling Microstructura! Evolution During Dynamic Recrystallization of Alloy D9 Using Artificial Neural Network

机译：建模微结构！ D9合金动态再结晶过程中的人工神经网络演变

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摘要

An artificial neural network (ANN) model was developed to predict the microstructural evolution of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (Alloy D9) during dynamic recrystallization (DRX). The input parameters were strain, strain rate, and temperature whereas microstructural features namely, percent DRX and average grain size were the output parameters. The ANN was trained with the database obtained from various industrial scale metal-forming operations like forge hammer, hydraulic press, and rolling carried out in the temperature range 1173-1473 K to various strain levels. The performance of the model was evaluated using a wide variety of statistical indices and the predictability of the model was found to be good. The combined influence of temperature and strain on microstructural features has been simulated employing the developed model. The results were found to be consistent with the relevant fundamental metallurgical phenomena.
机译：建立了人工神经网络（ANN）模型，以预测15Cr-15Ni-2.2Mo-Ti改性奥氏体不锈钢（Alloy D9）在动态重结晶（DRX）过程中的组织演变。输入参数为应变，应变速率和温度，而微观结构特征（即DRX百分比和平均晶粒尺寸）为输出参数。通过从各种工业规模的金属成型操作（例如锻锤，液压机以及在1173-1473 K温度范围内进行的轧制至各种应变水平）获得的数据库对ANN进行了培训。使用多种统计指标评估模型的性能，发现模型的可预测性良好。温度和应变对微观结构特征的综合影响已使用开发的模型进行了模拟。发现结果与相关的基本冶金现象一致。

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