Abstract

In this investigation, processing maps and artificial neural network (ANN) models were carried out to describe and predict the flow behavior of pure aluminum at various initial grain sizes in the hot working conditions. The elevated temperature flow behavior of AA1070 aluminum was done through isothermal hot compressive tests in a large range of initial grain size (IGS) (50–450 µm), strain rate (0.005–0.5 s⁻¹) and temperature (623–773 K). Consequences showed that the flow stress can be remarkably influenced by the initial grain size at high temperatures. Based on the results, the ANN model trained with a feed-forward back-propagation learning algorithm which was prepared to describe the flow behavior of pure aluminum at the elevated temperatures. In which the initial grain size, strain, temperature and strain rate were taken as input data and true stress was used as target data. The results showed that the developed ANN model was a powerful method to predict the complex non-linear of the hot flow behavior of pure aluminum. The processing map was plotted and analyzed via the dynamic material model as “stable” and “unstable” regions were determined by observing the microstructure evolution. Based on this, The optimum ranges for temperature and strain rate were 623–773 K and 0.05 s⁻¹ respectively, for fine-grained microstructure (lower than 50 µm) and were 650–720 K and 0.005–0.5 s⁻¹ respectively, for coarse-grained microstructures (over than 50 µm).

Graphical abstract

中文翻译：

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考虑初始晶粒尺寸的纯铝热变形特征的人工神经网络（ANN）和加工图

摘要

在这项研究中，进行了加工图和人工神经网络（ANN）模型来描述和预测在热加工条件下纯铝在各种初始晶粒尺寸下的流动行为。AA1070铝的高温流动行为是通过等温热压缩试验在大范围的初始晶粒尺寸（IGS）（50-450 µm），应变速率（0.005-0.5 s ^{-1）下完成的}）和温度（623–773 K）。结果表明，高温下的初始晶粒尺寸会显着影响流动应力。根据结果​​，使用前馈反向传播学习算法训练的ANN模型准备用来描述纯铝在高温下的流动行为。其中以初始晶粒尺寸，应变，温度和应变速率为输入数据，以真实应力为目标数据。结果表明，建立的人工神经网络模型是预测纯铝热流行为的复杂非线性的有力方法。通过动态材料模型绘制并分析加工图，通过观察微观结构演变确定“稳定”和“不稳定”区域。基于此，^-1分别为细粒显微结构（大于50μm降低）和650-720 K和0.005-0.5小号^-1分别为粗粒度的微结构（超过50微米）。

图形概要