锻压技术

基于BP神经网络的6082铝合金固溶时效处理后的晶粒尺寸预测

英文标题：Grain size prediction of 6082 aluminum alloy after solution and aging treatment based on BP neural network

单位：1.华中科技大学材料成形与模具技术国家重点实验室 2.湖北三环锻造有限公司

分类号：TG316

出版年,卷(期):页码：2023,48(3):227-235

摘要：

Al-Mg-Si铝合金在T6热处理后的晶粒尺寸会直接影响其服役性能。为了建立6082铝合金在固溶时效处理后的晶粒尺寸与变形条件、热处理制度相关的预测模型，对不同变形条件和不同热处理条件下晶粒尺寸的演化规律进行了研究，并针对热变形和热处理后的金相组织进行了表征。研究结果表明：在统一的热处理制度下，随着试样变形后GND、GROD值的增加，热处理后的晶粒尺寸相应增大；在不同的热处理制度下，随着固溶温度、固溶时间的增加，试样热处理后的晶粒尺寸明显增大。采用多隐含层的BP神经网络建立了6082铝合金热处理后的晶粒尺寸的预测模型，模型的预测精度达到95%。

The grain size of Al-Mg-Si aluminum alloy after T6 heat treatment directly affects its service properties. In order to establish the prediction model for the grain size of 6082 aluminum alloy after solution and aging treatment related with deformation condition and heat treatment system, the evolution laws of the grain size under different deformation conditions and different heat treatment conditions were studied, and the metallographic structure after thermal deformation and heat treatment was characterized. The research results show that under the unified heat treatment system, the grain size after heat treatment increases with the increaseing of GND and GROD values of sample after deformation, and under different heat treatment systems, with the increaseing of solution temperature and solution time, the grain size of sample after heat treatment increases obviously. A prediction model for the grain size of 6082 aluminum alloy after heat treatment was established by using BP neural network with multiple hidden layers, and the prediction accuracy of the model reaches 95%.

基金项目：

国家自然科学基金资助项目（52090043）; 湖北省重点研发计划（2020BAB040）

作者简介：张浩（1998-），男，硕士研究生 E-mail：zhanghao426@hust.edu.cn 通信作者：邓磊（1982-），男，博士，副教授 E-mail：denglei@hust.edu.cn

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