Transactions of Materials and Heat Treatment

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

Authors： Zhang Hao1 Wang Guowen2 Zeng Fanyi1 Tian Zhuang1 Deng Lei1 Wang Xinyun1 Tang Xuefeng1 Jin Junsong1

Unit： 1.State Key Laboratory of Material Processing and Die and Mould Technology Huazhong University of Science and Technolog 2.Hubei Tri-ring Forging Co. Ltd.

KeyWords： 6082 aluminum alloy T6 heat treatment BP neural network deformation state solid solution and aging grain size

ClassificationCode：TG316

year,vol(issue):pagenumber：2023,48(3):227-235

Abstract：

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%.

Funds：

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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