### 基于Deform软件二次开发和BP神经网络的TA15多向锻造微观组织预报

1. 1. 燕山大学 先进锻压成形技术与科学教育部重点实验室, 秦皇岛 066004;
2. 燕山大学 亚稳材料制备技术与科学国家重点实验室, 秦皇岛 066004
• 收稿日期:2020-09-01 修回日期:2020-10-09 发布日期:2020-12-18
• 通讯作者: 骆俊廷 E-mail:luojunting@ysu.edu.cn
• 基金资助:
河北省自然科学基金（E2019203005）

### Microstructure prediction of multi-directional forging of TA15 alloy based on secondary development of Deform and BP neural network

LUO Junting1,2, ZHAO Jingqi1, YANG Zheyi1, LIU Weipeng1, ZHANG Chunxiang2

1. 1. Education Ministry Key Laboratory of Advanced Forging and Stamping Science and Technology, Yanshan University, Qinhuangdao 066004, China;
2. State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao 066004, China
• Received:2020-09-01 Revised:2020-10-09 Published:2020-12-18
• Supported by:
Natural Science Foundation of Hebei Province (E2019203005)

Abstract: The true stress-strain curve of hot deformation of TA15 alloy was constructed through the hot compression test. The constitutive equations for hot deformation in the temperature range of the dual-phase region and single-phase region of the alloy were then established. A dynamic recrystallization model of TA15 alloy was established based on the statistical data of dynamic recrystallization of hot compressed samples. With the help of the secondary development function provided by Deform, programming of related mathematical models was realized, the experimental plan was formulated by the orthogonal method, and then simulation of the microstructure evolution of the multi-directional forging deformation of the dual-phase region and single-phase region of TA15 alloy was realized. Through analysis of the orthogonal experiment results, the objects under the influence of various factors and the influence degree of the factors were obtained, and the optimal combination of factors for multi-directional forging in the temperature range of the dual-phase region and the single-phase region was proposed. A Back Propagation (BP) neural network prediction model for the multi-directional forging deformation microstructure of TA15 alloy was established. The prediction results were compared with the finite element simulation results. The comparison results show that the prediction results of the two methods are basically the same, but the neural network based method can predict details, which is difficult to be achieved by finite element simulation, and thus can achieve more detailed division of microstructure distribution.