| 引用本文: | 刘志强,王东君,祝世强,刘钢.Ti2AlNb合金构件全流程模拟及组织性能预测[J].材料科学与工艺,2022,30(2):1-8.DOI:10.11951/j.issn.1005-0299.20210299. |
| LIU Zhiqiang,WANG Dongjun,ZHU Shiqiang,LIU Gang.Whole process simulation of Ti2AlNb alloy component and prediction of its microstructure and mechanical properties[J].Materials Science and Technology,2022,30(2):1-8.DOI:10.11951/j.issn.1005-0299.20210299. |
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| Ti2AlNb合金构件全流程模拟及组织性能预测 |
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刘志强1,4,王东君1,4,祝世强3,刘钢1,2
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(1.金属精密热加工国家级重点实验室(哈尔滨工业大学),哈尔滨150001;2.哈尔滨工业大学 流体高压成形技术研究所,哈尔滨150001;3.首都航天机械公司,北京100076;4.哈尔滨工业大学 材料科学与工程学院,哈尔滨150001)
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| 摘要: |
| 金属构件的塑性加工不仅需要控制其形状尺寸,还要调控其微观组织和力学性能,以获得满足服役要求的产品。构件成形结束后,常需要通过热处理工艺调控其组织和性能,但由于成形过程中的变形参数影响其热处理前的微观组织,因此,也影响到其热处理过程的组织演变,进而影响构件的服役性能,导致热处理调控更加复杂。本文基于机器学习的方法,考虑变形参数对热处理的影响,建立了Ti2AlNb合金构件高温成形过程微观组织和力学性能的预测模型,并与有限元模拟软件结合,建立了Ti2AlNb合金构件成形-热处理的全流程模拟方法。本文通过该方法对Ti2AlNb管材高温压制-时效处理工艺进行了全流程的模拟,模拟结果表明变形和热处理参数均会对成形构件的组织和力学性能产生影响。进而通过成形和热处理实验对模拟结果进行了验证,模拟结果与实验结果的一致性较好。说明通过该方法,可以实现构件成形-热处理全流程的模拟和组织-性能预测,可用于指导加工工艺的制定。 |
| 关键词: 组织性能预测 机器学习 Ti2AlNb合金 热成形 全流程模拟 |
| DOI：10.11951/j.issn.1005-0299.20210299 |
| 分类号:G3.1 |
| 文献标识码:A |
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| Whole process simulation of Ti2AlNb alloy component and prediction of its microstructure and mechanical properties |
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LIU Zhiqiang1,4, WANG Dongjun1,4, ZHU Shiqiang3, LIU Gang1,2
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(1.National Key Laboratory for Precision Hot Processing of Metals (Harbin Institute of Technology), Harbin 150001, China; 2. Institute of High Pressure Fluid Forming, Harbin Institute of Technology, Harbin 150001, China; 3. Capital Aerospace Machinery Company, Beijing 100076, China; 4. School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)
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| Abstract: |
| For the plastic forming of metal components, it is necessary to control not only the shape and dimension of components, but also the microstructure and mechanical properties of components, so as to obtain components that meet the service requirements. In general, the microstructure and mechanical properties of the components after forming are controlled by heat treatment. While the deformation state of the components during the forming process affects the microstructure before the heat treatment, which then affects the microstructure during the heat treatment as well as the service properties of the final components, making the heat treatment control more complex. Based on the method of machine learning, a prediction model for microstructure and mechanical properties of Ti2AlNb alloy components was established, considering the effect of deformation state on heat treatment. By combining the model with finite element software, the whole process simulation method of forming and subsequent heat treatment of Ti2AlNb alloy components was proposed. In this study, the whole process of high temperature pressing and aging treatment of Ti2AlNb tube was simulated by the proposed method.Simulation results show that the microstructure and mechanical properties of the final components were affected by deformation and heat treatment. The simulation results were verified by deformation and heat treatment experiments, and were in good agreement with the experimental results. It indicates that by this method, the whole process of forming and subsequent heat treatment of components can be simulated, and the microstructure and mechanical properties can be predicted, which will provide guidance for the design of the whole process. |
| Key words: prediction of microstructure and mechanical properties machine learning Ti2AlNb alloy hot forming whole process simulation |
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