考虑晶粒不均匀度的神经网络晶粒长大模型构建及应用
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作者单位:

1.杭州职业技术学院 友嘉智能制造学院;2.浙江广厦建设职业技术大学 智能制造学院

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中图分类号:

TG146.1; TG113.12

基金项目:

杭州市农业与社会发展科研一般项目 (20201203B135),浙江省教育厅一般科研项目(Y202148060)


Construction and application of artificial neural network based grain growth model considering non-uniformity of grains
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Affiliation:

1.Fair Friend Institute of Intelligent Manufacturing,Hangzhou Vocational Technical College,Hangzhou;2.School of Intelligent Manufacturing,Zhejiang Guangsha Vocational and Technical University of Construction,Dongyang;3.China

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    摘要:

    通过晶粒长大试验获得了Inconel X-750高温合金在不同保温时间和温度下的晶粒尺寸分布,研究了保温时间和温度对晶粒尺寸及其不均匀度的影响。通过将保温时间和温度作为输入,将平均晶粒尺寸和晶粒尺寸变异系数作为输出,构建了基于人工神经网络的包含晶粒不均匀度的晶粒长大模型。采用构建的晶粒长大模型对宽泛的工艺参数范围内的晶粒尺寸和晶粒不均匀度进行预测,建立了等温条件下晶粒尺寸、晶粒不均匀度、保温温度和保温时间之间的响应关系。

    Abstract:

    The grain growth tests of Inconel X-750 superalloy were carried out to obtain the grain distributions under different holding temperatures and holding times, and the influences of the holding temperature and holding time on the size and non-uniformity of grains were investigated. The artificial neural network based grain growth model involving grain non-uniformity was constructed by employing holding temperature and holding time as inputs, and average grain size and coefficient variation of grain size as outputs. The response relationships between the grain size, grain non-uniformity, holding temperature and holding time under isothermal condition were established by predicting the grain sizes and grain non-uniformities in wide process parameter range using the constructed grain growth model.

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历史
  • 收稿日期:2023-11-14
  • 最后修改日期:2024-01-27
  • 录用日期:2024-03-08
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