Ti-Zr-Nb 固溶体合金动态压缩强度的机器学习模型优化

Shukui Li, Bojian Fan, Xingwei Liu, Shengping Si, Shuang Liu, Ruyue Xie, Jinxu Liu*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

The Ti-Zr-Nb solid solution alloys possess great application value in the fields of blast and fragmentation warhead and shaped warhead due to its excellent strength, plasticity and impact energy release characteristics. In order to achieve accurate prediction of dynamic mechanical properties of Ti-Zr-Nb solid solution alloys and provide support to composition optimization of warhead materials, 56 Ti-Zr-Nb alloys were prepared by powder metallurgy and the dynamic compression strength was tested. Furthermore, optimization of machine learning models and selection of key features for the prediction of dynamic compression strength were carried out. The results show that the prediction error of optimized model can achieve less than 8%, and three key features can be selected and ordered as: Δχ>G>δG. The optimized model can be used to design new alloys with higher dynamic compression strength successfully, being 3 100 MPa and higher than other similar alloys.

投稿的翻译标题Study on the Machine Learning Model Optimization Based on Dynamic Compression Strength of Ti-Zr-Nb Solid Solution Alloys
源语言繁体中文
页(从-至)517-525
页数9
期刊Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
43
5
DOI
出版状态已出版 - 5月 2023

关键词

  • Ti-Zr-Nb solid solution alloys
  • dynamic compression strength
  • machine learning

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