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Ti-Zr-Nb固溶体合金动态压缩强度的机器学习模型优化 被引量:1

Study on the Machine Learning Model Optimization Based on Dynamic Compression Strength of Ti-Zr-Nb Solid Solution Alloys
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摘要 Ti-Zr-Nb固溶体合金具有优异的强塑性匹配和撞击释能活性,在杀爆战斗部毁伤元和聚能战斗部药型罩材料领域极具应用价值.为了实现Ti-Zr-Nb合金及类似固溶体合金的动态力学性能精准预测,并支撑战斗部材料的精准设计与成分优化,采用粉末冶金法制备了56种Ti-Zr-Nb合金,并测试了材料的动态压缩强度,在此基础上开展了Ti-Zr-Nb合金动态压缩强度预测的机器学习模型优化、主控参量筛选研究,优化后的模型实现了合金动态压缩强度的预测误差<8%,并揭示了影响合金动态压缩强度的3个关键主控参量及权重排序:Δχ>G>δG.采用优化后的模型成功设计了具有更高动态压缩强度的合金成分,经试验验证,所设计的材料动态压缩强度达到3100 MPa,高于同类固溶体合金. 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 3100 MPa and higher than other similar alloys.
作者 李树奎 樊博建 刘兴伟 司胜平 刘爽 谢如玥 刘金旭 LI Shukui;FAN Bojian;LIU Xingwei;SI Shengping;LIU Shuang;XIE Ruyue;LIU Jinxu(School of Materials Science and Engineering,Beijing Institute of Technology,Beijing 100081,China;School of Materials Science and Engineering,Shenzhen MSU-BIT University,Shenzhen,Guangdong 518172,China;National Key Laboratory of Science and Technology on Materials Under Shock and Impact,Beijing Institute of Technology,Beijing 100081,China)
出处 《北京理工大学学报》 EI CAS CSCD 北大核心 2023年第5期517-525,共9页 Transactions of Beijing Institute of Technology
基金 国家自然科学基金资助项目(52001026) 北京理工大学青年教师启动基金资助项目(XSQD-202210006) 北京理工大学科技创新项目创新人才科技资助专项(2022CX01012)。
关键词 Ti-Zr-Nb固溶体合金 动态压缩强度 机器学习 Ti-Zr-Nb solid solution alloys dynamic compression strength machine learning
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