首页|Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

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This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the pene-tration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimen-sionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.

Data-driven dimensional analysisPenetrationSemi-infinite metal targetDimensionless numbersFeature selection

Qingqing Chen、Xinyu Zhang、Zhiyong Wang、Jie Zhang、Zhihua Wang

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Institute of Applied Mechanics,College of Mechanical and Vehicle Engineering,Taiyuan University of Technology,Taiyuan 030024,China

Shanxi Key Lab.of Material Strength & structural Impact,College of Mechanical and Vehicle Engineering,Taiyuan 030024,China

Department of Civil and Environmental Engineering,National University of Singapore,1 Engineering Drive 2,117576,Singapore

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of Chinaspecial fund for Science and Technology Innovation Teams of Shanxi Province

122722571210229212032006202204051002006

2024

10.1016/j.dt.2024.04.012

防务技术
中国兵工学会

防务技术

CSTPCD
影响因子：0.358
ISSN：2214-9147
年,卷(期)：2024.40(10)