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

IF 5 Q1 ENGINEERING, MULTIDISCIPLINARY Defence Technology(防务技术) Pub Date : 2024-10-01 DOI:10.1016/j.dt.2024.04.012
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Abstract

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 penetration 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 dimensionless 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.
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通过整合机器学习和特征选择方法,以数据驱动预测半无限目标穿透的无量纲数量
本研究采用数据驱动方法,将尺寸不变性原理嵌入人工神经网络,从实验测量结果中自动识别棒状射弹穿透半无限金属目标时的主要无量纲量。通过检查指数矩阵和特征变量之间的耦合关系,简化了无量纲量的数学表达式。作为一种基于物理学的维度缩减方法,这种方法将高维参数空间缩减为在穿透情况下只涉及几个物理上可解释的无量纲量的描述。然后,通过特征选择工程评估了四种撞击条件下各种无量纲特征变量对穿透效率的相对重要性。结果表明,通过这种协同方法,无需参考复杂的理论方程,也无需借助详细的穿透力学知识,所选择的关键无量纲特征变量与参考文献中的变量一致。最后,确定的无量纲量可以有效地应用于对特定穿透情况进行半经验分析,并验证了回归函数的可靠性。
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来源期刊
Defence Technology(防务技术)
Defence Technology(防务技术) Mechanical Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering
CiteScore
8.70
自引率
0.00%
发文量
728
审稿时长
25 days
期刊介绍: Defence Technology, a peer reviewed journal, is published monthly and aims to become the best international academic exchange platform for the research related to defence technology. It publishes original research papers having direct bearing on defence, with a balanced coverage on analytical, experimental, numerical simulation and applied investigations. It covers various disciplines of science, technology and engineering.
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