Reversion Analysis of Ceramic Damage Based on Back Propagation Neural Network

doi:10.3969/j.issn.1000-1093.2018.01.016

Abstract

Abstract: Damage accumulation of ceramic material is accompanied by crack propagation and bulking under shock loading. In order to obtain the accurate damage parameters of ceramic, the tungsten alloy ball projectiles were used to penetrate into a ceramic composite armor at high speed, and the depth of penetration and the fracture degree of ceramic plate were gained. The sample points of back propagation neural network are determined according to the damage parameters of JH-II constitutive model in Ref.\[14\]. The process of penetration into all sample points is numerically simulated by using the finite element analysis software AUTODYN. The establishment of BP neural network and the damage inversion of TiB₂-B₄C composites are accomplished by simulation and experimental data. The validity of BP neural network model for damage inversion is verified by comparing the simulated and experimental penetrating depths and fractures of recovered ceramic plates. Key

Key words: ceramic, damage, backpropagationneuralnetwork, JH-IIconstitutivemodel

CLC Number:

O346.5

GAO Yu-bo, ZHANG Wei, LI Da-cheng, YI Chen-hong, TANG Tie-gang. Reversion Analysis of Ceramic Damage Based on Back Propagation Neural Network[J]. Acta Armamentarii, 2018, 39(1): 146-152.

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第39卷
第1期2018 年1月兵工学报ACTA
ARMAMENTARIIVol.39No.1Jan.2018

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