Reliability and sensitivity analysis of compressive strength after low-speed impact for composite honeycomb sandwich structure

doi:10.19936/j.cnki.2096-8000.20240228.001

Abstract

Abstract: A reliability and sensitivity analysis method of compressive strength of composite honeycomb sandwich structure after low-speed impact is established. The progressive damage failure analysis model of composite honeycomb sandwich structure is established by using ABAQUS finite element analysis software and VUMAT subroutine, and the effectiveness of the model is verified by experiments. On this basis, the material mechanical properties, panel thickness and honeycomb core thickness in the model are parameterized by Python language. The radial basis function neural network agent model is used to replace the progressive damage failure analysis model. The reliability analysis of compressive strength after impact is carried out based on Monte Carlo method. The methods based on variance and failure probability are respectively used to analyze the sensitivity of parameters, and the influence of variation coefficient of parameters on structural reliability is studied. The results show that when the external load is less than 10 000 N, the probability of structural failure is less than 0.000 17, and when the external load reaches 12 700 N, the probability of structural failure reaches 0.9. The mechanical properties of the panel, the thickness of the panel and the thickness of the honeycomb core have a great influence on the compressive strength after impact. The coefficient of variation of compressive strength after impact increases with the increase of the coefficient of variation of parameter.

Key words: honeycomb sandwich structure, compression after impact, neural network, reliability, sensitivity, composites

CLC Number:

TB332

LI Hongnian, SHI Qiangbin, WANG Xuan, ZOU Runwen, ZHANG Feng. Reliability and sensitivity analysis of compressive strength after low-speed impact for composite honeycomb sandwich structure[J]. COMPOSITES SCIENCE AND ENGINEERING, 2024, 0(2): 5-12.

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