Impact response of transparent ceramic sandwich structures and its prediction by BP neural network

LUO Haoshun; NIU Huanhuan; WANG Mufei; CHEN Jiajun; LI Zhiqiang

doi:10.11883/bzycj-2022-0199

Volume 43 Issue 10

Oct. 2023

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LUO Haoshun, NIU Huanhuan, WANG Mufei, CHEN Jiajun, LI Zhiqiang. Impact response of transparent ceramic sandwich structures and its prediction by BP neural network[J]. Explosion And Shock Waves, 2023, 43(10): 103103. doi: 10.11883/bzycj-2022-0199

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Impact response of transparent ceramic sandwich structures and its prediction by BP neural network

doi: 10.11883/bzycj-2022-0199

LUO Haoshun^1
,,
NIU Huanhuan¹,
WANG Mufei¹,
CHEN Jiajun¹,
LI Zhiqiang^{1, 2, 3, 4
,
,}

1.
Institute of Applied Mechanics, College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
2.
Shanxi Key Laboratory of Material Strength and Structural Impact, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
3.
National Experimental Teaching Demonstration Center of Mechanics, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
4.
College of Aeronautics and Astronautics, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China

Received Date: 2022-05-10
Rev Recd Date: 2023-05-29
Publish Date: 2023-10-27

Abstract

Abstract

Transparent sandwich structures can combine the advantages of various materials, thus avoiding secondary damage caused by brittle material fragments. Therefore, they are widely used in various impact protection fields. However, the impact resistance of the structure is influenced by different material thicknesses in a complex manner, and there is a lack of quick and convenient design guidance. Artificial neural network has good applicability to the nonlinear problems of multi-material structures, and provides a novel approach to structural design. In this study, a transparent sandwich structure consisting of sapphire (Al₂O₃) ceramic as the impact-absorbing layer, silica inorganic glass, polycarbonate plexiglass as the energy absorption layers, and polyurethane as the bonding material was selected as the research subject. Impact experiments were conducted on samples using a first-stage light-gas gun. Two failure modes were observed in the samples: ceramic layer bending failure and impact compression failure. The dynamic crack propagation process was meticulously captured using high-speed cameras. Subsequently, Abaqus finite element software was employed to simulate projectile impact on transparent sandwich structures with varying layers thickness ratios at 120, 150, and 180 m/s. For ceramic materials, a subroutine based on the JH-2 constitutive model was introduced. The numerical simulation of crack propagation and debris splashing process was performed using the element deletion method. The simulation results exhibited good agreement with the experimental results. Finally, the BP neural network algorithm was utilized to predict peak displacement behind the impact point. The average calculation time for single-layer and multi-layer neural network models was 1 minute and 3 minutes, respectively. Compared with the numerical simulation results of displacement peak, the average relative error of the predicted results of the two neural network models was 7.6% and 3.2%, respectively. The BP neural network model fulfills the requirement for calculation time and accuracy, saving a substantial amount of time compared to the traditional 5-hour finite element calculations. It can provide valuable guidance for the design and development of transparent sandwich structures.
- transparent sandwich structure,
- JH-2 constitutive model,
- BP neural network,
- light-gas gun impact experiment,
- crack mode

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References(25)

References

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