Parameter inversion method for target plate constitutive model based on BP neural network

• Published in: International journal of impact engineering Vol. 186; p. 104874
• Main Authors: Zheng, Zhao, Yuzhuo, Zhang, Rui, Tan, Dongyu, Li
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: Explosive cutting; Finite element analysis; Neural network; Parameter inversion; Target plate constitutive model; Parameter inversion; Neural network; Finite element analysis; Target plate constitutive model; Explosive cutting
• ISSN: 0734-743X; 1879-3509
• DOI: 10.1016/j.ijimpeng.2023.104874

•A neural network-based inversion method that can be used to streamline the acquisition of polymethyl methacrylate (PMMA) material target plate constitutive model parameters for explosive cutting simulations is presented.•The parameters of target plate material constitutive model are derived by parameter inversion and verified by numerical simulation.•The BP neural network-based parameter inversion method not only accurately simulated the jet penetration depth, impact fracture thickness, and spallation thickness of the PMMA target plate, but also effectively characterized the damage features and notch morphology of the damaged areas.•This method ensuring high accuracy while reducing experimental costs and risks compared to traditional methods. In this paper, a neural network-based inversion method that can be used to streamline the acquisition of polymethyl methacrylate (PMMA) material constitutive model parameters for explosive cutting simulations is presented. This approach mitigates the need for multiple tests, a requirement of traditional methods. Initially, three categories of the damage, penetration depth, impact fracture thickness and spallation damage thickness, were distinguished and quantified by cutting the14 mm PMMA target plate with a 2.5 mm wide linear shaped charge, and the adjustment interval for the constitutive model parameters could be determined by combining the explosive cutting experiment data and the empirical parameters of the Johnson Holmquist Ceramics (JH-2) constitutive model. Subsequently, the numerical simulations of the cutting process of the performed target plate tests were carried out using LS-DYNA, and a dataset of target plate damage incorporating the three types of damage data was amassed. Ultimately, a neural network model correlating the parameters of the PMMA target plate constitutive model with the damage data was developed and trained using the plate damage dataset, and several supplementary experiments as well as finite element numerical simulations involving a 4.2 mm wide linear shaped charge cutting a 19 mm PMMA target plate were conducted. The numerical results displayed minimal variance from the experimental results in terms of fracture characteristics and damage data, suggesting that the inverted JH-2 constitutive model parameters can be effectively applied to PMMA target plate explosive cutting simulations. The parameter inversion method allows for the acquisition of more accurate material constitutive model parameters with fewer experiments and will be beneficial for further theoretical studies and practical applications of target plates in the future.