关键词: 非线性辨识, 物理信息神经网络, 复合材料, 挤压强度, 性能预测

Abstract: Although deep learning-based black-box models demonstrate high efficiency and accuracy in establishing input-output mappings for composite bolted joint strength prediction, their inherent lack of physical interpretability obscures model decision logic, ulti-mately compromising reliability and generalizability. Based on the fusion of physical law constraints of composite materials and nonlinear Identification constraints, a multi-constraint identification Physics-Informed Neural Network (Multi-Constrain Identifi-cation physics-informed neural Network, MCI-PINN) is proposed. Firstly, the constraints of physical laws apply the engineering estimation formula for the extrusion strength of composite material bolt connections; Secondly, the nonlinear identification con-straint establishes the nonlinear relationship between material parameters, mechanical parameters, structural parameters, etc. and extrusion strength through various functions such as linear, polynomial, power function, exponential, logarithmic, etc., and iden-tifies the mapping relationship with the optimal accuracy; Then, the physical law constraints and nonlinear identification con-straints are embedded in the neural network in the form of loss functions to guide the model training. In the case verification, the single-pin connection extrusion strength prediction of two layers of X850 material was carried out. The analysis results show that the prediction error index MRE of the extrusion strength of the two layers is 1.24% and 1.27% respectively. In terms of discrete-ness prediction, interpretability and generalization ability, MCI-PINN shows superiority compared with ANN and PINN.

Key words: nonlinear Identification, physics-informed neural network, composite material, extrusion strength, performance prediction