Prediction of bearing strength for composite bolted joint structures based on MCI-PINN

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, ultimately 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 (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, using linear, polynomial, power, exponential, and logarithmic functions as basic functional forms, nonlinear relationships between material parameters, mechanical parameters, structural parameters, and extrusion strength are established, and the mapping relationship with the highest accuracy is identified to serve as a constraint for nonlinear identification. Then, the physical law constraints and nonlinear identification constraints are embedded in the neural network in the form of loss functions to guide the model training. Finally, 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 discreteness prediction, interpretability and generalization ability, MCI-PINN shows superiority compared with ANN and PINN.