A Dynamic Prediction Method for Assembly Quality Based on Physics-Informed Machine Learning

Abstract

In aircraft stringer assembly, flatness out-of-tolerance is caused by the cumulative propagation of multi-source coupled deviations, and it is difficult to perceive this phenomenon dynamically. To address this problem, this study proposes a dynamic prediction method that integrates assembly deviation mechanisms and machine learning. First, based on screw theory, a full-process deviation propagation model for manipulator positioning-clamping-transplanting is established. This model quantifies the coupling effects of positioning errors, flexible clamping deformations, and inertial disturbances. On this basis, a Physics-Informed Machine Learning (PIML) framework is designed. The Lagrangian dynamic equation is embedded into the Spatiotemporal Graph Convolutional Network (ST-GCN) as a physical constraint. A dual-stream architecture of Attention-LSTM is combined to process multi-sensor time-series data, and an online Bayesian update mechanism is introduced to adapt to changes in working conditions. Finally, case verification is carried out on the fuselage assembly line of a certain type of civil aircraft. The results show that the Root Mean Square Error (RMSE) of the proposed method for stringer flatness deviation prediction is reduced to 0.21 mm, which is about 19% higher in accuracy than the traditional Long Short Term Memory (LSTM) model. Moreover, this method can reduce the assembly out-of-tolerance rate by about 23%, providing a theoretical tool and technical support for high-precision aircraft assembly.