Digital twin modeling of the robotic gluing system for predicting the quality of glue lines and optimizing gluing parameters

Abstract

Digital twin (DT) technology is changing the current pattern of intelligent manufacturing, it makes up for the shortcomings of process parameter optimization methods to improve real-time and predictability. This paper developed DT models for the robotic gluing system to predict the quality (width and thickness) of glue lines and optimize gluing parameters (trajectory and extrusion speeds). The DT framework based on the geometric, physical, behavioral, and rule models is constructed to monitor and optimize the gluing parameters in real-time. An improved backpropagation neural network (BPNN) prediction model based on whale optimization algorithm (WOA) is established to predict the width and thickness of glue lines from historical and real-time data, while simultaneously enabling real-time calculation of the cross-sectional area of glue lines. A multi-objective optimization model constructed using non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the gluing parameters. The DT prototype of the robotic gluing system has been developed and verified experimentally. The position calibration of the geometric model is used to correct the gluing trajectory before gluing, and the position errors of the gluing points are within ± 0.5 mm. The gluing trajectory is designed to test the effectiveness of the adaptive optimization of gluing parameters. The prediction errors of the width and thickness of the glue line are controlled between ± 0.5 mm and ± 0.3 mm, individually. After parameter optimization, the width and thickness of the glue line at the corner are reduced by 4.53 % and 7.54 %, respectively, thus avoiding glue accumulation. This reduction solves the problem of poor consistency in the quality of glue lines and verifies the feasibility of integrated monitoring, prediction, and optimization based on the DT model.