A Physical-simulation synergy approach for high-uniformity robotic gluing

Traditional robotic gluing techniques suffer from uneven adhesive distribution and low coverage rates, particularly on complex surfaces and under varying process parameters, which impede their application in smart manufacturing. To overcome these limitations, this work presents a physical-simulation synergy approach for predicting glue line dimensions and optimizing toolpath planning, aimed at improving gluing quality and production efficiency. A predictive model is developed in the simulation layer using the Whale Optimization Algorithm combined with Gaussian Process Regression to accurately capture the nonlinear relationships between key process parameters and glue line dimensions. Building on this, a surrogate model is introduced to simulate glue line distribution after compression. To ensure full coverage and high uniformity, a high-uniformity toolpath planning strategy is implemented, utilizing growth-based Hilbert curves and conformal mapping to generate efficient gluing toolpaths on complex surfaces in physical environments. Experimental results validate the effectiveness of the proposed method in accurately predicting glue dimensions, enhancing coverage, and improving adhesive performance, demonstrating its suitability for applications involving complex surface geometries.