Automated spray path planning based on Bayesian optimization and ant colony optimization - Peng, Ta-Jen; Chen, Peng-Jen; Su, Ting-Lun; Lin, Chung-Chieh (International journal of advanced manufacturing technology, 2025)
Automated spraying of forging release agents faces significant challenges, including inadequate lubricant coverage and excessive material waste due to complex mold geometries and dynamic production requirements. This study proposes a dynamic path-planning method based on ant colony optimization (ACO) combined with Bayesian optimization (BO) to effectively address these challenges. The proposed approach dynamically optimizes critical algorithm parameters to effectively enhance spray coverage and reduce loss function values. Initially, the slope and height maps of the mold surface are generated using CAD software, quantifying the lubricant demand priorities of different regions. These data are then integrated into the pheromone update and path search processes of the ACO algorithm, significantly increasing pheromone accumulation in high-demand areas and enhancing spraying coverage efficiency. To overcome limitations of the ACO algorithm related to local optimality or prolonged trial-and-error parameter tuning, Bayesian optimization iteratively searches for optimal configurations of critical parameters (such as pheromone evaporation rate and heuristic weight). By continuously updating algorithm configurations based on loss function observations and Gaussian process modeling, the method ultimately converges to near-optimal solutions characterized by high coverage and effective resource utilization. Experimental results demonstrate that the optimized spraying system ensures lubricant stability under high-temperature forging conditions while substantially reducing redundant spraying in low-demand regions. By dynamically balancing exploration and exploitation, the loss function value decreased from an initial range of approximately 0.5–0.6 to below 0.06, confirming significant improvements in quality efficiency and material savings. The outcomes significantly enhance production yield, reduce material waste, and provide essential insights for advancing automation and smart manufacturing in the forging industry.