An improved QPSO-SVM-based approach for predicting the milling force for white marble in robot stone machining

Abstract

Milling force prediction is one of the most important ways to improve the quality of products and stability in robot stone machining. In this paper, support vector machines (SVMs) are introduced to model the milling force of white marble, and the model parameters in the SVMs are optimized by the improved quantum-behaved particle swarm optimization (IQPSO) algorithm. A set of online inspection data from stone-machining robotic manipulators is adopted to train and test the model. The overall performance of the model is evaluated based on the decision coefficient (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE), and the results obtained by IQPSO-SVM are superior to those of the PSO-SVM model. On this basis, the relationship between the milling force of white marble and various machining parameters is explored to obtain optimal machining parameters. The proposed model provides a tool for the adjustment of machining parameters to ensure stable machining quality. This approach is a new method and concept for milling force control and optimization research in the robotic stone milling process.

Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence

“…Yin F et al focused on the optimization problem of stone carving robots using particle swarm optimization algorithm based on reinforcement learning. Combining support vector machines, a milling force control and optimization method for engraving robots has been proposed, thereby improving the overall engraving processing quality [13] . Ji S et al focused on the intelligent manufacturing problem of robots, based on the automatic programming environment under reinforcement learning algorithms.…”

“…ratio represents the proportion of new and old strategies. The strategy update and value function update are shown in equation (13).…”