Affiliations: [a] Institute of Manufacturing Engineering, Huaqiao University, Xiamen, China
| [b] National & Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Material Products, Huaqiao University, Xiamen, China
Correspondence:
[*]
Corresponding author. Fangchen Yin, Institute of Manufacturing Engineering, HuaQiao University, Xiamen, 361021, China. E-mail: [email protected].
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.
Keywords: Robot stone machining, quantum-behaved particle swarm algorithm, regression of
support vector machines, milling force of white marble, machining parameters
