机器人曲面零件抛光粗糙度预测模型研究OACSTPCD

In order to improve the surface quality of polished surface parts,a roughness model should be established to select reasonable process parameters.Therefore,a modeling method based on support vector machine(SVM)is proposed in this paper.Through researching the robot polishing process and polishing process parameters,the tool rotation speed,polishing force,row spacing,robot feed speed,etc.are used as input variables,and roughness is used as output variables.Combined with particle swarm optimization(PSO)and SVM,a prediction model of curved surface parts polishing roughness was established,and compared with the regression analysis method.The experimental results show that the prediction error of the regression analysis method is relatively large,and the prediction model of polishing roughness of curved surface parts established based on SVM is highly consistent with the experimental results.The average relative error between the experimental measured value and the predicted value is 2.84%.The optimal combination of process parameters is obtained by optimization,and the model provides a basis for rational selection of polishing process parameters.