Prediction of Surface Roughness and Optimization of Process Parameters for Slow Tool Servo Turning

Abstract

Surface roughness is an important index to evaluate the quality of a machined surface. In order to accurately predict the surface roughness for slow tool servo (STS) turning, taking toric surface as an example, response surface methodology (RSM) was used to perform the process test. The second-order response surface prediction model was established and the variance analysis and reliability test were carried out. The results showed that the average prediction error was 7.6%. In order to obtain the best process parameters, standard particle swarm optimization (PSO) was used. The results showed that the global optimization ability of standard PSO was poor. In order to solve the problem, compression factor was introduced and particle swarm optimization with compression factor (WCF-PSO) was constructed, which enhanced the convergence of PSO effectively. WCF-PSO was used to optimize the process parameters and the results obtained were R t =0.87mm, a f =0.01mm/r, a p =0.05mm, Δθ=8.70°, with a corresponding surface roughness of Ra=0.0486μm. The results of the verification test showed that the actual value was Ra=0.0520μm, and the error was only 7.0%, indicating that WCF-PSO had a better optimization effect.