PREDICTION OF SURFACE ROUGHNESS FOR END MILLING TITANIUM ALLOY USING MODIFIED PARTICLE SWARM OPTIMIZ

• 作 者: 刘春景 唐敦兵 何华 陈兴强
• 机 构: College of Mechanical and Electronic Engineering,Nanjing University of Aeronautics and Astronautics,
• 出 处: Transactions of Nanjing University of Aeronautics & Astronau
• 发表日期: 2013年01期
• 关键词: roughness titanium SURFACE milling faster swarm magnitude explain variance axial

It is difficult to construct the prediction model for titanium alloy through analyzing the formation mechanism of surface roughness due to the complicated relation between influential factors and surface roughness.A novel algorithm based on the modified particle swarm optimization ( PSO ) least square support vector machine ( LSSVM ) is proposed to predict the roughness of the end milling titanium alloys.According to Taguchi method and features in milling titanium alloys , the influences of cutting speed , feed rate and axial depth of cut on surface roughness are investigated with the analysis of variance ( ANOVA ) of the experimental data.The research results show that the construction speed of the modified PSO LS-SVM model is two orders of magnitude faster than that of back propagation ( BP ) model.Moreover , the prediction accuracy is about one order of magnitude higher than that of BP model.The modified PSO LS-SVM prediction model can explain the influences of cutting speed , feed rate and axial depth of cut on the surface roughness of titanium alloys.Either a higher cutting speed , a lower feed rate or a smaller axial depth of cut can lead to the decrease of surface roughness.

It is difficult to construct the prediction model for titanium alloy through analyzing the formation mechanism of surface roughness due to the complicated relation between influential factors and surface roughness. A novel algorithm based on the modified particle swarm optimization (PSO) least square support vector machine ( LSSVM) is proposed to predict the roughness of the end milling titanium alloys. According to Taguchi method and features in milling titanium alloys, the influences of cutting speed, feed rate and axial depth of cut on surface roughness are investigated with the analysis of variance ANOVA) of the experimental data. The research results show that the construction speed of the modified PSO LS-SVM model is two orders of magnitude faster than that of back propagation (BP) model. More than one order of magnitude higher than that of BP model. The modified PSO LS-SVM prediction model can explain the influences of cutting speed, feed rate and axial depth of cut on the surface roughness of titanium alloys. Either a higher cutting speed, a lower feed rate or a smaller axial depth of cut can lead to the decrease of surface roughness.