PREDICTION OF SURFACE ROUGHNESS FOR END MILLING TITANIUM ALLOY USING MODIFIED PARTICLE SWARM OPTIMIZATION LS-SVM

It is difficult to construct the prediction model for titanium alloy through analyzing the formation mecha- nism of surface roughness due to the complicated relation between influential factors and surface roughness. A no- vel algorithm based on the modified particle swarm optimization （PSO） least square support vector machine （LS- SVM） 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.     

基于响应曲面车削加工表面粗糙度稳健性分析

随着现代科学技术的发展,对零件的表面加工质量提出了越来越高的要求。但由于机械加工过程较复杂,各种参数都不同程度地影响着表面粗糙度,表面粗糙度分析时,很难建立显式的解析模型。本文提出基于响应曲面法建立表面粗糙度可靠性指标对应刀具结构影响参数的灵敏度分析模型。结果表明,表面粗糙度对刀尖圆角半径r的变化最为敏感,对主偏角κ的变化敏感次之,对前角γ变化不敏感;刀尖圆角半径、主偏角和前角对表面粗糙度的贡献率分别为56.82%,27.84%和9.75%。