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PREDICTION OF SURFACE ROUGHNESS FOR END MILLING TITANIUM ALLOY USING MODIFIED PARTICLE SWARM OPTIMIZATION LS-SVM 被引量:1
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作者 刘春景 唐敦兵 +1 位作者 何华 陈兴强 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2013年第1期53-61,共9页
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 n... 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. 展开更多
关键词 titanium alloy cutting parameter surface roughness prediction modeling modified pso LS-SVM
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Multi-component system maintenance optimization of a rail transit train based on opportunistic correlations 被引量:1
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作者 Jisheng Dai Rongjun Ding +1 位作者 Yong Fu Yong Qin 《Transportation Safety and Environment》 EI 2023年第4期182-191,共10页
ln order to deal with the problems of insufficient or excessive maintenance in the current maintenance activities of China transit trains,this paper develops a novel multi-component system maintenance optimization app... ln order to deal with the problems of insufficient or excessive maintenance in the current maintenance activities of China transit trains,this paper develops a novel multi-component system maintenance optimization approach based on an opportunistic correlation model.Based on the minimal reliability and failure rate change rule of each train component,the novel proposed maintenance optimization benefits from an improved opportunistic maintenance model with system structure correlation,fault correlation and reliability correlation under imperfect maintenance.Then,different maintenance modes can be determined by a proposed mainte-nance factor under the different conditions of components.Specifically,the reliability threshold of each component is also considered to optimize the maintenance cost by the system reliability and operational availability of the train.Furthermore,as the mentioned problem belongs to the NP-Hard optimization problems,a modified particle swarm optimization(PSO)with the improvement of inertia weight is proposed to cope with the optimization problem.Based on a specific case under the practical recorded failure data,the analysis shows that the proposed model and approach can effectively cut the maintenance cost. 展开更多
关键词 multi-component system maintenance optimization rail transit train opportunistic correlation modified particle swarm optimization(pso)
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