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
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
基金Supported by the National Natural Science Foundation of China(51175262)the Trans-century Training Programme Foundation for the Talents of Humanities and Social Science by the State Education Commission(NCET-08)+3 种基金the Excellent Youth Foundation of Anhui Provincial Colleges and Universities(2010SQRL117)Anhui Provincia lNatural Science Foundation(1308085ME65)Jiangsu Province Science Foundation for Excellent Youths(BK201210111)Jiangsu Province Industry-Academy-Research Grant(BY201220116)
文摘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.
基金funded by the Hunan Science and Technology‘Lotus Bud’Talent Support Program(Gr ant No.2022TJ-XH-009).
文摘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.