The work done in this work deals with the efficacy of cutting parameters on surface of EN-8 alloy steel.For knowing the optimal effects of cutting parameters response surface methodology was practiced subjected to cen...The work done in this work deals with the efficacy of cutting parameters on surface of EN-8 alloy steel.For knowing the optimal effects of cutting parameters response surface methodology was practiced subjected to central composite design matrix.The motive was to introduce an interaction among input parameters,i.e.,cutting speed,feed and depth of cut and output parameter,surface roughness.For this,second order response surface model was modeled.The foreseen values obtained were found to be fairly close to observed values,showed that the model could be practiced to forecast the surface roughness on EN-8 within the range of parameter studied.Contours and 3-D plots are generated to forecast the value of surface roughness.It was revealed that surface roughness decreases with increases in cutting speed and it increases with feed.However,there were found negligible or almost no implication of depth of cut on surface roughness whereas feed rate affected the surface roughness most.For lower surface roughness,the optimum values of each one were also evaluated.展开更多
Using LBR-370 numerical control lathe,high speed cutting was applied to AZ31 magnesium alloy.The influence of cutting parameters on microstructure,surface roughness and machining hardening were investigated by using t...Using LBR-370 numerical control lathe,high speed cutting was applied to AZ31 magnesium alloy.The influence of cutting parameters on microstructure,surface roughness and machining hardening were investigated by using the methods of single factor and orthogonal experiment.The results show that the cutting parameters have an important effect on microstructure,surface roughness and machine hardening.The depth of stress layer,roughness and hardening present a declining tendency with the increase of the cutting speed and also increase with the augment of the cutting depth and feed rate.Moreover,we established a prediction model of the roughness,which has an important guidance on actual machining process of magnesium alloy.展开更多
Pure iron is one of the difficult-to-machine materials due to its large chip deformation,adhesion,work-hardening,and built-up edges formation during machining.This leads to a large workpiece deformation and challenge ...Pure iron is one of the difficult-to-machine materials due to its large chip deformation,adhesion,work-hardening,and built-up edges formation during machining.This leads to a large workpiece deformation and challenge to meet the required technical indicators.Therefore,under varying the grain size of pure iron,the influence of cutting speed,feed,and depth of cut on the cutting force,heat generation,and machining residual stresses were explored in the turning process to improve the machinability without compromising the mechanical properties of the material.The experimental findings have depicted that the influence of grain size on cutting force in the precision turning process is not apparent.However,the cutting temperature and residual stress of machining fine-grain iron were much smaller than the coarse grain at all levels of cutting parameters.展开更多
The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plas...The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plasma cutting system,which has wider industrial applications.There are many researches pursuing at various area of plasma cutting technology,still the automated and optimized parameters value selection is challenging.The work is aimed to eliminate the manual mode of feeding the input parameters for cutting operation.At present,cutting parameters are fed by referring the past cut data information or with the assistance of experienced employers.The cutting process parameters selections will have direct impact on the quality of the material being cut,and life of the consumables.This paper is intended to automate the process parameters selection by developing the mathematical model with existing cutting process parameters database.In this,three different approaches,multiple regression,multiple polynomial regression and AI technique,are selected and analyzed with the mathematical relations developed between the different cutting process parameters.The accuracy and reliability of those methods are detailed.The advantage and disadvantage of those methods for optimal setting conditions are discussed.The appropriate method that can be preferred for automated and optimal settings are elucidated.Finally,the selected technique is checked for accuracy and reliability for the existing cut data.展开更多
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
The influence of surface conditions on the corrosion behavior of engineering structures has been paid more attention.However,there is still a lack of systematic research on the effect of cutting parameters on material...The influence of surface conditions on the corrosion behavior of engineering structures has been paid more attention.However,there is still a lack of systematic research on the effect of cutting parameters on material’s microstructure and performance in service.In this paper,the effect of cutting parameters on microstructure and corrosion behaviors of 304 stainless steel in simulated primary water is well investigated.The results show that different cutting parameters can cause the superficial layer a gradient microstructure with nanocrystalized layer on top and deformation band structures underneath.With the similar surface roughness,the deformation microstructure can be very different due to the different cutting parameters.The effect degree on the depth of deformation zone is feed rate>cutting depth>cutting speed.The larger feed rate,lower cutting depth,lower cutting rate may induce a deeper deformation zone.With the increasing depth away from the machined surface,the localized corrosion rate is decreased,and at the same depth the localized corrosion rate along the deformation bands is higher than that along the grain boundaries(GBs).The nanocrystalized surface has a smallest general corrosion rate due to the quick formation of Cr rich oxide film.However,once the corrosion penetrates through this nanocrystalized layer,subsequent preferential corrosion at deformation bands and GBs will dominate and may lead to the significant increase of corrosion rate of the component in high temperature pressurized water.展开更多
Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energ...Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.展开更多
文摘The work done in this work deals with the efficacy of cutting parameters on surface of EN-8 alloy steel.For knowing the optimal effects of cutting parameters response surface methodology was practiced subjected to central composite design matrix.The motive was to introduce an interaction among input parameters,i.e.,cutting speed,feed and depth of cut and output parameter,surface roughness.For this,second order response surface model was modeled.The foreseen values obtained were found to be fairly close to observed values,showed that the model could be practiced to forecast the surface roughness on EN-8 within the range of parameter studied.Contours and 3-D plots are generated to forecast the value of surface roughness.It was revealed that surface roughness decreases with increases in cutting speed and it increases with feed.However,there were found negligible or almost no implication of depth of cut on surface roughness whereas feed rate affected the surface roughness most.For lower surface roughness,the optimum values of each one were also evaluated.
基金National Natural Science Foundation of China(Grant No.51505143)Hunan Provincial Natural Science Foundation of China(Grant nos.14JJ3111)+1 种基金L.L.appreciates the financial supports from the China Postdoctoral Science Foundation(Grant No.2014M562128)Scientific Research Fund of Hunan Provincial Education Department(Grant no.14C0455).
文摘Using LBR-370 numerical control lathe,high speed cutting was applied to AZ31 magnesium alloy.The influence of cutting parameters on microstructure,surface roughness and machining hardening were investigated by using the methods of single factor and orthogonal experiment.The results show that the cutting parameters have an important effect on microstructure,surface roughness and machine hardening.The depth of stress layer,roughness and hardening present a declining tendency with the increase of the cutting speed and also increase with the augment of the cutting depth and feed rate.Moreover,we established a prediction model of the roughness,which has an important guidance on actual machining process of magnesium alloy.
基金National Defense Foundation Pre-Research Science Challenge Project(Grant No.JCKY2016212A506-0107)Development Funds of China Academy of Engineering Physics(Grant No.2015B0203029).
文摘Pure iron is one of the difficult-to-machine materials due to its large chip deformation,adhesion,work-hardening,and built-up edges formation during machining.This leads to a large workpiece deformation and challenge to meet the required technical indicators.Therefore,under varying the grain size of pure iron,the influence of cutting speed,feed,and depth of cut on the cutting force,heat generation,and machining residual stresses were explored in the turning process to improve the machinability without compromising the mechanical properties of the material.The experimental findings have depicted that the influence of grain size on cutting force in the precision turning process is not apparent.However,the cutting temperature and residual stress of machining fine-grain iron were much smaller than the coarse grain at all levels of cutting parameters.
文摘The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plasma cutting system,which has wider industrial applications.There are many researches pursuing at various area of plasma cutting technology,still the automated and optimized parameters value selection is challenging.The work is aimed to eliminate the manual mode of feeding the input parameters for cutting operation.At present,cutting parameters are fed by referring the past cut data information or with the assistance of experienced employers.The cutting process parameters selections will have direct impact on the quality of the material being cut,and life of the consumables.This paper is intended to automate the process parameters selection by developing the mathematical model with existing cutting process parameters database.In this,three different approaches,multiple regression,multiple polynomial regression and AI technique,are selected and analyzed with the mathematical relations developed between the different cutting process parameters.The accuracy and reliability of those methods are detailed.The advantage and disadvantage of those methods for optimal setting conditions are discussed.The appropriate method that can be preferred for automated and optimal settings are elucidated.Finally,the selected technique is checked for accuracy and reliability for the existing cut data.
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supported by National Natural Science Foundation of China(No.51771211)the National Key Research and Development Program of China(No.2017YFB0702100)Key Research Program of Frontier Sciences,Chinese Academy of Sciences(No.QYZDY-SSWJSC012)。
文摘The influence of surface conditions on the corrosion behavior of engineering structures has been paid more attention.However,there is still a lack of systematic research on the effect of cutting parameters on material’s microstructure and performance in service.In this paper,the effect of cutting parameters on microstructure and corrosion behaviors of 304 stainless steel in simulated primary water is well investigated.The results show that different cutting parameters can cause the superficial layer a gradient microstructure with nanocrystalized layer on top and deformation band structures underneath.With the similar surface roughness,the deformation microstructure can be very different due to the different cutting parameters.The effect degree on the depth of deformation zone is feed rate>cutting depth>cutting speed.The larger feed rate,lower cutting depth,lower cutting rate may induce a deeper deformation zone.With the increasing depth away from the machined surface,the localized corrosion rate is decreased,and at the same depth the localized corrosion rate along the deformation bands is higher than that along the grain boundaries(GBs).The nanocrystalized surface has a smallest general corrosion rate due to the quick formation of Cr rich oxide film.However,once the corrosion penetrates through this nanocrystalized layer,subsequent preferential corrosion at deformation bands and GBs will dominate and may lead to the significant increase of corrosion rate of the component in high temperature pressurized water.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.51905448)the Fundamental Research Funds for the Central Universities of China(Grant No.SWU119060)+1 种基金the Natural Science Foundation of Chongqing,China(Grant No.cstc2018jcyjAX0579)the Technological Innovation and Application Development of Chongqing,China(Grant No.cstc2019jscx-mbdx0118).
文摘Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.
