The present investigation focuses on the parametric influence of machining parameters on the surface finish obtained in turning of glass fiber reinforced polymer （GFRP） composites. The experiments were conducted bas...The present investigation focuses on the parametric influence of machining parameters on the surface finish obtained in turning of glass fiber reinforced polymer （GFRP） composites. The experiments were conducted based on Taguchi＇s experimental design technique. Response surface methodology and analysis of variance （ANOVA） were used to evaluate the composite machining process to perform the optimization. The results revealed that the feed rate was main influencing parameter on the surface roughness. The surface roughness increased with increasing the feed rate but decreased with increasing the cutting speed. Among the other parameters, depth of cut was more insensitive. The predicted values and measured values were fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of GFRP composites with 95% confidence intervals. Using such model could remarkablely save the time and cost.展开更多
In recent years, glass fiber reinforced plastics (GFRP) are being extensively used in variety of engineering applications in many different fields such as aerospace, oil, gas and process industries. However, the users...In recent years, glass fiber reinforced plastics (GFRP) are being extensively used in variety of engineering applications in many different fields such as aerospace, oil, gas and process industries. However, the users of FRP are facing difficulties to machine it, because of fiber delamination, fiber pull out, short tool life, matrix debonding, burning and formation of powder like chips. The present investigation focuses on the optimization of machining parameters for surface roughness of glass fiber reinforced plastics (GFRP) using design of experiments (DoE). The machining parameters considered were speed, feed, depth of cut and workpiece (fiber orientation). An attempt was made to analyse the influence of factors and their interactions during machining. The results of the present study gives the optimal combination of machining parameters and this will help to improve the machining requirements of GFRP composites.展开更多
This work,examines the Surface Roughness(SR)of composite consisting Aluminium alloy(AA6061),Magnesium and Rock dust during turning process.To study the performance,three different test specimens with different constit...This work,examines the Surface Roughness(SR)of composite consisting Aluminium alloy(AA6061),Magnesium and Rock dust during turning process.To study the performance,three different test specimens with different constituents of Al 6061-T6,AZ31 and Rock dust were prepared by stir casting method.Turning experiments were carried out using MTAB Siemens-CNC lathe.The input parameters for machining are speed,depth of cut&feed and output response is surface roughness For each test specimen,there are 15 turning operations were performed using Box-Ben hen Design approach.To analyze the process parameters for SR,the models of ANOVA and Decision Tree(DT)algorithms were performed.Both algorithms are confirmed that,speed is the most significant factor for SR.The addition of AZ 31 with 1%and rock dust of 2%in AA6061 produced better surface finish.Regression models of linear regression,multilayer perception and support vector regression from data science were formulated to find the relationship between variables.Among these models multi layer perception produced minimum root mean square error.展开更多
The prediction and optimization of surface roughness values remain a critical concern in nano-fluids based minimum quantity lubrication (NFMQL) turning of titanium (grade-2) alloys.Here,we discuss an application of re...The prediction and optimization of surface roughness values remain a critical concern in nano-fluids based minimum quantity lubrication (NFMQL) turning of titanium (grade-2) alloys.Here,we discuss an application of response surface methodology with Box-Cox transformation to determine the optimal cutting parameters for three surface roughness values,i.e.,Ra,Rq,and Rz,in turning of titanium alloy under the NFMQL condition.The surface roughness prediction model has been established based on the selected input parameters such as cutting speed,feed rate,approach angle,and different nano-fluids used.Then the multiple regression technique is used to find the relationship between the given responses and input parameter.Further,the experimental data were optimized through the desirability function approach.The findings from the current investigation showed that feed rate is the most effective parameter followed by cutting speed,different nano-fluids,and approach angle on Ra and Rq values,whereas cutting speed is more effective in the case of Rz under NFMQL conditions.Moreover,the predicted results are comparatively near to the experimental values and hence,the established models of RSM using Box-Cox transformation can be used for prediction satisfactorily.展开更多
Green cutting has become focus of attention in ecological and environmental protection. Steam is cheap, poilution-free and eco-friendly, and then is a good and economical coolant and lubricant. Steam generator and ste...Green cutting has become focus of attention in ecological and environmental protection. Steam is cheap, poilution-free and eco-friendly, and then is a good and economical coolant and lubricant. Steam generator and steam feeding system were developed to generate and feed steam. Comparative experiments were carried out in cutting AA6061-15 vol.% SiC （25 p.m particle size）, with cubic boron nitride （CBN） insert KB-90 grade under the conditions of compressed air, oil water emulsion, steam as coolant and lubricant, and dry cutting, respectively. The experimental results show that, with steam as coolant and lubricant, gradual reduction in the cutting force, friction coefficient, surface roughness and cutting temperature values were observed. Further, there was reduction in built up edge formation. It is proved that use of water steam as coolant and lubricant is environmentally friendly.展开更多
文摘The present investigation focuses on the parametric influence of machining parameters on the surface finish obtained in turning of glass fiber reinforced polymer （GFRP） composites. The experiments were conducted based on Taguchi＇s experimental design technique. Response surface methodology and analysis of variance （ANOVA） were used to evaluate the composite machining process to perform the optimization. The results revealed that the feed rate was main influencing parameter on the surface roughness. The surface roughness increased with increasing the feed rate but decreased with increasing the cutting speed. Among the other parameters, depth of cut was more insensitive. The predicted values and measured values were fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of GFRP composites with 95% confidence intervals. Using such model could remarkablely save the time and cost.
文摘In recent years, glass fiber reinforced plastics (GFRP) are being extensively used in variety of engineering applications in many different fields such as aerospace, oil, gas and process industries. However, the users of FRP are facing difficulties to machine it, because of fiber delamination, fiber pull out, short tool life, matrix debonding, burning and formation of powder like chips. The present investigation focuses on the optimization of machining parameters for surface roughness of glass fiber reinforced plastics (GFRP) using design of experiments (DoE). The machining parameters considered were speed, feed, depth of cut and workpiece (fiber orientation). An attempt was made to analyse the influence of factors and their interactions during machining. The results of the present study gives the optimal combination of machining parameters and this will help to improve the machining requirements of GFRP composites.
文摘This work,examines the Surface Roughness(SR)of composite consisting Aluminium alloy(AA6061),Magnesium and Rock dust during turning process.To study the performance,three different test specimens with different constituents of Al 6061-T6,AZ31 and Rock dust were prepared by stir casting method.Turning experiments were carried out using MTAB Siemens-CNC lathe.The input parameters for machining are speed,depth of cut&feed and output response is surface roughness For each test specimen,there are 15 turning operations were performed using Box-Ben hen Design approach.To analyze the process parameters for SR,the models of ANOVA and Decision Tree(DT)algorithms were performed.Both algorithms are confirmed that,speed is the most significant factor for SR.The addition of AZ 31 with 1%and rock dust of 2%in AA6061 produced better surface finish.Regression models of linear regression,multilayer perception and support vector regression from data science were formulated to find the relationship between variables.Among these models multi layer perception produced minimum root mean square error.
文摘The prediction and optimization of surface roughness values remain a critical concern in nano-fluids based minimum quantity lubrication (NFMQL) turning of titanium (grade-2) alloys.Here,we discuss an application of response surface methodology with Box-Cox transformation to determine the optimal cutting parameters for three surface roughness values,i.e.,Ra,Rq,and Rz,in turning of titanium alloy under the NFMQL condition.The surface roughness prediction model has been established based on the selected input parameters such as cutting speed,feed rate,approach angle,and different nano-fluids used.Then the multiple regression technique is used to find the relationship between the given responses and input parameter.Further,the experimental data were optimized through the desirability function approach.The findings from the current investigation showed that feed rate is the most effective parameter followed by cutting speed,different nano-fluids,and approach angle on Ra and Rq values,whereas cutting speed is more effective in the case of Rz under NFMQL conditions.Moreover,the predicted results are comparatively near to the experimental values and hence,the established models of RSM using Box-Cox transformation can be used for prediction satisfactorily.
文摘Green cutting has become focus of attention in ecological and environmental protection. Steam is cheap, poilution-free and eco-friendly, and then is a good and economical coolant and lubricant. Steam generator and steam feeding system were developed to generate and feed steam. Comparative experiments were carried out in cutting AA6061-15 vol.% SiC （25 p.m particle size）, with cubic boron nitride （CBN） insert KB-90 grade under the conditions of compressed air, oil water emulsion, steam as coolant and lubricant, and dry cutting, respectively. The experimental results show that, with steam as coolant and lubricant, gradual reduction in the cutting force, friction coefficient, surface roughness and cutting temperature values were observed. Further, there was reduction in built up edge formation. It is proved that use of water steam as coolant and lubricant is environmentally friendly.
