The goal of this research is to identify the best set of process machining parameters for wire-EDM(Electrical Discharge Machining)cutting of hardened SKD11 steel when machining a curve profile.The multi-objective func...The goal of this research is to identify the best set of process machining parameters for wire-EDM(Electrical Discharge Machining)cutting of hardened SKD11 steel when machining a curve profile.The multi-objective function includes reducing surface roughness and increasing MRR(Material Removal Rate).The optimization process is prepared by using Taguchi method coupled Grey Relational Analysis.The obtained results revealed that Toff has the greatest influence on the average grey value(48.30%),followed by the influence of WF(Wire Feed,15.99%),VM(Cutting Voltage,9.33%),SV(Server Voltage,5.05%),Ton(Pulse on Time,1.81%),while SPD(Cutting Speed)has a negligible effect(0.89%).Moreover,using the optimal set of machining parameters generates in surface roughness of 1.25399mm and MRR of 26.5562 mm^(2)/min.The verification experiment and Anderson-Darling method demonstrate the validity of the proposed model,which can be utilized for estimating surface roughness and MRR.展开更多
Wire electrical discharge machining(wire-EDM)is an energy-intensive process,and its success relies on a correct selection of cutting parameters.It is vital to optimize energy consumption,along with productivity and qu...Wire electrical discharge machining(wire-EDM)is an energy-intensive process,and its success relies on a correct selection of cutting parameters.It is vital to optimize energy consumption,along with productivity and quality.This experimental study optimized three parameters in wire-EDM:pulse-on time,servo voltage,and voltage concerning machining time,electric power,total energy consumption,surface roughness,and material removal rate.Two different plate thicknesses(15.88 mm and 25.4 mm)were machined.An orthogonal array,signal-to-noise ratio,and means graphs,and an analysis of vari-ance(ANOVA),determine the effects and contribution of cutting parameters on responses.Pulse-on time is the most significant factor for almost all variables,with a percentage of contribution higher than 50%.Multi-objective optimization is conducted to accomplish a concurrent decrease in all variables.A case study is proposed to compute carbon dioxide(CO_(2))tons and electricity cost in wire-EDM,using cutting parameters from multi-objective optimization and starting values commonly employed to cut that tool steel.A sustainable manufacturing approach reduced 5.91%of the electricity cost and CO_(2)tons when machining the thin plate,and these responses were diminished by 14.09%for the thicker plate.Therefore,it is possible to enhance the sustainability of the process without decreasing its productivity and quality.展开更多
Wire breakages and spark absence are two typical machining failures that occur during wire electric discharge machining(wire-EDM),if appropriate parameter settings are not maintained.Even after several attempts to opt...Wire breakages and spark absence are two typical machining failures that occur during wire electric discharge machining(wire-EDM),if appropriate parameter settings are not maintained.Even after several attempts to optimize the process,machining failures cannot be eliminated completely.A n offline classification model is presented herein to predict machining failures.The aim of the current study is to develop a multiclass classification model using an artificial neural network(ANN).The training dataset comprises 81 full factorial experiments with three levels of pulse-on time,pulse-off time,servo voltage,and wire feed rate as input parameters.The classes are labeled as normal machining,spark absence,and wire breakage.The model accuracy is tested by conducting 20 confirmation experiments,and the model is discovered to be 95%accurate in classifying the machining outcomes.The effects of process parameters on the process failures are discussed and analyzed.A microstructural analysis of the machined surface and worn wire surface is conducted.The developed model proved to be an easy and fast solution for verifying and eliminating process failures.展开更多
文摘The goal of this research is to identify the best set of process machining parameters for wire-EDM(Electrical Discharge Machining)cutting of hardened SKD11 steel when machining a curve profile.The multi-objective function includes reducing surface roughness and increasing MRR(Material Removal Rate).The optimization process is prepared by using Taguchi method coupled Grey Relational Analysis.The obtained results revealed that Toff has the greatest influence on the average grey value(48.30%),followed by the influence of WF(Wire Feed,15.99%),VM(Cutting Voltage,9.33%),SV(Server Voltage,5.05%),Ton(Pulse on Time,1.81%),while SPD(Cutting Speed)has a negligible effect(0.89%).Moreover,using the optimal set of machining parameters generates in surface roughness of 1.25399mm and MRR of 26.5562 mm^(2)/min.The verification experiment and Anderson-Darling method demonstrate the validity of the proposed model,which can be utilized for estimating surface roughness and MRR.
文摘Wire electrical discharge machining(wire-EDM)is an energy-intensive process,and its success relies on a correct selection of cutting parameters.It is vital to optimize energy consumption,along with productivity and quality.This experimental study optimized three parameters in wire-EDM:pulse-on time,servo voltage,and voltage concerning machining time,electric power,total energy consumption,surface roughness,and material removal rate.Two different plate thicknesses(15.88 mm and 25.4 mm)were machined.An orthogonal array,signal-to-noise ratio,and means graphs,and an analysis of vari-ance(ANOVA),determine the effects and contribution of cutting parameters on responses.Pulse-on time is the most significant factor for almost all variables,with a percentage of contribution higher than 50%.Multi-objective optimization is conducted to accomplish a concurrent decrease in all variables.A case study is proposed to compute carbon dioxide(CO_(2))tons and electricity cost in wire-EDM,using cutting parameters from multi-objective optimization and starting values commonly employed to cut that tool steel.A sustainable manufacturing approach reduced 5.91%of the electricity cost and CO_(2)tons when machining the thin plate,and these responses were diminished by 14.09%for the thicker plate.Therefore,it is possible to enhance the sustainability of the process without decreasing its productivity and quality.
文摘Wire breakages and spark absence are two typical machining failures that occur during wire electric discharge machining(wire-EDM),if appropriate parameter settings are not maintained.Even after several attempts to optimize the process,machining failures cannot be eliminated completely.A n offline classification model is presented herein to predict machining failures.The aim of the current study is to develop a multiclass classification model using an artificial neural network(ANN).The training dataset comprises 81 full factorial experiments with three levels of pulse-on time,pulse-off time,servo voltage,and wire feed rate as input parameters.The classes are labeled as normal machining,spark absence,and wire breakage.The model accuracy is tested by conducting 20 confirmation experiments,and the model is discovered to be 95%accurate in classifying the machining outcomes.The effects of process parameters on the process failures are discussed and analyzed.A microstructural analysis of the machined surface and worn wire surface is conducted.The developed model proved to be an easy and fast solution for verifying and eliminating process failures.
