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
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.
