Optimization of Process Parameters of Powder Mixed Electric Discharge machining for D2 Steel (eBook, PDF)
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Bachelor Thesis from the year 2017 in the subject Engineering - General, , course: Mechanical Engineering, language: English, abstract: Electric Discharge Machining is mainly used for very hard materials. Now a days we know that product and process technology is very advance, many types of new materials are being developed which have very high strength, high thermal and electrical conductivity which are difficult to machine by traditional machining methods. So Non-conventional machining methods are used to machine such type of materials, EDM is also one of the Non-conventional machining method...
Bachelor Thesis from the year 2017 in the subject Engineering - General, , course: Mechanical Engineering, language: English, abstract: Electric Discharge Machining is mainly used for very hard materials. Now a days we know that product and process technology is very advance, many types of new materials are being developed which have very high strength, high thermal and electrical conductivity which are difficult to machine by traditional machining methods. So Non-conventional machining methods are used to machine such type of materials, EDM is also one of the Non-conventional machining methods which is used to machine such advance hard and brittle materials to satisfy the present days product needs like aerospace, mould, dies and other applications. Mixing of powders into dielectric fluid is a one of the recent advancement in the EDM process to improve its process capabilities and is known as Powder Mixed Electric Discharge Machining (PMEDM) process. The objective of this work is mainly to study the effect of various input parameters like pulse-on-time, discharge current, tool material and grit size on the various output parameters like MRR, TWR, Surface Roughness, Hole Diameter Variation and Micro structure. In this study, these output parameters are studied by using the ANOVA through Minitab17 software. By using this software, study the Means and S/N ratios for all these parameters. Optimization of the whole process through Grey Relational Analysis for obtains the better output response simultaneously. Improvement in output responses by using grey relational analysis are 64.84% in TWR, 52% in HDV, 49.78% in MRR and 41.57% in SR. Micro structure has been also observed through SEM analysis.
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