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Research Article

Process parameter improvement for NITi's electrical discharge machining (EDM) process utilizing the TOPSIS approach

Atish ManePradeep V. Jadhav

Mechanical Eng. Bharati Vidyapeeth Deemed University, College of Engineering, Pune, Maharashtra, India

Keywords

Abstract




Electro discharge Machine (EDM);

Shape memory alloy;

Surface roughness (Ra);

Material removal rate (MRR); TOPSIS

The current work focuses on finding the ideal set of Electro Discharge Machine (EDM) process variables for machining Shape memory alloy (NiTi). NiTi alloy is a significant class of smart material with several unique properties. There are numerous uses for NiTi in the security, marine, biomedical, and aerospace industries. NiTi is particularly difficult to cut using conventional machining methods due to its hardness; nevertheless, the material can be removed using an electric discharge machining technique. The experiments were carried out using Taguchi’s L27 orthogonal array. A Multi-criteria decision-making (MCDM) technique known as TOPSIS is used to optimize the response performance variables of material removal rate (MRR) and surface roughness (SR). TOPSIS combines multiple objectives into a single objective and provides the optimum set of parameters, From the optimization results, the optimal combination of process parameters is obtained at Voltage=30V, Discharge Current= 20A, Ton=35µs, Toff=8µs. Confirmatory experiments show a satisfactory improvement of preference values utilizing TOPSIS in the EDM experimental and initial settings of 1.82.

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01/01/2024 Best Paper Award: The winners of 2023 Best Paper Award of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Author Awards section.


01/01/2024 Most Cited Paper Award: The winners of 2023 Most Cited Paper Award of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Author Awards section.


01/01/2024 Reviewer Awards: The winners of 2023 reviewer awards of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Reviewer Awards section.


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2023 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.



2023 Best Paper Award:

The paper authored by Ferzan Fidan, Naim Aslan, Mümin Mehmet Koç entitled as “Morpho-structural and compressive mechanical properties of graphene oxide reinforced hydroxyapatite scaffolds for bone tissue applications” is awarded.



2023 Most Cited Paper Award:

The paper authored by Ercan Işık, Ehsan Harirchian, Hüseyin Bilgin, Kirti Jadhav entitled as “The effect of material strength and discontinuity in RC structures according to different site-specific design spectra" is awarded.


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