Potentiality Studies of Stainless Steel 304 Material for Production of Medical Equipment using Micro Electrical Discharge Machining (micro-EDM) Analysis and Modeling

Govindan Puthumana

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    Stainless steel 304 (SS304) is a material widely used for production of medical equipment mainly because of its anti-corrosive properties. It has excellent mechanical properties, strength and reliability because of which it is one of the best materials for fabrication of medical devices. This paper presents a systematic, scientific analysis, modeling study and optimization of quality characteristics of SS304 material by using micro-electrical discharge drilling process. The analysis of variance, main effects analysis, interactions analysis and study of contour plots were performed for three response variables; they are: material removal rate (MRR), volumetric tool wear rate (TWR) and geometric oversize. An overall increase in MRR is observed with an increase in capacitance, and the maximum increase was 61%. In the analysis of TWR, the volumetric tool electrode wear rate varies linearly with voltage. The interaction plots between voltage and capacitance showed that the lowest tool electrode wear rate is achieved at a capacitance of 0.10 μF at all levels of gap voltage. Capacitance is the only parameter influencing geometrical oversize in micro-EDM of SS304. The model equations for all the response variables and process parameters were developed. Grey relational analysis was used to optimize the micro-EDM quality characteristics, and the highest grey relational grade (GRG) of 0.8021 was obtained at a voltage of 100 V and a capacitance of 0.4 μF.
    Original languageEnglish
    JournalJournal of Advanced Manufacturing Research
    Volume1
    Issue number1
    Pages (from-to)18-33
    Publication statusPublished - 2016

    Keywords

    • Stainless steel 304
    • Micro-EDM
    • Process analysis
    • Modeling
    • Analysis of variance
    • Grey relational analysis

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