Performance improvement of machining parameters in electrical discharge machining process of tool steel 2714
Sameh Habib
International Journal of Manufacturing Technology and Management, 2018, vol. 32, issue 6, 533-551
Abstract:
Electrical discharge machining (EDM) is an extremely prominent machining process among newly developed non-traditional machining techniques for difficult-to-cut materials. Though a lot of research has been done to improve the process performance, optimal selection of process parameters for the best performance measures still remains a challenge. In this work, the performance of electrical discharge machining process when cutting hot work tool steel 2714 with copper electrodes has been investigated. Orthogonal array of L27 (3*4) based on the Taguchi experimental design is utilised to plan the experiments. Raw data is assessed by the analysis of variance (ANOVA) to find optimal conditions for response parameters. The main machining parameters such as pulse-on time, pulse-off time, discharge current and average machining voltage are chosen to determine the EDM response parameters such as material removal rate, surface roughness and gap size. Response tables and graphs are used to find the optimal parameter level in the EDM process.
Keywords: electrical discharge machining; EDM; Taguchi approach; material removal rate; surface roughness; gap size. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=95028 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmtma:v:32:y:2018:i:6:p:533-551
Access Statistics for this article
More articles in International Journal of Manufacturing Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().