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Performance Analysis of Rapid Tool in Electrical Discharge Machining During Machining of Titanium Alloy (Ti6Al4V)

[+] Author Affiliations
Anshuman Kumar Sahu, Siba Sankar Mahapatra

National Institute of Technology, Rourkela, India

Paper No. DETC2018-85489, pp. V004T05A016; 12 pages
  • ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 23rd Design for Manufacturing and the Life Cycle Conference; 12th International Conference on Micro- and Nanosystems
  • Quebec City, Quebec, Canada, August 26–29, 2018
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5179-1
  • Copyright © 2018 by ASME


Titanium and its alloys are a class of metallic materials having high strength to weight ratio with excellent properties of resistance to temperature, corrosion and oxidation. These properties increase their use in aerospace, chemical and biomedical industries. Electrical discharge machining (EDM), a non-conventional machining process, is the most suitable process for the machining of titanium and its alloys. Generally, tool electrode for EDM application is prepared through various conventional and non-conventional machining processes. The cost of production of EDM electrodes accounts for more than 50% of the cost of the final product. Therefore, additive manufacturing (AM) technology can be suitably applied for direct manufacturing of the complex EDM electrodes. Selective laser sintering (SLS) is one of the appropriate AM processes for preparation of EDM tool electrode. In the present work, machining performance of the AlSi10Mg tool electrode produced through AM process along with copper and brass tool electrodes have been studied considering titanium alloy (Ti6Al4V) as work piece material and commercial grade EDM 30 oil as dielectric fluid. In addition to the tool electrodes, two more EDM parameters such as pulse-on-time (Ton) and discharge current (Ip) have been considered. Four performance measures like material removal rate (MRR), tool wear rate (TWR), average surface roughness (Ra) and surface crack density (SCD) are used to assess the machining performance. In order to reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L9 orthogonal array is used. Since the performance measures are conflicting in nature, grey relational analysis (GRA) is used to convert four performance measures into an equivalent single performance measure. The best parametric condition is reported for optimal grey relational grade.

Copyright © 2018 by ASME



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