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Intelligent Modeling and Optimization of Material Removal Rate in Electric Discharge Diamond Grinding

[+] Author Affiliations
Pankaj Kumar Shrivastava

Vindhya Institute of Technology & Science, Satna, MP, India

Avanish Kumar Dubey

Motilal Nehru National Institute of Technology, Allahabad, UP, India

Paper No. MSEC2012-7252, pp. 431-438; 8 pages
doi:10.1115/MSEC2012-7252
From:
  • ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing
  • ASME 2012 International Manufacturing Science and Engineering Conference
  • Notre Dame, Indiana, USA, June 4–8, 2012
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5499-0
  • Copyright © 2012 by ASME

abstract

Metal matrix composites (MMCs) have wide applications in modern manufacturing industries due to their specific and improved technological characteristics such as high strength to weight ratio, high hardness, high thermal, corrosion and wear resistances. Such characteristics are highly demanded in automobile, aircraft and space research organizations. Shaping of MMCs has been a big challenge for manufacturing industries due to their superior mechanical properties and the peculiar microstructure composed of different phases in MMCs poses machining challenges. Unconventional machining methods have become an alternative to give desired shapes with intricate profiles and stringent design requirements. The aim of present research is to investigate the machining performance of copper-iron-carbide MMC using hybrid machining process, electric discharge diamond grinding (EDDG). A hybrid approach of neural network and genetic algorithm has been used to develop the intelligent model for material removal rate (MRR) and subsequent optimization with the experimental data obtained by scientifically designed experimentation.

Copyright © 2012 by ASME

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