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Parametric Optimization of WEDM of High Chromium High Carbon Die Steel Using ANN

[+] Author Affiliations
Y. M. Puri, N. V. Deshpande

Visvesvaraya National Institute of Technology

Paper No. IMECE2006-14306, pp. 167-175; 9 pages
doi:10.1115/IMECE2006-14306
From:
  • ASME 2006 International Mechanical Engineering Congress and Exposition
  • Manufacturing Engineering and Textile Engineering
  • Chicago, Illinois, USA, November 5 – 10, 2006
  • Conference Sponsors: Manufacturing Engineering Division and Textile Engineering Division
  • ISBN: 0-7918-4774-8 | eISBN: 0-7918-3790-4
  • Copyright © 2006 by ASME

abstract

In this research, single pass cutting of HCHC (High Chromium High Carbon) die steel AISI D3 Grade has been used where material removal rate (MRR) and surface roughness (SR) are of primary importance. In general, achieving a high level cutting speed with a better surface finish is extremely difficult task because in wire cut electric discharge machining (WEDM), no particular parametric combination is expected to yield simultaneously in the best MRR and the best SR. Hence it can be considered as multi objective optimization problem. This research presents an attempt at multi objective optimization of the process parametric combinations by modeling the process using artificial neural network (ANN). A feed forward back propagation neural network based on matrix experimental design is developed to model the WEDM process. Based on the developed model, different response parameters are calculated for various input parameter setting. Finally they are compared to find out optimal combination of machining parameter setting. Electronica make EZEECUT PLUS model has been used for experimentation and a methodology has been suggested to determine the optimal combination of control parameters in WEDM. Research findings in the area of machining HCHC die steel through WEDM process will open up a new horizon and will certainly solve various challenging problems faced by the engineers and die makers in the field of modern manufacturing industry.

Copyright © 2006 by ASME

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