Authors: Pankaj Kumar Shrivastava; Avanish Kumar Dubey
Addresses: Mechanical Engineering Department, AKS University, Satna – 485001, Madhya Pradesh, India ' Mechanical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad – 211004, Uttar Pradesh, India
Abstract: The high wheel wear rate (WWR) is one of the limiting factors during electrical discharge abrasive grinding (EDAG) of ferrous alloys using diamond abrasive. High WWR may adversely affect the material removal rate (MRR) during EDAG. In the present research, the performance of cubic boron nitride (CBN) abrasive has been explored during EDAG of high speed steel by using L27 orthogonal array design of experiments. The performances of CBN abrasive have been compared with the diamond abrasive by considering MRR and WWR as quality characteristics. The performances of CBN abrasive have been found much better than that of diamond abrasive for both the quality characteristics. Further, the modelling and optimisation of the above two quality characteristics have been done by using hybrid artificial neural network and genetic algorithm approach. Optimisation results show considerable improvement in both MRR and WWR.
Keywords: artificial neural networks; ANNs; CBN abrasives; electrical discharge abrasive grinding; EDAG; genetic algorithms; GAs; optimisation; wheel wear rate; WWR; ferrous alloys; diamond abrasive; material removal rate; MRR; cubic boron nitride; steel grinding; high speed steel; orthogonal array; design of experiments; DOE.
International Journal of Abrasive Technology, 2015 Vol.7 No.2, pp.90 - 106
Received: 14 Apr 2015
Accepted: 16 Jul 2015
Published online: 18 Dec 2015 *