News Updates Thursday 23rd Nov 2017 :
  • Welcome to International Press Corporation, world's leading publishers, We have served more than 10000+ authors
  • Articles are invited in engineering, science, technology, management, industrial engg, biotechnology etc.
  • Paper submission last date of Nov/Dec 2017 extended to 25 Nov 2017, Submit online or at
  • Our journals are indexed in University of Regensburg Germany, Google Scholar, Cross Reference data bases
  • Applications for reviewers are invited and can be sent directly to concerned editor's mail

Experimental Investigation and Development of Multi Response ANN Modeling in Turning Al-SiCp MMC using Polycrystalline Diamond Tool

Author : Santosh Tamang and M.Chandrasekaran

Pages : 1-8, DOI:
Download PDF

Metal matrix composites (MMC) are widely used for producing components in automotive, aerospace and bio-medical industries because of their improved properties in comparison with non reinforced alloys. These materials are known as difficult-to-machine materials because of hard abrasive particles were used for reinforcement. Among these, the aluminium based silicon carbide particulate (Al-SiCp) reinforced MMC have received more commercial attention due to their high performance. In this work, machinability study and predictive model development was carried out for machining Al-SiCp MMC using polycrystalline diamond (PCD) tool. Turning experiments based on full factorial design (33), a total of 27 machining trials are carried out to study the effect of turning parameters viz., spindle speed (N), feed (f) and depth of cut (d) on the responses such as surface roughness (Ra) as product quality and material removal rate (MRR) as productivity improvement in the machining process. Multi response predictive modeling has been developed using artificial neural network (ANN). The ANN architecture having 3-6-2 is found to be optimum average percentage error of 4.46% for surface roughness and 7.26 % for material removal rate. The predictive model exhibit close correlation with the experimental result as confirmed by the validation test. The methodology found to be effective tool and can be developed with minimum effort.

Keywords: MMC, Turning, ANN, Ra, MRR.

Article published in International Conference on Advances in Mechanical Sciences 2014, Special Issue-2 (Feb 2014)




Call for Papers
  1. IJCET- Nov/Dec-2017 Issue

    Submission Last Date
    25 Nov 2017
  2. IJTT-Sept-2017
  3. IJAIE-Sept-2017
  4. IJCSB-Sept-2017
  • Inpressco Google Scholar
  • Inpressco Science Central
  • Inpressco Global impact factor
  • Inpressco aap

International Press corporation is licensed under a Creative Commons Attribution-Non Commercial NoDerivs 3.0 Unported License
©2010-2017 INPRESSCO® All Rights Reserved