• Mar 24, 2016 News!Vol.3, No.3 has been indexed by EI(Inspec)!   [Click]
  • Sep 27, 2017 News!IJMMM Vol.6, No.3 has been published with online version. 16 peer reviewed articles are published in this issue.   [Click]
  • Sep 25, 2017 News!IJMMM Vol.6, No.2 has been published with online version. 16 peer reviewed articles are published in this issue.   [Click]
General Information
    • ISSN: 1793-8198
    • Frequency: Quarterly
    • DOI: 10.18178/IJMMM
    • Editor-in-Chief: Prof. K. M. Gupta, Prof. Ian McAndrew
    • Executive Editor: Ms. Cherry L. Chen
    • Abstracting/Indexing: EI (INSPEC, IET), Chemical Abstracts Services (CAS), Engineering & Technology Digital Library,  ProQuest, Crossref, Ulrich's Periodicals Directory, and Electronic Journals Library .
    • E-mail ijmmm@ejournal.net
Editor-in-chief
Prof. Ian McAndrew
Embry Riddle Aeronautical University, UK.
It is my honor to be the editor-in-chief of IJMMM. I will do my best to help develop this journal better.

IJMMM 2014 Vol.2(4): 335-338 ISSN: 1793-8198
DOI: 10.7763/IJMMM.2014.V2.152

Study of the Influence of Process Parameters on Surface Roughness When Inconel 718 Is Dry Turned Using CBN Cutting Tool by Artificial Neural Network Approach

M. V. R. D. Prasad, Yelamanchili Sravya, and Karri Sai Tejaswi
Abstract—Inconel 718, a nickel based super alloy is an extensively used alloy in the aerospace industry, marine industry, steam turbine power plants. It is difficult to cut material due to its properties like low thermal conductivity, work hardening etc and retains its strength at high temperatures. Due to low mach inability of this material the worked surface and subsurface are easily effected during machining operations. Therefore surface finish plays a vital role in machining Inconel 718. The objective of this paper is to obtain optimal turning process parameters like cutting speed, feed, and depth of cut resulting in an optimal value of surface roughness for machining Inconel 718 with Cubic Boron Nitride (CBN10) tool insert using Taguchi’s design of experiments approach. The experiments are carried out using L9 orthogonal array. Artificial Neural Networks is used to validate the experimental results.

Index Terms—Surface roughness, dry turning, Inconel 718, CBN cutting tool, artificial neural network.

M. V. R. D. Prasad is with the Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Bachupally, Hyderabad (e-mail: dpmandava.vnr@gmail.com )

[PDF]

Cite: M. V. R. D. Prasad, Yelamanchili Sravya, and Karri Sai Tejaswi, "Study of the Influence of Process Parameters on Surface Roughness When Inconel 718 Is Dry Turned Using CBN Cutting Tool by Artificial Neural Network Approach," International Journal of Materials, Mechanics and Manufacturing vol. 2, no. 4, pp. 335-338, 2014.

Copyright © 2008-2015. International Journal of Materials, Mechanics and Manufacturing. All rights reserved.
E-mail: ijmmm@ejournal.net