Parametric optimization of non-traditional machining processes using Taguchi method and super ranking concept

  • Shankar Chakraborty Jadavpur University
  • Partha Protim Das Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Majitar, Sikkim, India

Abstract

In order to achieve higher dimensional accuracy along with better surface quality, the conventional machining processes are now-a-days being replaced by non-traditional machining (NTM) processes, because of their ability to generate intricate shape geometries on various advanced engineering materials. In order to exploit their fullest machining potential, it is often recommended to operate those NTM processes at their optimal parametric settings. Several optimization tools and techniques are now available which can be effectively applied to obtain the optimal parametric conditions of those processes. In this paper, Taguchi method and super ranking concept are integrated together to present an efficient optimization technique for simultaneous optimization of three NTM processes, i.e. electro-discharge machining process, wire electro-discharge machining process and electro-chemical discharge drilling process. The derived results are validated with the help of developed regression equations which show that the proposed approach outperforms the other popular multi-response optimization techniques. Analysis of variance is also performed to identify the most influencing control parameters for the considered NTM processes. The developed response surface plots further help the process engineers in identifying the effects of various NTM process parameters on the calculated sum of squared rank values.

Published
2018-12-10
How to Cite
CHAKRABORTY, Shankar; DAS, Partha Protim. Parametric optimization of non-traditional machining processes using Taguchi method and super ranking concept. Yugoslav Journal of Operations Research, [S.l.], v. 29, n. 2, p. 249-271, dec. 2018. ISSN 2334-6043. Available at: <https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/663>. Date accessed: 24 nov. 2024.
Section
Articles

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