Title: Experimental investigation and optimisation of WEDM process for machining maraging steel using neural network based Jaya algorithm

Authors: Ruma Sen; Bikash Choudhuri; John Deb Barma; Prasun Chakraborti

Addresses: Mechanical Engineering Department, National Institute of Technology, Agartala, India ' Mechanical Engineering Department, National Institute of Technology, Agartala, India ' Mechanical Engineering Department, National Institute of Technology, Agartala, India ' Mechanical Engineering Department, National Institute of Technology, Agartala, India

Abstract: Wire electric discharge machining has become one of the popular machining processes used for generating complex geometry on electrically conductive materials. Due to its complex behaviour, a correlation between parameters and machining characteristics has been established by a neural network model. This study also recommends an optimal setting of process parameters with an aim to improve machining performance, which is achieved by using Jaya algorithm. Experiments were conducted on maraging steel 300 using silver coated brass wire to study the effects of process parameters (pulse on time, pulse off time, peak current, and servo voltage and wire tension) on the performance characteristics such as root mean square roughness, cutting speed, and kerf width. From the study, it is revealed that pulse on time is the predominant factor that mostly influences the machining characteristics. According to the analysis of results, the most suitable parametric combinations which obtained from Jaya algorithm offer the best performance characteristics.

Keywords: wire electric discharge machining; WEDM; maraging steel; BPNN; Jaya.

DOI: 10.1504/IJMMM.2018.094733

International Journal of Machining and Machinability of Materials, 2018 Vol.20 No.4, pp.387 - 399

Received: 14 Dec 2016
Accepted: 22 Oct 2017
Published online: 14 Sep 2018 *

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