Experimental investigation & optimisation of wire electrical discharge machining process parameters for Ni49Ti51 shape memory alloy

Goyal, Ashish and Rahman, Huzef U. R. and Che Ghani, S. A. (2021) Experimental investigation & optimisation of wire electrical discharge machining process parameters for Ni49Ti51 shape memory alloy. Journal of King Saud University - Engineering Sciences, 33 (2). pp. 129-135. ISSN 1018-3639. (Published)

[img]
Preview
Pdf (Open access)
Experimental investigation and optimisation of wire electrical discharge machining.pdf
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Preview

Abstract

Shape Memory Alloys (SMAs) are unique class of modern material with various functional properties such as pseudo-elasticity, biocompatibility, high specific strength, high corrosion resistance and high anti-fatigue property. The machining of these SMAs is difficult by conventional machining processes due to strain-hardening effect which changes the properties of materials and it is found that non-conventional machining processes are more suitable to machine them. In present investigations, the experiments were performed on wire electrical discharge machining (WEDM) to study the interaction effects of the process parameter on surface characteristics of Ni49Ti51 SMA’s by brass tool electrode. Peak current, pulse on time, pulse off time, wire tension and wire feed rate were taken as input parameters and their effect were analysed on material removal rate. Artificial neural network was adopted to develop and to train the experimental data using back-propagation neural network (BPNN) approach. The response surface methodology (RSM) was adopted to develop the second order mathematical based quadratic models. The recast layer formation and surface of machined materials were also analysed by the SEM characterization. It was noticed that the machined surface contains the surface cracks and uneven distribution of crater on the surface.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Shape memory alloy; Artificial neural network; Material removal rate
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculty/Division: Faculty of Mechanical and Automotive Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 30 Jul 2021 07:57
Last Modified: 30 Jul 2021 07:57
URI: http://umpir.ump.edu.my/id/eprint/31741
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item