Open AccessEnhancement of surface quality in Wire EDM machining of Magnesium alloy using ANN modeling approach
Author(s) -
M. Sreenivasulu,
A. Muniappan,
G. Bharathiraja,
N. Karunagaran
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/912/3/032073
Subject(s) - machining , electrical discharge machining , mechanical engineering , voltage , quality (philosophy) , process (computing) , response surface methodology , magnesium alloy , materials science , engineering , manufacturing engineering , computer science , magnesium , metallurgy , electrical engineering , machine learning , philosophy , epistemology , operating system
Wire electrical discharge machining is an sophisticated technology that contracts with very high speed cutting performance characteristics and precision machining process. In This paper deals about the surface quality enhancement by demonstrating various device parameters using ANN modeling for wire EDM process and the work piece required for machining was magnesium mix alloy. For exploratory game plan the machining parameters for example pulse off time, pulse on time, wire feed and current(voltage) were considered. The experiment was organized by Response Surface Methodology (RSM), Central Composite Design (CCD) was used. Surface quality was foreseen by ANN modeling. Different enlistment limits were utilized to improve the methodology parameters for surface quality. The foreseen characteristics were uncommonly near the test regard and the best assortment was 4.3%. Confirmation test was moreover coordinated to endorse the results and ANN predicted results have been seen as in exceptional concurrence with test revelations.