Open AccessMulti response optimization in WEDM of H13 steel using hybrid optimization approach
Author(s) -
Lakhan Rathod,
N.S. Poonawala,
Ramesh Rudrapati
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/814/1/012015
Subject(s) - surface roughness , machining , electrical discharge machining , taguchi methods , design of experiments , tool steel , orthogonal array , mechanical engineering , surface finish , response surface methodology , die (integrated circuit) , process (computing) , engineering drawing , engineering , materials science , computer science , metallurgy , mathematics , composite material , statistics , machine learning , operating system
Surface roughness and material removal rate (MRR) are important technological parameters which describe quality of machined surfaces and productivity of machining process. Surface roughness and MRR are significantly influenced by many interactive process parameters dynamically but those are difficult to quantify adequately in any machining process. Wire electrical discharge machining (WEDM) is one of the advanced machining processes which used thin wire as cutting tool for creating intricate features on machined parts. In WEDM, optimizing surface roughness(Ra) and material removal rate (MRR) combinedly by controlling process variables namely pulse on time, pulse off time, wire feed, voltage gap, etc, is difficult task and much needed area of research. In the present work, investigation is made to study and optimize the surface roughness of H13 steel in WEDM. L16 orthogonal array of Taguchi methodology has been used to conduct the experiments. Analysis of variance (ANOVA) has been applied on experimental data to determine the significance of input parameters on surface roughness and MRR. Mathematical relationships are developed to correlate the machining parameters and output responses: surface roughness and MRR. Contour plots have been drawn to illustrate the combined effects of process parameters on output responses. Multi-objective jaya optimization algorithm (MJOA) applied on developed mathematical equations to predict the multi-responses simultaneously. From the study, it is stated that hybridTaguchi method, RSM and MJOA is useful for studying, modeling and optimizing the multiple responses: surface roughness and MRR, simultaneously in WEDM of H13 steel.