Volume 2, 2015
|Number of page(s)||19|
|Published online||17 November 2015|
Modeling and optimization of Electrical Discharge Machining (EDM) using statistical design
Department of Mechanical Design and Production Engineering, Cairo University, 12613
2 Department of Mechanical Engineering, The American University in Cairo, 11835 Cairo, Egypt
* e-mail: Mohamed@aucegypt.edu
Accepted: 22 September 2015
Modeling and optimization of nontraditional machining is still an ongoing area of research. The objective of this work is to optimize Electrical Discharge Machining process parameters of Aluminum-multiwall carbon Nanotube composites (AL-CNT) model. Material Removal Rate (MRR), Wear Electrode Ratio (EWR) and Average Surface Roughness (Ra) are primary objectives. The Machining parameters are machining-on time (sec), discharge current (A), voltage (V), total depth of cut (mm), and %wt. CNT added. Mathematical models for all responses as function of significant process parameters are developed using Response Surface Methodology (RSM). Experimental results show optimum levels for material removal rate are %wt. CNT (0%), high level of discharge current (6A) and low level of voltage (50 V) while optimum levels for Electrode wear ratio are %wt. CNT (5%), high level of discharge current (6A) and optimum levels for average surface roughness are %wt. CNT (0%), low level of discharge current (2A) and high level of depth of cut (1 mm). Single-objective optimization is formulated and solved via Genetic Algorithm. Multi-objective optimization model is then formulated for the three responses of interest. This methodology gathers experimental results, builds mathematical models in the domain of interest and optimizes the process models. As such, process analysis, modeling, design and optimization are achieved.
Key words: Taguchi approach / Response Surface Methodology (RSM) / AL-CNT composites / Electrical Discharge Machining (EDM) / Modeling and optimization
© H.A. Hegab et al., Published by EDP Sciences, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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