Research Article | OPEN ACCESS
Prediction and Optimization Approaches for Modeling and Selection of Optimum Machining Parameters in CNC down Milling Operation
1, 3Asaad A. Abdullah, 1Caihua Xiong, 1Xiaojian Zhang, 1Zhuang Kejia and 2Nasseer K. Bachache
1School of Mechanical Science and Engineering
2College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
3Department of Material Engineering, College of Engineering, University of Basrah, Basrah, Iraq
Research Journal of Applied Sciences, Engineering and Technology 2014 14:2908-2913
Received: May 16, 2013 | Accepted: October 19, 2013 | Published: April 12, 2014
Abstract
In this study, we suggested intelligent approach to predict and optimize the cutting parameters when down milling of 45# steel material with cutting tool PTHK- ((Ø10*20C*10D*75L) -4F-1.0R under dry condition. The experiments were performed statistically according to four factors with three levels in Taguchi experimental design method. Adaptive Neuro-fuzzy inference system is utilized to establish the relationship between the inputs and output parameter exploiting the Taguchi orthogonal array L27. The Particle Swarm Optimized-Adaptive Neuro- Fuzzy Inference System (PSOANFIS) is suggested to select the best cutting parameters providing the lower surface through from the experimental data using ANFIS models to predict objective functions. The PSOANFIS optimization approach that improves the surface quality from 0.212 to 0.202, as well as the cutting time is also reduced from 7.5 to 4.78 sec according to machining parameters before and after optimization process. From these results, it can be readily achieved that the advanced study is trusted and suitable for solving other problems encountered in metal cutting operations and the same surface roughness.
Keywords:
ANFIS, down milling process, Particle Swarm Optimization (PSO), surface roughness,
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Competing interests
The authors have no competing interests.
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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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