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Appl. Sci. 2017, 7(10), 1064; doi:10.3390/app7101064

Mathematical Modelling and Optimization of Cutting Force, Tool Wear and Surface Roughness by Using Artificial Neural Network and Response Surface Methodology in Milling of Ti-6242S

1
Department of Mechanical Engineering, Faculty of Engineering, Dicle University, Diyarbakir 21280, Turkey
2
Department of Mechanical Engineering, Faculty of Engineering-Architecture, Batman University, Batman 72060, Turkey
*
Author to whom correspondence should be addressed.
Received: 21 September 2017 / Revised: 21 September 2017 / Accepted: 25 September 2017 / Published: 15 October 2017
(This article belongs to the Section Mechanical Engineering)
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Abstract

In this paper, an experimental study was conducted to determine the effect of different cutting parameters such as cutting speed, feed rate, and depth of cut on cutting force, surface roughness, and tool wear in the milling of Ti-6242S alloy using the cemented carbide (WC) end mills with a 10 mm diameter. Data obtained from experiments were defined both Artificial Neural Network (ANN) and Response Surface Methodology (RSM). ANN trained network using Levenberg-Marquardt (LM) and weights were trained. On the other hand, the mathematical models in RSM were created applying Box Behnken design. Values obtained from the ANN and the RSM was found to be very close to the data obtained from experimental studies. The lowest cutting force and surface roughness were obtained at high cutting speeds and low feed rate and depth of cut. The minimum tool wear was obtained at low cutting speed, feed rate, and depth of cut. View Full-Text
Keywords: cutting force; tool wear; surface roughness; ANN-RSM cutting force; tool wear; surface roughness; ANN-RSM
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kilickap, E.; Yardimeden, A.; Çelik, Y.H. Mathematical Modelling and Optimization of Cutting Force, Tool Wear and Surface Roughness by Using Artificial Neural Network and Response Surface Methodology in Milling of Ti-6242S. Appl. Sci. 2017, 7, 1064.

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