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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Volume were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2003, 8(2), 235-243; doi:10.3390/mca8020235

Cutting Tool Condition Monitoring Using Surface Texture via Neural Network

Selçuk University, Technical Sci. Vocational High School. 42031, Konya, Turkey
Celal Bayar University, Faculty of Engineering, Manisa, Turkey
Authors to whom correspondence should be addressed.
Published: 1 August 2003
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For defining surface finish and monitoring tool wear is essential for optimisation of machining parameters and performing automated manufacturing systems. There is very close relationship between tool wear and surface finish parameters as surface roughness (Ra,) and maximum depth of profile (Rt). The machined surface reflects the rate of tool wear and the plot of surface pmvides reliable information about tool condition. In this paper an approach for estimating Ra,and Rt in milling process using the artificial neural networks is proposed. Feed-forward multi-layered neural networks, trained by the back-propagation algorithm are used. In training phase seven input parameters (v, f, d, Fx, Fy, Fz and Vb) and two output parameters are used and the network architecture is as 7x6x6x6x2. It was found that the ANN results are very close to the experimental resuks. The developed model can be used to define the quality of surface finish in tool condition monitoring systems.
Keywords: Tool condition monitoring; neural networks; surface texture analysis; tool wear Tool condition monitoring; neural networks; surface texture analysis; tool wear
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Sağlam, H.; Kaçar, H. Cutting Tool Condition Monitoring Using Surface Texture via Neural Network. Math. Comput. Appl. 2003, 8, 235-243.

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