Surface roughness prediction of particulate composites using artificial neural networks in turning operation

Ramezani, M.

Growing Science » Decision Science Letters » Surface roughness prediction of particulate composites using artificial neural networks in turning operation

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Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print) Quarterly Publication Volume 4 Issue 3 pp. 419-424 , 2015

Surface roughness prediction of particulate composites using artificial neural networks in turning operation Pages 419-424 Download PDF

Authors: Mohammad Ramezani

DOI: 10.5267/j.dsl.2015.3.001

Keywords: Artificial Neural Network (ANN), Composites (PAMCs), Reinforced Aluminum Matrix, Surface Roughness Particulate, Turning

Abstract: A number of factors, e.g. cutting speed and feed rate, affect the surface roughness in machining process. In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. Moreover, four other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation of Particulate Reinforced Aluminum Matrix Composites (PAMCs) specimens with 0%, 5%, 10% and 15% filler. The aim of this work is to decrease the production cost and consequently increase the production rate of these materials for industry without any trial and error method procedure.

How to cite this paper

 Ramezani, M. (2015). Surface roughness prediction of particulate composites using artificial neural networks in turning operation.Decision Science Letters , 4(3), 419-424.

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