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Prediction of Surface Roughness for AISI 304 Steel with Solid Carbide Tools in End Milling Process Using Regression and ANN Models

  • Research Article - Mechanical Engineering
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Abstract

This paper focuses on two different models, namely regression mathematical and artificial neural network (ANN) models for predicting surface roughness. In the present work, surface roughness is taken as the response (output) variable measured during milling, while helix angle, spindle speed, feed and depth of cut are taken as input parameters. The design of experiments (DOE) technique is developed for four factors at five levels to conduct experiments. Experiments have been conducted for measuring surface roughness based on the DOE technique in a vertical machining centre on AISI 304 steel using an uncoated solid carbide end mill cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed-forward back-propagation ANN for the prediction of surface roughness. Predicted values of response by both models, i.e. regression and ANN, are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of surface roughness within the trained range.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, 641048, Tamil Nadu, India

    S. Kalidass

  2. Department of Mechanical Engineering, Annai Mathammal Sheela Engineering College, Erumapatty, Namakkal, 637013, Tamil Nadu, India

    P. Palanisamy

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  1. S. Kalidass
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  2. P. Palanisamy
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Correspondence to S. Kalidass.

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Kalidass, S., Palanisamy, P. Prediction of Surface Roughness for AISI 304 Steel with Solid Carbide Tools in End Milling Process Using Regression and ANN Models. Arab J Sci Eng 39, 8065–8075 (2014). https://doi.org/10.1007/s13369-014-1346-6

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  • DOI: https://doi.org/10.1007/s13369-014-1346-6

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