Predictive Model for Surface Roughness Using ANN and RSM Approach

P. Suresh(1*), K. Marimuthu(2), S. Ranganathan(3)

(1) Bharathiyar University, India
(2) the Department of Mechanical Engineering at Coimbatore Institute of Technology, Coimbatore, India
(3) Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha University, Chennai, India
(*) Corresponding author


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Abstract


The application of computer modelling techniques are prevalent usage in scientific research. Artificial neural networks (ANN) and Response surface methodology (RSM) are some of the well established, and prominent in the literature, where computational based approaches are involved. In this work, an artificial neural network and response surface models have been developed, to predict the surface roughness of the machined surface. The experimental results are concurring well with the predicted models.Using these techniques, confirms satisfactory results and hence reducing testing time and cost. Comparatively predicted ANN values have good agreement with the experimental values with lower percentage of error.
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Keywords


Surface Roughness; Prediction; Artificial Neural Network; Response Surface Methodology

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References


W. Sha, K.L. Edwards, The use of artificial neural networks in materials science based research, Mater Des, Vol. 28, pp. 1747–1752, 2007.

Ramesh Babu, N., Arulmozhivarman, P., Forecasting of wind speed using artificial neural networks, (2012) International Review on Modelling and Simulations (IREMOS), 5 (5), pp. 2276-2280.

C. Abeesh, Basheera, A. Uday, Dabadea, Suhas S. Joshia, V.V. Bhanuprasadc, V.M. Gadreb, Modeling of surface roughness in precision machining of metal matrix composites using ANN, J Mater Process Technol, Vol. 197, pp. 439–444, 2008.

N. Muthukrishnana, J. Paulo Davim, Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis, J Mater Process Technol, Vol. 209, pp. 225–232, 2009.

Suresh, P., Marimuthu, K., Ranganathan, S., Determination of optimum parameters in turning of aluminium hybrid composites, (2013) International Review of Mechanical Engineering (IREME), 7 (1), pp. 115-125.

Adel Mahamood Hassan, Abdalla Alrashdan, Mohammed T. Hayajneh, Ahmad Turki Mayyas, Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network, J Mater Process Technol, Vol. 209, pp. 894–899, 2009.

Necat Altinkok, Rasit Koker, Neural network approach to prediction of bending strength and hardening behaviour of particulate reinforced (Al–Si–Mg)-aluminium matrix composites, Mater Des, Vol. 25, pp. 595–602, 2004.

T. A. El-Taweel, Multi-response optimization of EDM with Al–Cu–Si–TiC P/M composite electrode, Int J Adv Manuf Technol, Vol. 44, pp. 100–113, 2009.

Ko-Ta Chiang, Modeling and analysis of the effects of machining parameters on the performance characteristics in the EDM process of Al2O3+TiC mixed ceramic, Int J Adv Manuf Technol, Vol. 37, pp. 523–533, 2008.

Senthil Babu, S., Vinayagam, B.K., An improved prediction model for drilling characteristics of Al/SiC metal matrix composites, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 630-638.

Rasit Koker, Necat Altinkok, Adem Demir, Neural network based prediction of mechanical properties of particulate reinforced metal matrix composites using various training algorithms, Mater Des, Vol. 28, pp. 616–627, 2007.

Mohammed Hayajneh, Adel Mahamood Hassan, Abdalla Alrashdan, Ahmad Turki Mayyas, Prediction of tribological behavior of aluminium-copper based composite using artificial neural network, J alloys and compounds, Vol.470, pp. 584-588, 2009.

R. Panneerselvam, Design and analysis of experiments, PHI learning private limited, pp. 313-359, 2012.

U. Natarajan, P.R. Periyanan, S.H. Yang, Multiple-response optimization for micro-endmilling process using response surface methodology, Int J Adv Manuf Technol, DOI 10.1007/s00170-011-3156-2.

Ranganathan, S., Senthilvelan, T., Prediction of machining parameters of surface roughness of GFRP composite by applying ANN and RSM, (2012) International Review of Mechanical Engineering (IREME), 6 (5), pp. 1068-1073.

S. Ranganathan, T. Senthilvelan, G. Sriram, Evaluation of machining parameters of hot turning of stainless steel by applying ANN and RSM, Journal of Materials and Manufacturing Process, Vol. 25, pp. 1131-1141, 2010.


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