Abstract
Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed (N), feed rate (f) and depth of cut (d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3–5–1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN–PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.
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References
J.T. Lin, D. Bhattacharyya, V. Kecman, Multiple regression and neural networks analyses in composite machining. Compos. Sci. Technol. 63, 539–548 (2003)
D.R. Cramer, D.F. Taggart, Design and manufacture of an affordable advanced composite automotive body structure, Proceedings 19th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition, 19–23 October 2002 pp. 1–12
M. Chandrasekaran, M. Muralidhar, C. Murali Krishna, U.S. Dixit, Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int. J Adv. Manuf Technol. 46, 445–464 (2010)
D.K. Sonar, U.S. Dixit, D.K. Ojha, The application of radial basis function for predicting the surface roughness in a turning process. Int. J. Adv. Manuf. Technol. 27, 661–666 (2006)
K.A. Risbood, U.S. Dixit, A.D. Sahasrabudhe, Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J. Mater. Process. Technol. 132, 203–214 (2003)
C.C. Tsao, H. Hocheng, Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. J. Mater. Process. Technol. 203, 342–348 (2008)
S. Bharathi Raja, N. Baskar, Application of particle swarm optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Syst. Appl. 39, 5982–5989 (2012)
F. Cus, U. Zuperl, V. Gecevska, High speed end milling optimization using particle swarm intelligence. J. Achiev. Mater. Manuf. Eng. 22, 75–78 (2007)
J. Mallick, R. Mishra, I. Singh, PSO- ANN approach for estimating drilling induced damage in CFRP Laminates. Adv. Prod. Eng. Manag. 6, 95–104 (2011)
M.R. Razfar, M. Asadnia, M. Haghshenas, M. Farahnakian, The selection of milling parameters by the PSO based neural network modeling. Int. J. Adv. Manuf. Technol. 57, 49–60 (2011)
P.M. Dixit, U.S. Dixit, Modeling of metal forming and machining processes by finite element and soft computing methods (Springer, London, 2008)
A. Kohli, U.S. Dixit, A neural-network-based methodology for the prediction of surface roughness in a turning process. Int. J. Adv. Mnuf. Technol. 25, 118–129 (2005)
D. Devarasidappa and M. Chandrasekaran, Development of surface roughness prediction model and parameters optimization in machining Al-SiCp metal matrix composites, 4th International and 25th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2012), Jadavpur University, Kolkata, India, 14–16 December 2012
J. Kennedy, R. Eberhart, Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, (Perth, Australia,1995)
M. Chandrasekaran, M. Muralidhar, C. Murali Krishna, U.S. Dixit, Online Machining Optimization with Continuous Learning, Book chapter in computational methods for optimizing manufacturing technology: Models and techniques, ed. by J. Paulo Davim, Portugal, ISBN 978-1-4666-0128-4,2012
Acknowledgments
The authors gratefully acknowledge the North Eastern Regional Institute of Science & Technology (NERIST), Arunachal Pradesh, India for providing all facilities to carry out this research. Authors also wish to thank the anonymous reviewers of this journal.
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Chandrasekaran, M., Tamang, S. ANN–PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining. J. Inst. Eng. India Ser. C 98, 395–401 (2017). https://doi.org/10.1007/s40032-016-0276-3
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DOI: https://doi.org/10.1007/s40032-016-0276-3