An Improved Prediction Model for Drilling Characteristics of Al/SiC Metal Matrix Composites

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Aluminium Silicon Metal Matrix Composites (ASMMCs) are new generation engineering materials that possess improved physical and mechanical and thermal properties. The drilling of Al/SiC metal matrix composite has received a serious attention for many years. The drilling is a process of high complexity due to its special difficulties such as cutting in a closed and limited space, high cutting temperature and the difficulty of chip formation and removal. These types of operations, known as cutting speed, cutting environment, point angle, feed rate and devices. Surface finish has been an important factor of machining in predicting performance of any machining operation. A lot of researches have focused on modelling and prediction of surface roughness of Al/Sic MMCs, however, the modelling fails when the input models and output categorization varies. In this paper, we propose an improved prediction technique, which is from the inspiration of artificial neural network, to learn the drilling performance of MMCs under various inputs and output characteristics. This paper is considers two Al/SiC and Al/SiC/B4C MMCs and their different test data on drilling performance. The experimental results prove that the error gets minimized in the improved prediction model when compared to earlier neural models.
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Al/ Si; MMC; Prediction Model; Particle Swarm Optimization (PSO); Fine Tuning

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Sivarao, Castilo and Tajul, “Surface Roughness Prediction in Deep Drilling by Fuzzy Expert System”, International Journal of Mechanical & Mechatronics Engineering (IJMME), Vol. 9, No.9, pp.331 -335, 2009.

ErolKilickap, MesutHuseyinoglu and AhmetYardimeden, “Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm”, International Journal of Advanced Manufacturing Technolology, Vol. 52, No.1-4, pp.79–88, 2011.

C.C.Tsao, “Comparison between response surface methodology and radial basis function network for core-center drill in drilling composite materials”, International Journal of Advanced Manufacturing Technolology, Vol. 37, No.11-12, pp.1061–1068, 2008.

P.V. Gopal Krishna, K. Kishore and Syed Yousufuddin, “Performance of a Drill-Reamer (D-Reamer) Combination Tool”, Journal of Advanced Research in Mechanical Engineering Vol.1, No.1, pp.17-21, 2010.

S. Panda and S.S Mahapatra, “Parametric Optimisation of Multi-response Drilling Process using Grey based Taguchi Methods”, Proceedings of AIMS International Conference, Noida, New Delhi, 2008.

K. Alam, A.V. Mitrofanov and V.V. Silberschmidt, “Measurements of Surface Roughness in Conventional and Ultrasonically Assisted Bone Drilling”, American Journal of Biomedical Sciences, Vol.1, No.4, pp.312-320, 2009.

Sahoo P., Barman, T. K. and Routara, B. C., “Taguchi based fractal dimension modelling and optimization in CNC turning”, Advances in production engineering and management journal, Vol.3, No.4, pp205-217, 2008.

C. Natarajan, S. Muthu and P. Karuppuswamy, “Investigation of cutting parameters of surface roughness for a non-ferrous material using artificial neural network in CNC turning”, Journal of Mechanical Engineering Research, Vol. 3, No.1, pp. 1-14, 2011.

Mahapatra S.S , Amar Patnaik and Prabina Ku Patnaik, “Parametric Analysis and Optimization of Cutting Parameters for Turning arametric Analysis and Optimization of Cutting Parameters for Turning Operations based on Taguchi Method”, Proceedings of the International Conference on Global Manufacturing and Innovation , July 27-29, 2006.

P V S Suresh, P VenkateswaraRao and S G Deshmukh, “A genetic algorithmic approach for optimization of surface roughness prediction model”, International Journal of Machine Tools and Manufacture, Vol. 42, No. 6, pp. 675-680, 2002.

H. Hochenga and C.C. Tsao, “Effects of special drill bits on drilling-induced delamination of composite materials”, International Journal of Machine Tools and Manufacture, Vol. 46, No. 12–13, pp.1403–1416, 2006.

Wei-Chang Yeh and Chien-Hsing Lin, “A Squeeze Response Surface Methodology for Finding Symbolic Network Reliability Functions”, IEEE Transactions on Reliability, Vol. 58, No. 2, pp.374-382, 2009.

Donald E. Brown and Jeffrey B. Schamburg,“A Modified Response Surface Methodology for Knowledge Discovery with Simulations”, Technical Report on Systems and Information Engineering, Virginia University, 2004.

REN Yuan and BAI Guangchen, “New Neural Network Response Surface Methods for Reliability Analysis”, Chinese Journal of Aeronautics, Vol. 24, No.1, pp.25-31, 2011.

A. NoorulHaq, P. Marimuthu and R. Jeyapaul, “Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method”, The international journal of advanced manufacturing technology, Vol. 37, No. 3-4, pp.250-255, 2008.

C. Dhavamani and T. Alwarsamy, “Optimization of Cutting Parameters of Composite Materials using Genetic Algorithm”, European Journal of Scientific Research, Vol.63, No.2, pp.279-285, 2011.

R. Arokiadass, K. Palaniradja and N. Alagumoorthi, “Surface roughness prediction model in end milling of Al/SiCp MMC by carbide tools”, International Journal of Engineering, Science and Technology, Vol. 3, No. 6, pp. 78-87, 2011.

Alakesh Manna andKanwaljeet Singh, “An Experimental Investigation on Drilled Hole-Surface during Drilling of Al/Sic-MMC”, National Conference on Advancements and Futuristic Trends in Mechanical and Materials Engineering, 2011

T.S. Mahesh Babu and N.Muthu Krishnan, “An experimental Investigation of turning Al/SiC/B4CHybrid Metal Matrix Composites using ANOVA analysis”,Scholarly Journal of Engineering Research, Vol. 1(2), pp. 25-31, 2012

Abdolali, A., Oraizi, H., Tavakoli, A., Determination of dispersion relations for ultra wide band radar absorbing materials, (2010) International Review of Electrical Engineering (IREE), 5 (2), pp. 779-784.

Reddy, V.N.B., Rao, S.N., Babu, C.S., Advanced modulating techniques for multilevel inverters by using FPGA, (2010) International Review of Electrical Engineering (IREE), 5 (3), pp. 842-848.

Ashour Z. H., Hashem S. R. and Fayed H. A., A new approach for combining neural networks during training for time series modeling, (2007) International Review of Electrical Engineering (IREE), 2 (5), pp. 745-750.

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.

Shanmughasundaram, P., Subramanian, R., Ravikumar, A.R., Influence of fly ash/Gr reinforcements on corrosion behaviour of aluminium matrix composites, (2012) International Review of Mechanical Engineering (IREME), 6 (4), pp. 790-795.

Maguteeswaran, R., Sivasubramanian, R., Suresh, V., Analysis and optimization of LM25 aluminum alloy composites reinforced with iron oxide [FE3O4], (2012) International Review of Mechanical Engineering (IREME), 6 (7), pp. 1449-1452.


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