Optimal Solution for Gear Drive Design Using Population Based Algorithm


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Abstract


Optimization plays a fundamental role in numerous engineering applications such as process design, product design, re-engineering, new product development etc. In engineering, a best answer is achieved by comparison of some completely different solutions through utilization of previous downside information. Optimization algorithms provide systematic associated economical ways of constructing and comparing new design solutions. This enables us to understand a best trend, in order to boost solution efficiency and acquire the foremost optimal design impact. In this paper, a new Evolutionary Algorithm based Modified Artificial Immune System (MAIS) algorithm is used to optimize a gear drive design. The results are compared with an existing design.
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Keywords


Design Optimization; Helical Gear Drive; Modified Artificial Immune System and Multi-Objective Optimization

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References


David F. W. Yap, and Koh, S. P. and Tiong, S. K. , Mathematical function optimization using AIS antibody remainder method, International Journal of Machine Learning and Computing, Vol. 1, n.1, pp. 13-19, 2011.

Deb.K, J. Sundar, N. Udaya Bhaskara Roa and S. Chaudhuri, Reference point based multi-objective optimization using evolutionary algorithms, Int. Journal of computational Intelligence Research, Vol. 2, n. 3, pp. 273-286, 2006.

Coello, C., Cortés, N., Solving Multiobjective Optimization Problems Using an Artificial Immune System, Genetic Programming and Evolvable Machines, Vol. 6, pp.163-190, 2002.

Woldemariam, K.M. and Yen, G.G, Constrained optimization using artificial immune system, IEEE Congress on Evolutionary Computation (CEC), Spain, 2010, pp. 1-8.

Luh, G.C., Chueh, C. H., and Liu W.W. MOIA, Multi-objective Immune Algorithm, Engineering Optimization, Vol. 35, n. 2, pp. 143-164, 2003.

Garrett, S.M., Parameter-free, adaptive clonal selection, In Proc of Congress on Evolutionary Computing, Portland Oregon, USA, Vol. 1, 2004, pp. 1052-1058.

Juan Carlos Herrera–Lozada, Hiram Calvo and Hind Taud, A Micro Artificial Immune system, Polibits no.43 México ene./jun. 2011.

Padmanabhan, S., Srinivasa Raman, V., Asokan, P., Arunachalam, S. and Page, T., Design optimisation of bevel gear pair, Int. J. Design Engineering, Vol. 4, n. 4, pp.364–393, 2011.

Quancai Li; Xuetao Qiao; Cuirong Wu and Xingxing Wang,

The study on gear transmission multi-objective optimum design based on SQP algorithm, Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, Singapore (January 12, 2012); doi:10.1117/12.920249.

Deb. K and Jain, S, Multi-Speed Gearbox Design Using Multi-Objective Evolutionary Algorithms, Journal of Mechanical design, Vol.125, n.3, pp.609-619, 2002.

Dr.V.Jayakumar, Design of transmission Systems (Fourth Edition, Lakshmi Publications, 2012).

Dudley.D.W, Handbook of Practical Gear Design (McGraw –Hill Book Co: New York, 1994).

Faculty of Mechanical Engineering, PSG College of Technology, Design Data Book (PSG College of Technology, 2008, Coimbatore- 641004, Tamilnadu, India).


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