Application of Genetic Algorithm (GA) for Optimum Design of Module, Shaft Diameter and Bearing for Bevel Gearbox

Faruk Mendi; Tamer Baskal; Mustafa Kemal Külekci

doi:10.3139/120.110349

Article

Application of Genetic Algorithm (GA) for Optimum Design of Module, Shaft Diameter and Bearing for Bevel Gearbox

Faruk Mendi , Tamer Baskal and Mustafa Kemal Külekci

Published/Copyright: May 26, 2013

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From the journal Materials Testing Volume 54 Issue 6

Abstract

In this study selection of optimum module, shaft diameter and rolling bearing for conical gear has been done using genetic algorithm (GA). GA, is a novel stochastic method of optimization. GAs are based on the principles of natural selection and evolutionary theory. Objective function was optimized for the design variables between determined boundary values. The GA was constrained by taking into account the power, moment, velocity, wall thickness and bearing distances. Tooth strength and surface crush were considered to be design constraints for module optimization. The other algorithm constraints are maximum bending and torsion moments for shaft optimization, and working life for bearing optimization.

Kurzfassung

In der diesem Beitrag zugrunde liegenden Studie wurde die Auswahl des optimalen Moduls, des Achsendurchmessers und der Rollenlagerung für ein Kegelradgetriebe mittels eines genetischen Algorithmus’ (GA) durchgeführt. Bei GAs handelt es sich um ein neues stochastisches Optimierungsverfahren. GAs basieren auf den Prinzipien der natürlichen Auslese und der Evolutionstheorie. Die objektive Funktion wurde innerhalb bestimmter Grenzwerte für die Designvariablen optimiert. Der GA wurde eingeschränkt, indem die Leistung, das Moment, die Geschwindigkeit, die Wanddicke und die Lagerabstände berücksichtigt wurden. Die Zahnfestigkeit und Oberflächenstöße wurden als Designparameter für die Moduloptimierung herangezogen. Die übrigen Algorithmenvariablen waren das maximale Biege- und Torsionsmoment für die Achsenoptimierung und die Betriebsdauer für die Lageroptimierung.

Faruk Mendi received his BS from the Department of Mechanical Education at Gazi University, Ankara, Turkey in 1970, and his MS and PhD degrees in Mechanical Education from Gazi University, Ankara, Turkey, in 1988 and 1992, respectively. Dr. Mendi is currently professor in the Department of Manufacturing Engineering at Gazi University. His research interest areas are CAD/CAM, cutting speed and power in machine tools, and die design for sheet metal forming.

Tamer Başkal is assistant professor in the Vocational High School of Kirikkale University. He obtained his PhD from Gazi University in 2006. His research interests include CAD/CAM, geometric modeling, computer graphics and software in CAD/CAM systems.

Mustafa Kemal Kulekci is presently professor in Mechanical Design Department at the Mersin University in Turkey. He received his BS and MS degrees in 1990 and 1996 respectively in Mechanical Design from Gazi University. He received his PhD degree in Industrial Technolgy in 2000 from Gazi University. His research interests include computer graphics, CAD/CAM, design for manufacturing, internet-based design and manufacturing.

References

1 I.Chakraborthy, V.Kumar, S.Nair, R.Tiwari: Rolling element bearing design through genetic algorithms, Engineering Optimization35 (2003), No. 6, pp. 649–65910.1080/03052150310001624403Search in Google Scholar

2 M.Yaman: Genetik Algoritma ile Kardan Mili Tasarımı, G. Ü. Fen Bilimleri Enstitüsü, Y. Lisans Tezi, Ankara (2007), pp. 47–59Search in Google Scholar

3 B. R.Rao, R.Tiwari: Optimum design of rolling element bearings using genetic algorithms, Mechanism and Machine Theory42 (2007), No. 2, pp. 233–25010.1016/j.mechmachtheory.2006.04.003Search in Google Scholar

4 M.Melanie: An introduction to Genetic Algorithms, A Bradford B, The MIT Press, London (1998)Search in Google Scholar

5 H.Saruhan, I.Uygur: Design optimisation of mechanical systems using genetic algorithms, SAÜ Fen Bilimleri Dergisi7 (2003), No. 2, pp. 77–84Search in Google Scholar

6 D.Goldberg: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Boston, MA, USA (1989)Search in Google Scholar

7 A.Bozacı: Makine Elemanları Cilt 2, Çağlayan yayınevi, İstanbul (2004), pp. 259–386Search in Google Scholar

8 M.Akkurt: Makine Elemanları, Birsen Yayınevi, İstanbul (2000), pp. 64–230Search in Google Scholar

9 O.Hasancebi, F.Erbatur: Evaluation of crossover techniques in genetic algorithm based optimum structural design, Computers and Structures, 78 (2000), No. 1 to 3, pp. 435–44810.1016/S0045-7949(00)00089-4Search in Google Scholar

10 J.Holland: Adaptation in Natural and Artificial Systems, University of Michigan, Ann Arbor Press (1975)Search in Google Scholar

11 H.Adeli, N.Cheng: Augmented Lagrangian genetic algorithm structural optimization, Journal of Aerospace Engineering7 (1994), No. 1, pp. 104–11810.1061/(ASCE)0893-1321(1994)7:1(104)Search in Google Scholar

12 M.Gen, R.Cheng: Genetic algorithms and engineering design, John Wiley & Sons, Canada (1997)10.1002/9780470172254Search in Google Scholar

13 R.Jang, C.Sun, E.Mizutani: Neuro-Fuzzy and Soft Computing, Upper Saddle River, Prentice-Hall (1997)Search in Google Scholar

Published Online: 2013-05-26

Published in Print: 2012-06-01

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https://doi.org/10.3139/120.110349