Influence of profile shift on the spur gear pair optimization

Abstract Due to the established load capacity calculations and extensive literature, gearbox design process is rather simple. In order to gain a competitive edge, transmission designers and manufacturers are forced to constantly improve the design process and the product quality. The gearbox design process can be improved by using genetic algorithm to determine the optimal values of gear pair parameters. In this paper, besides the frequently used gear optimization variables, i.e. the module, the face width, and the number of teeth, profile shift coefficients of both gears are included to create a five-variable process. Since the profile shift influences the form, zone and stress factors, as well as the transversal and face load factors, it affects the optimization results significantly. The gear pair volume served as a fitness function, while the tooth root bending strength and the contact pressure calculations were used as constraints. Optimization results of the pair without profile shift and the arbitrary profile shifted pair were compared. The genetic algorithm solution is found for three data sets; the solution is verified using commercial gear optimization software.

[1]  Cevdet Göloglu,et al.  A genetic approach to automate preliminary design of gear drives , 2009, Comput. Ind. Eng..

[2]  Mitsuo Gen,et al.  A solution method for optimal weight design problem of the gear using genetic algorithms , 1998 .

[3]  K. Miettinen,et al.  Quasi-random initial population for genetic algorithms , 2004 .

[4]  Hammoudi Abderazek,et al.  A differential evolution algorithm for tooth profile optimization with respect to balancing specific sliding coefficients of involute cylindrical spur and helical gears , 2015 .

[5]  Francisco T. Sanchez-Marin,et al.  Determination of the ISO face load factor in spur gear drives by the finite element modeling of gears and shafts , 2013 .

[6]  Jean-Luc Marcelin,et al.  Using genetic algorithms for the optimization of mechanisms , 2005 .

[7]  A. Diez-Ibarbia,et al.  Efficiency analysis of spur gears with a shifting profile , 2016 .

[8]  Wanming Zhai,et al.  Mesh stiffness evaluation of an internal spur gear pair with tooth profile shift , 2016 .

[9]  Zbigniew Michalewicz,et al.  An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms , 1991, ICGA.

[10]  Paul Bratley,et al.  Algorithm 659: Implementing Sobol's quasirandom sequence generator , 1988, TOMS.

[11]  A Almasi.  Latest technologies and modern guidelines on gear units , 2014 .

[12]  Zoran Milojevic,et al.  A practical approach to the optimization of gear trains with spur gears , 2012 .

[13]  Hammoudi Abderazek,et al.  Adaptive mixed differential evolution algorithm for bi-objective tooth profile spur gear optimization , 2017 .

[14]  V. Senthil Kumar,et al.  Optimization of asymmetric spur gear drives to improve the bending load capacity , 2008 .

[15]  Ovidiu Buiga,et al.  Automated optimal design of a two-stage helical gear reducer , 2010 .

[16]  R. Rao,et al.  Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms , 2010 .

[17]  Brian A. Shaw,et al.  External spur gear root bending stress: A comparison of ISO 6336:2006, AGMA 2101-D04, ANSYS finite element analysis and strain gauge techniques , 2017 .

[18]  Fatih Emre Boran,et al.  Optimization of module, shaft diameter and rolling bearing for spur gear through genetic algorithm , 2010, Expert Syst. Appl..

[19]  A. Kapelevich.  Direct Gear Design , 2013 .

[20]  Kaisa Miettinen,et al.  On initial populations of a genetic algorithm for continuous optimization problems , 2007, J. Glob. Optim..

[21]  Bart Koopman,et al.  The effect of tyre and rider properties on the stability of a bicycle , 2015 .

[22]  Francesco Pellicano,et al.  Optimum profile modifications of spur gears by means of genetic algorithms , 2008 .

[23]  Jean-Luc Marcelin,et al.  Genetic Optimisation of Gears , 2001 .