Design Optimization of Gears for Compact Size with Improved Strength

Abstract

Compact size with higher strength has always been a challenging task for gear designers. Perpetual need for such gears in various industrial applications remains newlinea focal area of considerable research interest. Research studies on design optimization of gears are being carried out by many researchers over the years. The newlinecurrent studies have found some unique research aspects in this direction such as newlineprofile shift coefficients, helix angle as design variables and scoring, contact ratio, newlineinvolute interference, specific sliding, thermal rating, face width constraint as design constraints. The work presented in this thesis utilizes various non-traditional newlineoptimization techniques to reduce the size with even improved strength of gear newlinepair. Out of various types of gears, current studies focus on commonly used spur newlinegear, helical gear and straight bevel gear. In view of above-mentioned aim, certain newlineobjectives have been identified out of the current research gaps from literature. newlineThe objectives are as follows a) Minimization of centre distance of spur gear pair newlinewith inclusion of scoring as design constraint. b) Conflicting bi-objective design newlineproblem on minimizing volume and minimizing tooth deflection of spur gear pair newlinewith varied tooth profile. c) Minimization of volume of two-stage helical gearbox newlinewith varying helix angle. d) Design optimization of helical gear pair with balanced newlinespecific sliding and modified tooth profile. e) Minimization of volume of straight newlinebevel gear with scoring failure as design constraint. f) In addition to these gears, newlineworm gear drive is considered to study for a possible reduction in power loss with newlineconsideration of temperature factor and scoring as design constraints. newlineTo fulfil the objectives as outlined above, mainly four non-traditional optimization newlinetechniques have been applied. These optimization techniques are:- i) Real-coded newlinegenetic algorithm (RCGA) ii) Simulated annealing (SA) iii) Accelerated particle swarm optimization (APSO) iv) Non-sorting genetic algorithm-II (NSGA-II).