Optimizing the design of straight bevel gear with reduced scoring effect

Abstract

Purpose The purpose of this paper is to minimize the volume of straight bevel gear and to develop resistance towards scoring failure in the straight bevel gear. Two evolutionary and more advance optimization techniques were used for performing optimization of straight bevel gears, which will also save computational time and will be less computationally expensive compared to a previously used optimization for design optimization of straight bevel gear. Design/methodology/approach The following two different cases are considered for the study: the first mathematical model similar to that used earlier and without any modification to show efficiency of the optimization algorithm for straight bevel gear design optimization and the second mathematical model consist of constraints on scoring and contact ratio along with other generally used design constraints. Real coded genetic algorithm (RCGA) and accelerated particle swarm optimization (APSO) are used to optimize the straight bevel gear design. The effectiveness of the algorithms used has been validated by comparing the obtained results with previously published results. Findings It has been found that APSO and RCGA outperform other algorithms for straight bevel gear design. Optimized design values have reduced the scoring effect significantly. The values of the contact ratio obtained further enhances the meshing operation of the bevel gear drive by making it smoother and quieter. Originality/value Low volume is one of the essential requirements of gearing applications. Scoring is a critical gear failure aspect that leads to the broken tooth in both high speed and low-speed applications of gears. The occurrence of scoring is hard to detect early and analyse. Scoring failure and contact ratio have been introduced as design constraints in the mathematical model. So, the mathematical model demonstrated in this paper minimizes the volume of the straight bevel gear drive, which has been very less attempted in previous studies, with scoring and contact ratio as some of the important design constraints, which the objective function has been subjected to. Also, two advanced and evolutionary optimization algorithms have been used to implement the mathematical model to reduce the computational time required to attain the optimal solution.