Development of Optimization Design Software for Bevel Gear Based on Integer Serial Number Encoding Genetic Algorithm

Abstract

Bevel gear drive is widely used, quality of which not only affects its own transmission performance, size and weight, but also has some impact on the machine's performance. This paper introduces optimization design software for bevel gear, in which automatic optimization design is realized. In the paper mathematical model, programming of design data and realization of optimization design based on genetic algorithm are described in detail. The paper proposed integer  serial number encoding genetic algorithm, which effectively deals with continuous and discrete variable optimization problem and reduces the code length of the string to improve the encoding and decoding efficiency, no invalid solution or duplicate solutions

Advanced design optimization on straight bevel gears pair based on nature inspired algorithms

“…Gologlu and Zeyveli [8] demonstrated the capability of GA, for obtaining minimum volume of two stage helical gearbox. Zhang et al [9] conducted design optimization on a bevel gear, through a software based on GA. Tudose et al [10] optimized two stage coaxial helical gearbox by using GA. Tamboli et al [11] minimized volume of a helical gear pair. They obtained energy efficient helical gear pair using particle swarm optimization (PSO).…”

“…Ang et al [19] devised a 3-Dimensional parametric modelling method for a straight bevel gear. Zhang et al [9] developed design optimization software for a bevel gear for minimum volume. It was based on integer serial number encoding GA.…”

“…2. The researchers used either conventional or less efficient techniques, that end up in local minimum search, converge slowly, and inefficient and thus fruitless [9]. In many cases, they do not find global optimum and require too many resources to obtain the same.…”

“…Popular nature inspired algorithms are used in this research. They are much simpler and powerful enough to solve any design problems [9,[22][23][24]. They also give faster convergence and greater computational speed.…”

“…They also give faster convergence and greater computational speed. Many research [23,24] indicate nature inspired algorithms are superior and effective in dealing with optimization problems involving both continuous and discrete variables [9].…”