Design Optimization of Spur and Helical Gear Pairs

Abstract

Gears are the most common of machine elements and due to that many studies have been conducted on optimum gear design. Gear optimization can be divided into two categories, namely, single gear pair or Gear train optimization. The problem of gear pairs design optimization is difficult to solve because it involves multiple objectives and large number of variables. Hence a trustworthy and resilient optimization technique will be more useful in obtaining an optimal solution for the problems. In the proposed work an effort has been made to optimize spur and helical gear pair design using LINGO and Meta heuristics algorithms like Real Coded Genetic Algorithm (RCGA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).On applying the combined objective function factors like Power, Efficiency is maximized and the overall Weight, Centre distance has been minimized in the model. The performance of the proposed algorithms is validated through test problems and the comparative results are reported.