Differential Evolution Optimization Technique to Design Gear Train System

Abstract

A high performance mechanical power transmission system needs least weight, minimum centre to centre distance and higher strength to maintain its performance. In the present paper the gear problem is solved by minimizing volume, centre to centre distance and maximizing gear strength of gear trains since they are crucial parameters of the gear design problem. Conventional optimisation techniques cannot be used to optimise multi- objective function with constraints easily. The expectation from a desired optimisation are it should find a true global minimum, convergence should be fast, have a minimum number of control parameters, simple and efficient to utilise. Differential evolution optimisation, a simple and effective technique for global optimisation over incessant space, doesn’t need the function have to be continuous or differential as usually required by classical optimization. Some system parameters represented as vector are chosen, are decision variables. a multi objective function taking into consideration of module, width factor, number of teeth like its parametric vector or decision variable. DE is a population based optimisation technique, tries to improve a candidate solution iteratively, accepts a solution vector and uses the formula in order to derive a new candidate solution from the existing candidates and find out the best function value from the existing functions by comparing. Penalty function is incorporated in order to handle constraints.