A Hybrid of Back Propagation Neural Network and Genetic Algorithm for Modeling and Simulation of Standard Involute Spur Gear

Abstract

A method is presented for gear modeling and simulation based on a hybrid of back propagation (BP) neural network and genetic algorithm (GA). Generally, the method of mechanical design includes building up 3D models and gets them checked in corresponding software, which is complicated and takes much time. This proposed method offers a quick way for designers, which combines finite element analysis (FEA) with a hybrid of BP neural network and an intelligence global optimization algorithm, i.e. genetic algorithm (GA). ANSYS APDL is applied to build up the FEA database, which offers data for study and training of BP neural network. Then, the GA is applied to optimize the process parameters that would result in optimal solution of the BP neural network goals. The case study demonstrates that the proposed method can get accurate results in a short time.