Chain Gear Design Using Artificial Neural Networks

In this study, the analytical calculation and analysis for the data corresponding to these gears have been made to obtain alternative test and training data sets to be used at the artificial neural networks (ANNs) with the constraints and requirements for the design of chain gears of power and motion transmission mechanisms are determined. In the input layer, the constraints and requirement values (input power, number of revolution of the pinion, number of revolution of the gear and center distance) of chain gears are used while at the output layer chain code (i.e., the chain gear type and chain sequence number), specifying the functional and physical properties, is used. Then the network is tested with the test data. The analytical calculation results and ANN predictions are compared by using statistical error analyzing methods such as absolute fraction of variance (R-2), root mean square error (RMSE), and mean error percentage (MEP) for the training and test data. The chain code has been determined by the ANN with an acceptable accuracy. It is concluded that ANNs can be used as an alternative method in chain gear design. (C) 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 38-44, 2012; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.20371