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Prediction of stress in fillet portion of spur gears using artificial neural networks

Published online by Cambridge University Press:  12 December 2007

M.S. Shunmugam  and
N. Siva Prasad
M.S. Shunmugam
Affiliation:
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India
N. Siva Prasad
Affiliation:
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, India
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Abstract

A fillet curve is provided at the root of the spur gear tooth, as stresses are high in this portion. The fillet curve may be a trochoid or an arc of suitable size as specified by designer. The fillet stress is influenced by the fillet geometry as well as the number of teeth, modules, and the pressure angle of the gear. Because the relationship is nonlinear and complex, an artificial neural network and a backpropagation algorithm are used in the present work to predict the fillet stresses. Training data are obtained from finite element simulations that are greatly reduced using Taguchi's design of experiments. Each simulation takes around 30 min. The 4-5-1 network and a sigmoid activation function are chosen. TRAINLM function is used for training the network with a learning rate parameter of 0.01 and a momentum constant of 0.8. The neural network is able to predict the fillet stresses in 0.03 s with reasonable accuracy for spur gears having 25–125 teeth, a 1–5 mm module, a 0.05–0.45 mm fillet radius, and a 15°–25° pressure angle.

Keywords

Artificial Neural NetworkBackpropagation AlgorithmFillet GeometryFinite Element MethodSpur GearTaguchi Method
Type
Practicum Paper
Information
AI EDAM , Volume 22 , Issue 1 , February 2008 , pp. 41 - 51
Copyright
Copyright © Cambridge University Press 2008

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