Browsing by Author "Hailemariam, Nigus (Mr.) Co-advisor"

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    Contact Fatigue Analysis of Helical Transmission Gear Using Finite Element Method with Material property Prediction by Artificial Neural Network Model
    (Addis Ababa University, 2020-02) Dejenie, Wletaw; Daniel, Tilahun (PhD); Hailemariam, Nigus (Mr.) Co-advisor
    Gear is the most essential element in power transmission system. Helical transmission gear can operate at high speed with large load carrying capacity. Due to this high contact stress is created at the mating surfaces. One of the main gear tooth failure type is contact fatigue failure due repetition of high contact stresses. In addition to design aspects, two important areas need to be addressed in order to enhance helical gear damage due to contact fatigue; improvements of material and enhancement in heat treatment. But it is very difficult to develop a complete theoretical/analytical model to improve the material property and heat treatment. In addition, to perform those enhancements, it needs an experimental work. In this study prediction of mechanical property of helical gear material using artificial neural network (ANN) and analyzing the contact fatigue of the predicted materials has been performed. After training the network, different performance measurements of the neural network accuracy was taken and prediction of the new concept (mechanical property) was performed. From five candidate materials, concept one was selected. By using the developed mechanical properties, contact fatigue was analyzed using AGMA standard and Finite Element Method. The results indicates, the fatigue life is infinite until the contact stress reach 959.7 Mpa. But beyond this contact stress, the fatigue life is limited and decreased. The comparison of contact stress by using AGMA and Ansys for the predicted material using ANN has shown and an error of 4.46 % and below was obtained. The material has best performance until the applied tangential load reaches 2000 N, because for applied tangential load of 2000 N, the factor of safety for AGMA as well as Ansys is greater than one. This indicates that, it is selective technique to predict the mechanical properties of materials using ANN model, when there is limited condition to use experimental investigation, because ANN simulates any correlations that are difficult to describe using physics based models.