Article · Trudy Universiteta · March 30, 2024
Bevel Gear Contact Analysis Based on Machine Learning
Zhenis Otarbay, Gulsim TULEPOVA, Syndar SATBAYEV, Mira Bukayeva, Altynbek Seitenov
Abstract
In this paper, we study the analysis of the contact of bevel gear teeth based on machine learning methods. Bevel gear teeth are widely used in various industrial and mechanical systems to transmit rotational motion with high precision and reliability. Traditional methods of gear contact analysis are based on mathematical models and empirical formulas, which may be limited in accuracy and applicability. In recent years, machine learning has become a powerful tool in the field of engineering and mechanics to more accurately model and predict the contact behavior of bevel gear teeth. The paper proposes the use of machine learning methods, such as neural networks or deep learning algorithms, to train models capable of analyzing the contact of bevel gear teeth. Wear, load, and gear geometry data can be used to train models, which can then predict contact parameters and evaluate contact performance.
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