In this study, a mathematical model of the honing process for helical gears with external teeth was developed. The proposed novel method of obtaining the two-parameter envelope of a family of surfaces proved useful in simulated machining by means of a tool with a profile deviation. Based on performed simulations and industrial applications, it was found that this method can be useful in predicting the accuracy of machined gear and therefore can be used in the design process of honing tool for high precision aerospace gears. For the example provided, the average absolute prediction error of machined tooth profile was 0.28 µm.

The paper presents mathematical models of cylindrical gear pairs with various types of tooth profiles, such as eccentric-cycloidal, concavo-convex Novikov type, and involute. Comparative analyses were provided for the aforementioned gear meshes, aimed at determining contact patterns, sliding...

This paper presents a method of pitting failure detection in toothed gears based on the reconstruction of the gear case vibrational signal. The effectiveness of the proposed method was tested in an experiment on a power circulation test stand. The autoencoder deep neural network architecture, semi-supervised training, and validation, along with the latent data convex hull-based clustering, are presented. The proposed method offers high efficiency (0.99 F1-measure) in gear state prediction (100% in failure detection, 98.9% in normal state prediction) and provides more capabilities in terms of generalization in comparison with linear machine learning techniques such as principal component analysis and nonlinear like the generative adversarial network. Moreover, it is distinguished by high sensitivity while also being able to detect even slight surface damage (initial pitting). These findings will be of particular relevance to a range of scientists and practitioners working with gear drives who are willing to implement machine learning in signal processing and diagnosis.