Deep Contact Strength of Surface Hardened Gears

Abstract

This article is devoted to the analysis methods for assessing the load capacity of gears hardened by surface chemical-thermal treatment (CTT), which are characterized by structural and chemical heterogeneity. The leading type of failure is determined by several factors, the main of which are the surface and deep layer properties of the material, which fundamentally differ in the energy and structural state. Intercrystalline fracture mechanisms predominate in the surface layers and transcrystalline in the core. For these cases, the classical failure criteria of Mohr, Tresca, and Mises lead to significant errors. Therefore, the bearing capacity of the layer component is investigated by the generalized criterion of the limit state of the Pisarenko-Lebedev structurally inhomogeneous material, considering changes in its plastic properties due to surface hardening. The reliability of predicting the level of bearing capacity of surface hardened steel parts, such as gears, was significantly improved. The influence of the plasticity parameter on the level of bearing capacity for various types of CTT is estimated. Calculations using the presented model show that for alloy steels with a Ni content up to 1%, the safety coefficient can be limited to 1.2, which will increase the bearing capacity by 25–27%.

Comparison of the efficiency of neural network algorithms in recognition and classification problems

“…Another example of the application of neural network algorithms is the prediction of deep failure due to fatigue damage in highly loaded gears. It was shown in [16] that the appearance of fatigue cracks occurs under the surface and can lead to a breakdown of the mechanism. The construction of effective neural networks will allow detecting deep damage at an early stage.…”