Prediction of tribological properties of structural steels using artificial neural networks
Date
2019-03-20
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Видавництво Львівської політехніки
Lviv Politechnic Publishing House
Lviv Politechnic Publishing House
Abstract
The effect of quenching temperature on wear resistance of 40Kh steel after tempering
has been investigated. It was found that compared to standard heat treatment, quenching from 1050 °С
and high temperature tempering increase its tribological characteristics. The character of fracture of
the contacting surfaces was studied. It was shown that in the specimens quenched from 860 °С and
tempered, the fracture of the contact surface occurs by the mechanisms of smooth splitting and
delamination with plastic deformation. Increasing the quenching temperature to 1050 °С along with
high temperature tempering changes the character of the contact surface destruction. The areas with a
distinctive microstructure appear on the surface exhibiting substantially higher wear resistance during
friction as compared to the surrounding volume. The structural-geometrical parameters characterizing
the roughness and bearing capacity of the contact interaction surface were analyzed. It was found that
increasing the quenching temperature to 1050 °С allows to reduce the surface roughness and increase
the bearing capacity. Using the methods of optical and transmission electron microscopy, the
peculiarities of forming the microstructure of the investigated steel were studied, depending on the
temperature conditions of the thermal treatment. It was shown that raising the quenching temperature
to 1050 °С increases the austenitic grain size, enhances non-uniformity of carbon distribution, which
leads to the formation of large needle-shaped crystals of lath martensite with microtwin boundaries
inside. This, in turn, promotesthe formation at high tempering of non-uniformly distributed aggregates
of coarse carbides at these microtwin boundaries. The aggregates form areas of microstructure with
increased resistance to plastic deformation processes. That is, the morphology of the carbide phase is
one of the main factors that determine the tribological characteristics of steel, namely roughness,
structural-geometrical parameters and bearing capacity of the surface. The expediency of using
artificial neural networks for prediction of tribological properties of structural steels was shown.
According to the results of modeling the structural-geometrical parameters of the surface and the
roughness characteristics, the bearing capacity of the 40Kh steel surface during friction was predicted.
Description
Keywords
steel microstructure, carbide phase, wear resistance, surface bearing capacity, structural-geometrical parameters of the surface, neural network modeling
Citation
Uvarov V. Prediction of tribological properties of structural steels using artificial neural networks / Viktor Uvarov, Serhii Bespalov // Ukrainian Journal of Mechanical Engineering and Materials Science. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 5. — No 1. — P. 45–60.