Abstract
In the present study two highly alloyed steel grades, intended for cold working applications, were subjected to plane-contact, nonlubricating sliding friction testing in order to estimate the working regions (applied pressure and sliding velocity) that are governed by mild, moderate and severe wear mechanisms, before and after nitrocarburizing. For this purpose, both tool steel grades were initially pre-subjected to the proper heat treatments, in order to achieve a final bulk hardness of 40, 50 and 60 HRC whereas, after heat treatment, half of the material specimens were further subjected to nitrocarburizing surface treatment. The comparative experimental study of heat-treated and heat-/ surface-treated grades within a wide range of applied pressure and sliding velocity values, in combination to the development of a relevant Artificial Neural Network, allowed to determine the areas of recommended operation of such tribosystems. In all cases, the positive influence of the post-hardening surface treatment was proved; further work is in progress to generalize these preliminary results and establish the relevant wear maps correlating the wear lifetime, the hardening levels and the surface treatment to the operational plane sliding conditions.
Highlights
Galling is one of the most crucial problems that appear when metallic tribosystems, two metallic surfaces in relevant motion to each other, - operate under severe working conditions
The friction coefficient of the heattreated-only material varied from 0.24 up to 0.30, tending practically to a mean value of 0.27. These fluctuations can be attributed to the presence of large-sized chromium carbides (Fig. 1a) that interfere in the sliding mechanism
This section summarizes the basic concepts of artificial neural networks (ANNs) as well as the architecture of the optimum Artificial Neural Network (ANN) model developed for the wear characterization of the two steel grades under investigation
Summary
Galling is one of the most crucial problems that appear when metallic tribosystems, two metallic surfaces in relevant motion to each other, - operate under severe working conditions. An adverse combination of loads, sliding velocities and environmental factors can often induce severe failure of the surfaces in contact, via tearing, ploughing and significant plastic deformation, while massive material transfer between the two surfaces can be simultaneously observed [1, 2, 3, 4]. This disastrous adhesive wear mechanism could result in seizure - virtual welding of the two surfaces - eventually rendering the relevant motion of the two metallic bodies no longer possible. The experimental findings on the friction coefficient evolution and the post-testing evaluation of the wear mechanisms were used to develop a reliable Artificial Neural Network (ANN), dedicated to predict the tribological performance of these materials
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