A Simple, Fast and Low-Cost Method for in situ Monitoring of Topographical Changes and Wear Rate of a Complex Tribo-System Under Mixed Lubrication

This contribution presents a simple, fast, and low-cost method to track in situ and in real time the evolution of the surface topography. The method combines an optical method (collimated light) with image analysis. The method was validated using a complex tribo-system; a reciprocal sliding of a rough cast iron counter-piece under mixed lubrication. We demonstrated that the optical method is well suited to observe the evolution of the contact areas during sliding. We also proved that the contact area occurs on the highest peaks of the surface roughness. Finally, we estimated the wear rate by combining the information of the contact area and the Abbott-Firestone curve obtained by profilometry. The wear rate was found to be 40 times higher in the early stage of sliding as compared to the steady-state. The running-in of this particular system was found to be approximately 10 h. In addition to be simple, fast and low-cost, the proposed method has other advantages. It is adaptable for real industrial tests conditions. The fast data analysis allows the wear rate of a tribo-system being determined in real-time. This method characterise the real contact areas of the observed piece, which is difficult, if not impossible, to obtain by profilometric observation alone.

Characterization of Grey Cast Iron-Steel Tribo-system under Starved Lubrication Conditions

Fatemeh Saeidi

Reducing friction and wear is essential for building efficient systems with low energy consumption and a long lifetime. Surface texturing is one of the methods to reduce friction and wear, especially in oil-lubricated systems. However, there is still a lack of knowledge about the fundamental aspects involved in the improvement of tribological performance, especially for the mechanical parts sliding under starved lubrication conditions. Moreover, failure in such lubricated contacts is poorly understood and there are little agreements regarding the mechanisms leading to scuffing. The present research focuses on three main objectives. The first is to characterize a cast iron-steel tribo-system under starved lubrication conditions, and to improve its tribological performance by laser surface texturing. All tribo-tests are performed using a flat-on-flat set-up with reciprocal movements. This set-up simulates specific industrial operating conditions of a semi-journal bearing used in a cutting machine. A Design of Experiments (DoE) approach is selected to investigate the effect of the different geometrical micro-texture parameters on the coefficient of friction (COF) and the lifetime of the cast iron samples. An optimum micro-texture geometry leading to a low COF and a long lifetime is determined based on a fractional factorial design. The second goal is to investigate the failure mechanisms of the selected tribo-pair. Failure of the tribo-system is characterized for both textured and un-textured surfaces. Scuffing is found to be the failure mechanism for the un-textured cast iron samples. For the textured cast iron samples, in contrast, two different failure mechanisms are observed depending on the distance between the micro-textures in direction of sliding (DMS). The textured surfaces with a DMS > 3.5 mm fail via the scuffing mechanism, whereas the surfaces with a DMS < 3 mm fail by a different mechanism named the oxidation mechanism. In contrary to the scuffing mechanism, the oxidation failure is found to be more a gradual failure. Based on the experimental observations, two hypotheses are suggested for the scuffing mechanism, and one for the oxidation mechanism. Finally, predicting the lifetime of a tribo-system is of the utmost importance to save costs. The DoE model developed for the lifetime was found to be a weak predictive model due to the sudden nature of scuffing. The accuracy of this model could only be improved by performing large statistics, which is not time and cost effective. Consequently, the last objective of this work is to employ an acoustic emission (AE) technique to detect precisely the surface state of our tribo-system. The application of wavelet packet decomposition, as an advanced signal processing method used for AE signals, is found to be promising for early detection of scuffing. The extension of this work using random forest regression shows the possibility of predicting scuffing 5 min before it occurs.

EPFL2016

Characterization of Grey Cast Iron-Steel Tribo-system under Starved Lubrication Conditions

Fatemeh Saeidi

EPFL2016