Abstract
Main performance characteristics of any tribosystem depend on the correct selection, quality and properties of structural and lubricating materials, surface roughness (in particular, volumetric spatial configuration) and macrogeometric indicators. In the modern world of tough competition there is a need to reduce the time and costs of laboratory research and replace them with a computational experiment using the developed mathematical models. The aim of this research is to develop a multivariable regression model depending on the actual thickness of the lubricant from the factors identified in local contact. It will enable to predict the properties of structural and lubricating materials in tribosystems to improve performance in modern machines and mechanisms. The investigated model factors were chosen: the range of change is fast varying from 0 to 1.8 m/s; the volumetric oil temperature during the experiment varied from 293.1 K to 343.1 K; the contact load was 251.5 MPa. Initial parameter is thickness of the lubricating layer in contact determined by the method of optical interference. The experimental results were processed by the least squares method to obtain adequate mathematical model of the second order depending on the thickness of the lubricant from changing factors. The correlation coefficient is an average of 0.98. The relative error of the predicted values of the output variable does not exceed 5%. The resulting dependence to predict the properties of the lubricant from the technical parameters were specified in local contact.
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Dmytrychenko, N., Khrutba, V., Savchuk, A., Hlukhonets, A. (2020). Using Mathematical, Experimental and Statistical Modeling to Predict the Lubricant Layer Thickness in Tribosystems. In: Palagin, A., Anisimov, A., Morozov, A., Shkarlet, S. (eds) Mathematical Modeling and Simulation of Systems. MODS 2019. Advances in Intelligent Systems and Computing, vol 1019. Springer, Cham. https://doi.org/10.1007/978-3-030-25741-5_5
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