Anticipating the friction coefficient of friction materials used in automobiles by means of machine learning without using a test instrument


Abstract: The most important factor for designs in which friction materials are used is the coefficient of friction. The coefficient of friction has been determined taking such variants as velocity, temperature, and pressure into account, which arise from various factors in friction materials, and by analyzing the effects of these variants on friction materials. Many test instruments have been produced in order to determine the coefficient of friction. In this article, a study about the use of machine learning algorithms instead of test instruments in order to determine the coefficient of friction is presented. Isotonic regression was selected as the machine learning method in determining the coefficient of friction. The correlation coefficient between the results of isotonic regression algorithms and the results taken from the test instruments was measured as 0.9999 and the root mean squared error was 0.0014 in the experiments conducted. Selection of the number of optimum samples was enabled by taking bias--variance tradeoff into account, and this increased the performance of the classifier in use. The target of this study was to prevent the practice of time-consuming test activities by using machine learning methods instead of test instruments in determining the friction coefficient. This presents a solution for decreasing the factors of time and cost.

Keywords: Machine learning, isotonic regression, bias--variance tradeoff, friction

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