** Authors:**
MUSTAFA TİMUR, FATİH AYDIN

** 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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