Anticipating the friction coe cient of friction materials used in automobiles by means of machine learning without using a test instrument
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Tarih
2013
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The most important factor for designs in which friction materials are used is the coe cient of friction. The coe cient 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 e ects of these variants on friction materials. Many test instruments have been produced in order to determine the coe cient of friction. In this article, a study about the use of machine learning algorithms instead of test instruments in order to determine the coe cient of friction is presented. Isotonic regression was selected as the machine learning method in determining the coe cient of friction. The correlation coe cient 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 tradeo into account, and this increased the performance of the classi er 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 coe cient. This presents a solution for decreasing the factors of time and cost.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yazılım Mühendisliği, Mühendislik, Makine, Malzeme Bilimleri, Özellik ve Test
Kaynak
Turkish Journal of Electrical Engineering and Computer Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
21
Sayı
5
