Evaluation of the tribological behavior of a brake disc-pad friction pair using a fuzzy inference model based on an adaptive network (ANFIS)

Authors

  • George Ipate Department of Biotechnical Systems Politehnica University of Bucharest, Bucharest, Romania
  • Andreea Catalina Cristescu Department of Biotechnical Systems Politehnica University of Bucharest, Bucharest, Romania
  • Constantin Daniel Cotici Department of Biotechnical Systems Politehnica University of Bucharest, Bucharest, Romania
  • Nelus Evelin Gheorghita Department of Biotechnical Systems Politehnica University of Bucharest, Bucharest, Romania
  • Andrei Florin Hristache Department of Biotechnical Systems Politehnica University of Bucharest, Bucharest, Romania
  • Daiana Alina Ionescu Department of Biotechnical Systems Politehnica University of Bucharest, Bucharest, Romania

DOI:

https://doi.org/10.47577/technium.v14i.9695

Keywords:

tribology, friction coefficient, adaptive neuro- fuzzy system, model

Abstract

The purpose of this research is to forecast the tribological behavior of the materials used in the field of braking systems using an Artificial Neural Network (ANN) based on the experimental data obtained by measuring the friction between the friction linings and the brake disc of a bicycle in the translational movement. The data analysis results from this research show that the estimates and forecasts with the proposed model (ANFIS) of the dynamic friction coefficient (COF) between the pads and the disc in translational motion using the ANN have been confirmed to be powerful and useful. The experimentally determined average value of the dynamic COF was 0.2003 with a standard deviation of 0.0233 in the range of values of 0.1244-0.3013.

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Published

2023-10-09

How to Cite

Ipate, G., Cristescu, A. C., Cotici, C. D., Gheorghita, N. E., Hristache, A. F., & Ionescu, D. A. (2023). Evaluation of the tribological behavior of a brake disc-pad friction pair using a fuzzy inference model based on an adaptive network (ANFIS). Technium: Romanian Journal of Applied Sciences and Technology, 14, 142–146. https://doi.org/10.47577/technium.v14i.9695