Using Artificial Neural Networks to Determine Wear of Composite Friction Material

Abstract

Sintered friction materials are widely used in friction units of automotive vehicles and special purpose vehicles. The main purpose is to transmit torque to the actuator. The development of the technology market requires the development and use of new units. At the same time, the creation of new materials is required, which also applies to sintered friction materials. This group of materials is characterized by a high service life, efficiency of torque transmission, as well as the ability to restore performance in case of violation of operating modes. One of the most significant parameters characterizing a sintered friction material is wear resistance. In most cases, it determines not only the resource of the unit itself, but the entire machine as a whole. A special place is occupied by brake units, which also use friction materials. The increased wear resistance of the friction material contributes to a decrease in the efficiency and service life of the brake system. Evaluation of the wear resistance of a friction material for the given operational parameters is a very long and costly process. The development of methodology and methods for accelerating the assessment of wear resistance is an important scientific and practical task. The paper presents the results of using artificial neural networks to predict the service life of a composite friction material based on copper on the sliding speed, pressure on the material and the amount of lubricant supplied to the friction zone. An artificial neural network has been trained using an array of experimental data for the FM-15 friction material. The training results have shown high accuracy, correctness of the proposed and implemented network architecture. The developed software has demonstrated its efficiency and the possibility of using it in calculations to determine the wear of a composite friction material.

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