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Wear Measuring and Wear Modelling Based on Archard, ASTM, and Neural Network Models

Shebani, Amer and Pislaru, Crinela (2015) Wear Measuring and Wear Modelling Based on Archard, ASTM, and Neural Network Models. International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering, 9 (1). pp. 177-182.

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Abstract

The wear measuring and wear modelling are a fundamental issue in the industrial field, mainly correlated to the economy and safety. Therefore, there is a need to study the wear measurements and wear estimation. Pin-on-disc test is the most common test which is used to study the wear behaviour. In this paper, the pin-on-disc (AEROTECH UNIDEX 11) is used for the investigation of the effects of normal load and hardness of material on the wear under dry and sliding conditions. In the pin-on-disc rig, two specimens were used; one, a pin which is made of steel with a tip, is positioned perpendicular to the disc, where the disc is made of aluminum. The pin wear and disc wear were measured by using the following instruments: The Talysurf instrument, a digital microscope, and the alicona instrument; where the Talysurf profilometer was used to measure the pin/disc wear scar depth, a digital microscope was used to measure the diameter and width of wear scar, and the alicona was used to measure the pin wear and disc wear. After that, the Archard model, American Society for Testing and Materials model (ASTM), and neural network model were used for pin/disc wear modelling. Simulation results are implemented by using the Matlab program. This paper focuses on how the alicona can be considered as a powerful tool for wear measurements and how the neural network is an effective algorithm for wear estimation.

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Item Type: Article
Uncontrolled Keywords: Wear models, Archard Model, ASTM Model, Neural Networks; Pin-on-disc Test; Talysurf; Digital microscope; Alicona; wear scar; tribology; surface metrology; volume loss; wear prediction.
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Institute of Railway Research
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Depositing User: Crinela Pislaru
Date Deposited: 24 Mar 2015 15:00
Last Modified: 02 Dec 2016 10:53
URI: http://eprints.hud.ac.uk/id/eprint/23886

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