En Route to the Automated Wear Surface Classification System: Differentiating Between Adhesive, Abrasive, and Corrosive
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ORIGINAL PAPER
En Route to the Automated Wear Surface Classification System: Differentiating Between Adhesive, Abrasive, and Corrosive Wear Under Different Load Conditions Marcin Wolski1 · Tomasz Woloszynski1 · Pawel Podsiadlo1 · Gwidon W. Stachowiak1 Received: 27 May 2020 / Accepted: 1 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract From the industrial view point, an automated classification system of worn surfaces is highly desirable for the monitoring and prediction of the operational health status of machines and their components. Optical microscopy images of abrasive and adhesive wear surfaces were obtained and analyzed using recently developed directional blanket covering (DBC) and DBC curvature (DBCC) methods. As these methods have the unique ability of to measure the surface roughness and curvature complexity at individual scales and directions, minute differences have been detected. In the present study, both DBC and DBCC methods were evaluated in differentiating between surfaces generated under abrasive, adhesive, and corrosive wear modes under different operating conditions, i.e., exhibiting different wear severity. The wear surfaces were imaged using an optical microscope and a confocal surface profilometer. Results obtained showed that the methods can detect minute differences between the wear modes and different wear severity, regardless of the imaging technique used. This is an important step in the development of machine diagnostic and prognostic systems/tools based on the images of worn surfaces. Keywords Surface characterization · Abrasive wear surfaces · Adhesive wear surfaces · Corrosive wear surfaces · Condition monitoring · Automated diagnostics
1 Introduction Large-scale introduction of machinery in nineteenth century, accompanied by massive reduction in labor and increased profits, was the engine of our rapid progress. Inevitable side effects were increased pollution and wear. As profits depended largely on the machine performance wear could not be neglected, hence its measurements and studies became a necessity in materials ranking and selection for machine parts. Over last few decades, wear testing machines (tribometers) have been gradually standardized so it is now possible to compare the experimental data from different research groups across the world and wear testing can now be performed by trained technicians. Practical issue, which still remains to be addressed, is an automated classification * Marcin Wolski [email protected] 1
Tribology Laboratory, School of Civil and Mechanical Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
of wear surfaces. A wear surface classification system that would tell the machine operator not only what wear mechanism is taking place but also how severe it is highly desirable. Such a system would find the applications throughout the all industries, i.e., manufacturing, mining, transportation (e.g., aviation, marine, automotive), etc. Building such a system would involve f
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