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ScienceDirect - Tribology International : Automated classification of wear particles based on their surface texture and shape features .nojs { display: none; } 350? '350px':'auto'); max-height:60; height:expression(this.scrollHeight > 60? '60px':'auto');overflow:hidden;"> Athens/Institution Login Not Registered? User Name: Password: Remember me on this computer Forgotten password? Home Browse My Settings Alerts Help Quick Search Title, abstract, keywords Author Journal/book title Volume Issue Page Tribology International Volume 41, Issue 1, January 2008, Pages 34-43 Font Size: Abstract Abstract - selected Article Figures/Tables Figures/Tables - selected References References - selected Purchase PDF (869 K) E-mail Article Add to my Quick Links Cited By in Scopus (0) Related Articles in ScienceDirectSpeculations on the theory of adhesive wearWear Speculations on the theory of adhesive wearWear, Volume 21, Issue 1, August 1972, Pages 103-114Eugene F. FinkinAbstractA new sliding model applicable to adhesive wear is offered which includes the influence of asperity interaction distance. This model reduces to that of Archard in the limit as the asperity interaction distance reduces to the length of the junction. The new model allows wear to be related to three independent quantities: number of asperity interactions, total real area of interaction, and the volume of slid interaction. On physical grounds, the first of these is shown to be an implausible mechanism, and the remaining two are shown to be plausible. Potential explanations are offered for the influence of asperity interaction distance on the probability of wear particle formation.A consideration of the plastic real area of contact, instead of the total real area of contact, is suggested as a means to increase the Archard wear particle formation probabilities by two orders of magnitude.The new sliding model may have implications on a number of subjects in addition to wear, including friction, and surface temperature analysis. Purchase PDF (813 K) Characterization and classification of wear particles a...Wear Characterization and classification of wear particles and surfacesWear, Volume 249, Issues 3-4, May 2001, Pages 194-200G. W. Stachowiak, P. PodsiadloAbstractWear particles and surfaces are three-dimensional (3-D) objects and their numerical characterization and classification is still largely an unresolved problem. Usually a set of various parameters is employed to describe the surface topography. These parameters are of limited use, especially when dealing with anisotropic surfaces. To solve this problem a modified Hurst orientation transform (HOT) method has been developed and applied to characterize the surface anisotropy. However, despite the apparent success this method does not yet provide a full description of the surface topography. It is known that complex structures observed in nature can be described and modeled by a combination of simple mathematical rules. It is therefore reasonable to assume that, in principle, it should also be possible to describe any surface by a set of such rules. The problem is in finding those rules. For this purpose, a modified partitioned iterated function system (PIFS) was developed and applied to encode the 3-D surface topography information, i.e. to obtain full description of surface topography of wear particles and surfaces. Importantly, PIFS information gained from individual wear particles or surfaces allows to classify them in groups which are characteristic to a particular failure type. This, in turn, allows to ascribe an ‘unclassified’ particle or surface to a particular group/category which is characteristic to a specific failure type or wear mechanism. This forms the basis of a system, which when fully developed, would allow an automated recognition of particles and surface morphologies without the need for experts. The system then can be developed further to include diagnosis of the type of failure. In this paper an overview of recent advances and developments in the characterization, classification and recognition of wear particles and surfaces is presented. Purchase PDF (438 K) Wear behaviour and mechanical properties: The similarit...Wear Wear behaviour and mechanical properties: The similarity of seemingly unrelated approachesWear, Volume 21, Issue 1, August 1972, Pages 167-177G. Levy, R. G. Linford, L. A. MitchellAbstractParameters which have been derived empirically or theoretically by several investigators to predict wear rate, wear particle size and the conditions of real contact between surfaces are discussed. It is shown that greater similarities than suspected exist between these parameters, and that many can be reduced to the square of the ratio of Young's modulus to hardness. Purchase PDF (725 K) View More Related Articles View Record in Scopus doi:10.1016/j.triboint.2007.04.004 Copyright © 2007 Elsevier Ltd All rights reserved. Automated classification of wear particles based on their surface texture and shape features Gwidon P. Stachowiak, a, , Gwidon W. Stachowiaka and Pawel Podsiadloa aTribology Laboratory, School of Mechanical Engineering, University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia Received 22 November 2006; revised 15 April 2007; accepted 15 April 2007. Available online 4 June 2007. References and further reading may be available for this article. To view references and further reading you must purchase this article. AbstractIn this study, the automated classification system, developed previously by the authors, was used to classify wear particles. Three kinds of wear particles, fatigue, abrasive and adhesive, were classified. The fatigue wear particles were generated using an FZG back-to-back gear test rig. A pin-on-disk tribometer was used to generate the abrasive and adhesive wear particles. Scanning electron microscope (SEM) images of wear particles were acquired, forming a database for further analysis. The particle images were divided into three groups or classes, each class representing a different wear mechanism. Each particle class was first examined visually. Next, area, perimeter, convexity and elongation parameters were determined for each class using image analysis software and the parameters were statistically analysed. Each particle class was then assessed using the automated classification system, based on particle surface texture. The results of the automated particle classification were compared to both the visual assessment of particle morphology and the numerical parameter values. The results showed that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry. Keywords: Automated classification; Particle surface texture; Fatigue wear particles; Abrasive wear particles; Adhesive wear particles; Condition monitoring Article Outline1. Introduction2. Experimental methods2.1. Fatigue wear tests2.2. Abrasive and adhesive wear tests2.3. Wear particle collection2.4. Acquisition and processing of SEM images2.5. Size and shape parameters of particle images2.6. Generation of surface texture databases for particle classification2.7. Pattern recognition method3. Results3.1. Visual examination of SEM particle images3.2. Statistical analysis of particles area, perimeter and elongation parameters3.3. Texture-based classification of wear particles4. Discussion4.1. Comparison between wear particles4.2. Classification errors5. ConclusionsAcknowledgementsReferences Fig. 1. FZG back-to-back gear test rig. View Within ArticleFig. 2. Pin-on-disk tribometer. View Within ArticleFig. 3. Examples of typical surface textures of wear particle classes used in the analysis. View Within ArticleFig. 4. Images of wear particles from each wear mechanism: (a) fatigue wear, (b) abrasive wear, (c) adhesive wear. View Within Article Table 1. Summary of particle classes and wear test conditions View Within ArticleTable 2. Mean values (average from 10 particles) of particle area, perimeter, convexity and elongation for the three classes of wear particles View Within ArticleTable 3. Classification results obtained for wear particle surfaces View Within Article Tribology International Volume 41, Issue 1, January 2008, Pages 34-43 Home Browse My Settings Alerts Help About ScienceDirect | Contact Us | Terms & Conditions | Privacy Policy Copyright © 2008 Elsevier B.V. All rights reserved. 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