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Recognition of texture types of wear particles.

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Title: Recognition of texture types of wear particles.
Authors: Laghari, M. S.
Source: Neural Computing & Applications. 2003, Vol. 12 Issue 1, p18-25. 8p.
Subjects: Engineering databases, Information storage & retrieval systems, Artificial neural networks, Industrial productivity, Artificial intelligence, Industries
Abstract: Microscopic wear particles are produced in all machines containing moving parts in contact. The particles, transported by a lubricant from wear sites; carry important information relating to the condition of the machinery. This information is classified by compositional and six morphological attributes of particle size, shape, edge details, color, thickness ratio, and surface texture. This article describes an automated system for surface texture identification of wear particles by using artificial neural networks. The aim is to classify these particles according to their morphological attributes and by using the information obtained, to predict wear failure modes in engines and other machinery. This approach will enable the manufacturing industry to improve quality, productivity and economy. The procedure reported in this article is based on gray level co-occurrence matrices which are used to train a feed-forward neural network classifier in order to distinguish among seven different patterns which can aid in the identification of wear particles. The investigated patterns are: smooth, rough, striations, holes, pitted, cracked, and serrated. An accuracy classification rate of 94.64% has been achieved and is shown by a confusion matrix. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Microscopic wear particles are produced in all machines containing moving parts in contact. The particles, transported by a lubricant from wear sites; carry important information relating to the condition of the machinery. This information is classified by compositional and six morphological attributes of particle size, shape, edge details, color, thickness ratio, and surface texture. This article describes an automated system for surface texture identification of wear particles by using artificial neural networks. The aim is to classify these particles according to their morphological attributes and by using the information obtained, to predict wear failure modes in engines and other machinery. This approach will enable the manufacturing industry to improve quality, productivity and economy. The procedure reported in this article is based on gray level co-occurrence matrices which are used to train a feed-forward neural network classifier in order to distinguish among seven different patterns which can aid in the identification of wear particles. The investigated patterns are: smooth, rough, striations, holes, pitted, cracked, and serrated. An accuracy classification rate of 94.64% has been achieved and is shown by a confusion matrix. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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