Search:
Computing and Library Services - delivering an inspiring information environment

Fuzzy pattern recognition of AE signals for grinding burn

Liu, Qiang, Chen, Xun and Gindy, Nabil (2005) Fuzzy pattern recognition of AE signals for grinding burn. International Journal of Machine Tools and Manufacture, 45 (7-8). pp. 811-818. ISSN 08906955

[img] PDF
Restricted to Repository staff only

Download (401kB)

Abstract

Grinding burn is a common phenomenon of thermal damage that has been one of the main constraints in grinding difficult-to-machine materials. Grinding burn damages materials and degrades properties, by causing tensile residual stresses or microfractures in the workpiece surface. Numerous methods have been proposed to identify grinding burn. However, the main problems of current methods are their sensitivity and robustness. This paper describes a new method of grinding burn identification with highly sensitive acoustic emission (AE) techniques. The wavelet packet transform is used to extract features from AE signals and fuzzy pattern recognition is employed for optimising features and identifying the grinding status. Experimental results show that the accuracy of grinding burn recognition is satisfactory.

Item Type: Article
Additional Information: UoA 25 (General Engineering)
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TS Manufactures
T Technology > TA Engineering (General). Civil engineering (General)
Schools: School of Computing and Engineering
School of Computing and Engineering > Centre for Precision Technologies
School of Computing and Engineering > Centre for Precision Technologies > Advanced Machining Technology Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > High-Performance Intelligent Computing
Related URLs:
Depositing User: Graham Stone
Date Deposited: 20 Oct 2008 12:06
Last Modified: 21 Aug 2015 03:43
URI: http://eprints.hud.ac.uk/id/eprint/2298

Downloads

Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©