Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

Elgargni, M, Al-Habaibeh, A ORCID: 0000-0002-9867-6011 and Lotfi, A ORCID: 0000-0002-5139-6565, 2015. Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks. The International Journal of Advanced Manufacturing Technology, 77 (9), pp. 1965-1978. ISSN 0268-3768

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Abstract

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using
infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms.

Item Type: Journal article
Publication Title: The International Journal of Advanced Manufacturing Technology
Creators: Elgargni, M., Al-Habaibeh, A. and Lotfi, A.
Publisher: Springer
Place of Publication: London
Date: 2015
Volume: 77
Number: 9
ISSN: 0268-3768
Identifiers:
NumberType
10.1007/s00170-014-6576-yDOI
Divisions: Schools > School of Architecture, Design and the Built Environment
Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 10:35
Last Modified: 09 Jun 2017 13:34
URI: http://irep.ntu.ac.uk/id/eprint/15281

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