Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 21, Issue 4, Pages 1799-1814Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2006.07.016
Keywords
tool wear; support vector machine; process monitoring
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The development of tool wear monitoring system for machining processes has been well recognised in industry due to the ever-increased demand for product quality and productivity improvement. This paper presents a new tool wear predictive model by combination of least squares support vector machines (LS-SVM) and principal component analysis (PCA) technique. The corresponding tool wear monitoring system is developed based on the platform of PXI and LabVIEW PCA is firstly proposed to extract features from multiple sensory signals acquired from machining processes. Then, LS-SVM-based tool wear prediction model is constructed by learning correlation between extracted features and actual tool wear. The effectiveness of proposed predictive model and corresponding tool wear monitoring system is demonstrated by experimental results from broaching trials. (c) 2006 Elsevier Ltd. All rights reserved.
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