Abstract
Condition monitoring is a very important aspect in automated manufacturing processes. Any malfunction of a machining process will deteriorate production quality and efficiency. This paper presents an application of support vector machines in grinding process monitoring. The paper starts with an overview of grinding behaviour. Grinding force is analysed through a Short Time Fourier Transform (STFT) to identify features for condition monitoring. The Support Vector Machine (SVM) methodology is introduced as a powerful tool for the classification of different wheel wear situations. After training with available signal data, the SVM is able to identify the state of a grinding process. The requirement and strategy for using SVM for grinding process monitoring is discussed, while the result of the example illustrates how effective SVMs can be in determining wheel redress-life.
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Xun Chen received his B. Eng. degress from Fuzhou University. He received his M. Sc. degree from Zhejiang University and his Ph. D. degree from Liverpool John Moores University. He has been a visiting professor to Fuzhou University since 2001.
Dr. Chen has published more than 100 research papers. He is a founder member of the International Committee of Abrasive Technology. Before his employment at Nottingham, Dr. Chen was a lecturer of Mechanical Engineering at the University of Dundee. Prior to that, he was a research fellow, a Royal Society Royal Fellow at Liverpool John Moores University and a lecturer at Fuzhou University.
His research interests include advanced manufacturing technology including application of computer science, mechatronics and artificial intelligence to manufacturing process monitoring and control, particularly to the high efficiency precision grinding.
Thitikorn Limchimchol received his B. Eng. (honour) degree in manufacturing engineering from University of Nottingham in United Kingdom in 2002. Currently he is undertaking his doctorial study on manufacturing engineering in the University of Nottingham. His main research interests include Grinding Technology, Artificial Intelligence, Support Vector Machines, Genetic Algorithm, and Java Application.
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Chen, X., Limchimchol, T. Monitoring grinding wheel redress-life using support vector machines. Int J Automat Comput 3, 56–62 (2006). https://doi.org/10.1007/s11633-006-0056-2
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DOI: https://doi.org/10.1007/s11633-006-0056-2