Chen, Xun and Limchimchol, Thitikorn (2006) Monitoring Grinding Wheel Redress-life Using Support Vector Machines. International Journal of Automation and Computing, 3 (1). pp. 56-62. ISSN 1476-8186Metadata only available from this repository.
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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