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Information 2016, 7(1), 7; doi:10.3390/info7010007

An Approach to the Classification of Cutting Vibration on Machine Tools

1
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan
2
Department of Electrical and Electronic Engineering, Faculty of Information Technology, University of Transport Technology, Hanoi 100000, Vietnam
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 15 November 2015 / Revised: 30 January 2016 / Accepted: 3 February 2016 / Published: 15 February 2016
View Full-Text   |   Download PDF [1475 KB, uploaded 15 February 2016]   |  

Abstract

Predictions of cutting vibrations are necessary for improving the operational efficiency, product quality, and safety in the machining process, since the vibration is the main factor for resulting in machine faults. “Cutting vibration” may be caused by setting incorrect parameters before machining is commenced and may affect the precision of the machined work piece. This raises the need to have an effective model that can be used to predict cutting vibrations. In this study, an artificial neural network (ANN) model to forecast and classify the cutting vibration of the intelligent machine tool is presented. The factors that may cause cutting vibrations is firstly identified and a dataset for the research purpose is constructed. Then, the applicability of the model is illustrated. Based on the results in the comparative analysis, the artificial neural network approach performed better than the others. Because the vibration can be forecasted and classified, the product quality can be managed. This work may help new workers to avoid operating machine tools incorrectly, and hence can decrease manufacturing costs. It is expected that this study can enhance the performance of machine tools in metalworking sectors. View Full-Text
Keywords: vibration; artificial neural network; decision tree; support vector machine; naive Bayes classifier vibration; artificial neural network; decision tree; support vector machine; naive Bayes classifier
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, J.-F.; Lo, S.-K.; Do, Q.H. An Approach to the Classification of Cutting Vibration on Machine Tools. Information 2016, 7, 7.

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