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J. Manuf. Mater. Process. 2018, 2(3), 60; https://doi.org/10.3390/jmmp2030060

Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

Department of Mechanical Engineering, Polytechnique Montréal, P.O. Box 6079, Station Downtown, Montréal, QC H3C 3A7, Canada
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Received: 30 July 2018 / Revised: 29 August 2018 / Accepted: 31 August 2018 / Published: 4 September 2018
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Abstract

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification. View Full-Text
Keywords: machine tools; volumetric errors; feature extraction; feature classification; principal component analysis; K-means machine tools; volumetric errors; feature extraction; feature classification; principal component analysis; K-means
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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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Xing, K.; Mayer, J.; Achiche, S. Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means. J. Manuf. Mater. Process. 2018, 2, 60.

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