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Machine tool volumetric error features extraction and classification using principal component analysis and K-means

Kanglin Xing, René Mayer and Sofiane Achiche

Article (2018)

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Cite this document: Xing, K., Mayer, R. & Achiche, S. (2018). Machine tool volumetric error features extraction and classification using principal component analysis and K-means. Journal of Manufacturing and Materials Processing, 2(3). doi:10.3390/jmmp2030060
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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.

Uncontrolled Keywords

machine tools; volumetric errors; feature extraction; feature classification; principal component analysis; K-means

Open Access document in PolyPublie
Subjects: 2100 Génie mécanique > 2100 Génie mécanique
2100 Génie mécanique > 2109 Instrumentation et systèmes mécaniques
Department: Département de génie mécanique
Research Center: Non applicable
Funders: CRSNG/NSERC, China Scholarship Council
Grant number: NETGP479639-15, 201608880003
Date Deposited: 09 Mar 2020 12:33
Last Modified: 10 Mar 2020 01:20
PolyPublie URL: https://publications.polymtl.ca/3577/
Document issued by the official publisher
Journal Title: Journal of Manufacturing and Materials Processing (vol. 2, no. 3)
Publisher: MDPI
Official URL: https://doi.org/10.3390/jmmp2030060

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