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

Kanglin Xing, René Mayer, Sofiane Achiche

Article (2018)

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Abstract

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting featuresfrom the complex VE data provides with a means to characterize this data. VE feature classificationcan reveal the machine tool accuracy states. This paper presents a study on how to use principalcomponent analysis (PCA) to extract the features of VE and how to use the K-means methodfor machine tool accuracy state classification. The proposed data processing methods have beentested 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 informationand 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 forVE features extraction and classification.

Uncontrolled Keywords

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

Subjects: 2100 Mechanical engineering > 2100 Mechanical engineering
2100 Mechanical engineering > 2109 Mechanical systems and instrumentation
Department: Department of Mechanical Engineering
Funders: CRSNG/NSERC, China Scholarship Council
Grant number: NETGP479639-15, 201608880003
PolyPublie URL: https://publications.polymtl.ca/3577/
Journal Title: Journal of Manufacturing and Materials Processing (vol. 2, no. 3)
Publisher: MDPI
DOI: 10.3390/jmmp2030060
Official URL: https://doi.org/10.3390/jmmp2030060
Date Deposited: 09 Mar 2020 12:33
Last Modified: 11 Nov 2022 13:35
Cite in APA 7: 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). https://doi.org/10.3390/jmmp2030060

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