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

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

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 an...

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Bibliographic Details
Main Authors: Kanglin Xing, J.R.R. Mayer, Sofiane Achiche
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Journal of Manufacturing and Materials Processing
Subjects:
machine tools
volumetric errors
feature extraction
feature classification
principal component analysis
K-means
Online Access:http://www.mdpi.com/2504-4494/2/3/60

Internet

http://www.mdpi.com/2504-4494/2/3/60

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