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

• Published in: Journal of Manufacturing and Materials Processing 2018-09, Vol.2 (3), p.60
• Main Authors: Xing, Kanglin, Mayer, J.R.R., Achiche, Sofiane
• Format: Article
• Language: English
• Subjects: Accuracy; Classification; Clustering; Data acquisition; Data processing; Failure; Fault diagnosis; feature classification; Feature extraction; Five axis; K-means; Kinematics; Machine tools; Maintenance management; Manufacturing; Mechanical engineering; Methods; Monitoring systems; Neural networks; Pattern recognition; principal component analysis; Principal components analysis; Signal processing; volumetric errors
• ISSN: 2504-4494; 2504-4494
• DOI: 10.3390/jmmp2030060

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.