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Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on

Yi-Cheng Huang¹, Ching-Chen Hou¹

¹Department of Mechanical Engineering, National Chung Hsing University, Taichung 402, Taiwan.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

This study introduces a smart sensing method using AI for real-time monitoring of CNC machine tools. The developed feature engineering, principal component analysis, and Gaussian mixture model approach effectively diagnoses manufacturing parameter changes.

Keywords:

Gaussian mixture model feature engineering machine tool principal component analysis

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Area of Science:

Manufacturing Engineering
Artificial Intelligence
Machine Learning

Background:

The manufacturing industry is shifting towards smart machinery, integrating AI into Computer Numerically Controlled (CNC) machine tools for self-diagnosis and improved product quality.
Real-time monitoring of manufacturing parameters is essential for detecting deviations and ensuring process stability.

Purpose of the Study:

To develop and validate a novel method for real-time monitoring and diagnosis of manufacturing parameter changes in CNC machine tools.
To enable smart sensing capabilities for improved machining process control and quality assurance.

Main Methods:

Combined feature engineering and principal component analysis (FE-PCA) with an online Gaussian mixture model (GMM) utilizing Kullback-Leibler divergence (KLD) for unsupervised learning.
Utilized vibration signals from accelerometer devices and spindle current sensing for data acquisition.
Applied the FE-PCA-GMM/KLD method for real-time diagnosis of spindle speed changes during milling operations.

Main Results:

The developed unsupervised learning model successfully diagnosed spindle speed changes in a CNC machine tool.
Achieved high F1-scores for diagnosing changes across axes: 0.95 for X, 0.88 for Y, and 0.93 for Z.
Experimental verification confirmed the method's effectiveness in detecting manufacturing parameter variations.

Conclusions:

The established FE-PCA-GMM/KLD method provides a robust approach for real-time monitoring and early warning of manufacturing process parameter changes.
The developed smart sensing technology is suitable for fabricating devices to diagnose machining status, enhancing manufacturing intelligence.