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Effective multi-sensor data fusion for chatter detection in milling process.

Minh-Quang Tran1, Meng-Kun Liu2, Mahmoud Elsisi3

  • 1Industry 4.0 Implementation Center, Center of Cyber-physical System Innovation, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan; Department of Mechanical Engineering, Thai Nguyen University of Technology, Thai Nguyen, Viet Nam.

ISA Transactions
|July 13, 2021
PubMed
Summary

This study presents a cost-effective multi-sensor data fusion method for milling chatter detection using sound and vibration. The optimized approach enhances accuracy for industrial applications.

Keywords:
Chatter detectionMachine learningMulti-sensor fusionTime–frequency analysisWavelet packet decomposition

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Manufacturing Technology

Background:

  • Chatter detection in milling is crucial for product quality and tool life.
  • Traditional methods like dynamometers are expensive and complex to install.
  • A need exists for low-cost, easily implementable chatter detection solutions.

Purpose of the Study:

  • To develop and validate a novel multi-sensor data fusion technique for milling chatter detection.
  • To compare the proposed method's performance against traditional schemes.
  • To optimize parameters for enhanced detection accuracy.

Main Methods:

  • Utilized microphone and accelerometer for multi-sensor data acquisition.
  • Applied wavelet packet decomposition for sound and vibration signal analysis.
  • Optimized wavelet parameters (mother wavelet, decomposition level) using kurtosis and crest factors.
  • Employed recursive feature elimination for feature selection.
  • Utilized machine learning for cutting stability classification.

Main Results:

  • The multi-sensor data fusion scheme effectively detects milling chatter under industrial conditions.
  • Optimized wavelet packet decomposition parameters significantly improved performance.
  • The proposed method demonstrated higher accuracy compared to traditional chatter detection schemes.
  • Recursive feature elimination identified key chatter features.

Conclusions:

  • The developed multi-sensor data fusion approach offers a practical and cost-effective solution for milling chatter detection.
  • Optimization of signal processing parameters is vital for robust chatter identification.
  • This method provides a reliable alternative for real-time monitoring in manufacturing environments.