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Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process

Jianyong Chen1, Jiayao Lin2, Ming Zhang3

  • 1College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary

This study predicts surface roughness in complex workpiece turning using Gaussian Process Regression (GPR) and vibration signals. The developed model accurately forecasts surface roughness, enhancing manufacturing quality and efficiency.

Keywords:
Daubechies Wavelet Packet TransformGaussian Process Regressioncomplex-structured workpiecessurface roughnessvibration signal analysis

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Area of Science:

  • Manufacturing Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Surface roughness is critical for product quality and process optimization in manufacturing.
  • Predicting surface roughness in complex-structured workpiece turning remains a challenge.
  • Vibration signals offer valuable insights into machining processes.

Purpose of the Study:

  • To develop a predictive model for surface roughness in the turning of complex-structured workpieces.
  • To utilize Gaussian Process Regression (GPR) informed by vibration signals for accurate prediction.
  • To enhance manufacturing quality and process optimization through reliable surface roughness forecasting.

Main Methods:

  • Feature extraction from vibration signals in both time and frequency domains (mean, median, STD, RMS).
  • Signal processing using Welch's method for frequency domain analysis.
  • Time-frequency domain analysis employing Daubechies Wavelet Packet Transform (WPT) at three levels.
  • Gaussian Process Regression (GPR) model implementation for surface roughness prediction.

Main Results:

  • The GPR model accurately predicts surface roughness for complex-structured workpieces.
  • Vibration signal features effectively inform the predictive model.
  • The integration of time and frequency domain analysis improves prediction accuracy.

Conclusions:

  • The developed GPR model provides a robust solution for surface roughness prediction in turning operations.
  • This predictive strategy can significantly improve product quality and streamline manufacturing processes.
  • The methodology offers potential for waste reduction and enhanced industrial efficiency.