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Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of

Yi-Wen Huang1, Syh-Shiuh Yeh2

  • 1Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
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Summary

This study developed an online classification system for lathe machining insert conditions using vibration signals. The system accurately identifies four common insert conditions, enhancing manufacturing quality and efficiency.

Keywords:
CNC lathesaccelerometerinsert conditionspower spectral density

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Area of Science:

  • Mechanical Engineering
  • Manufacturing Technology
  • Signal Processing

Background:

  • Lathe machining insert conditions critically impact product quality and manufacturing efficiency.
  • Effective online monitoring of insert conditions is essential for optimizing machining processes.

Purpose of the Study:

  • To design and validate an online classification system for identifying four common lathe machining insert conditions (built-up edge, flank wear, normal, fracture).
  • To improve manufacturing quality and efficiency in computer numerical control (CNC) lathes through real-time insert condition assessment.

Main Methods:

  • Utilized power spectral density distribution of accelerometer vibration signals to capture magnitude features of segmented frequencies.
  • Developed insert condition models using Principal Component Analysis (PCA) and backpropagation neural networks (BPNN).
  • Implemented a machining model fusion stage with a BPNN to establish weight functions and classify insert conditions based on calculated weights.

Main Results:

  • The designed insert condition classification system demonstrated feasibility in online identification and classification under various machining conditions.
  • Achieved a classification rate exceeding 80% for the four common insert conditions.
  • Validated through cutting tests on a computer numerical control (CNC) lathe.

Conclusions:

  • The developed system effectively classifies lathe machining insert conditions online, contributing to improved manufacturing quality and efficiency.
  • The approach using vibration signal analysis and neural networks provides a robust solution for real-time monitoring in CNC machining.
  • This technology enables proactive adjustments, preventing potential defects and optimizing tool life.