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Energy efficiency identification and surface roughness prediction using cutting force signal for computer numerical

Chunhua Feng1, Meng Li2, Haohao Guo2

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This study introduces a novel method for real-time energy efficiency identification in machining using cutting force signals. The approach accurately predicts surface roughness, aiding sustainable manufacturing.

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Signal Processing

Background:

  • Real-time energy efficiency tracking in machining is challenging due to multiple influencing factors.
  • Traditional power signals are insufficient for comprehensive energy efficiency assessment.
  • Accurate monitoring is crucial for energy-efficient manufacturing and improved productivity.

Purpose of the Study:

  • To develop a robust method for energy efficiency recognition in machining processes.
  • To establish a reliable surface roughness prediction model.
  • To investigate the influence of cutting parameters on energy efficiency and surface quality.

Main Methods:

  • Utilized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose cutting force signals into intrinsic mode functions (IMFs).
  • Employed PCA-Fast Independent Component Analysis (ICA) for energy efficiency characterization based on IMF proportions.
  • Developed a Support Vector Regression (SVR) model for surface roughness prediction using specific cutting energy consumption (SCEC).
  • Conducted orthogonal tests varying spindle speed, feed rate, depth, and width of cutting.

Main Results:

  • Successfully classified energy efficiency into high, medium, and low levels.
  • Extracted distinct time-frequency features of cutting force signals corresponding to different energy efficiency levels.
  • Achieved an average absolute error of 0.058 in surface roughness prediction.
  • Demonstrated the significant influence of cutting parameters on cutting force, SCEC, and surface roughness.

Conclusions:

  • The proposed method effectively identifies machining energy efficiency using cutting force signals.
  • The developed model accurately predicts surface roughness, meeting industry requirements.
  • This approach supports sustainable manufacturing through enhanced energy monitoring and quality prediction.