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This study identifies optimal regression models for predicting cutting tool lifespan in milling. Two-dimensional neural networks and Support Vector Regression (SVR) offer the best accuracy for intelligent manufacturing applications.

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Area of Science:

  • Manufacturing Engineering
  • Data Science
  • Machine Learning

Background:

  • Accurate estimation of cutting tool lifespan is crucial for efficient milling operations.
  • Sensor data from milling processes require sophisticated models to capture dependencies.
  • Industry 4.0 initiatives emphasize intelligent manufacturing and process monitoring.

Purpose of the Study:

  • To identify the most effective regression model for estimating cutting tool lifespan in milling.
  • To evaluate the performance of Support Vector Regression (SVR), decision trees, and neural networks.
  • To contribute to intelligent manufacturing by improving production process monitoring and reducing losses.

Main Methods:

  • Utilized experimental data from a Haas VF-1 milling machine with vibration sensors and a Beckhoff PLC data collector.
  • Applied regression models, including Support Vector Regression (SVR), decision trees, and neural networks, to analyze continuous sensor data.
  • Evaluated model performance based on R2 parameters and error values.

Main Results:

  • Two-dimensional neural networks with the LBFGS solver achieved the highest prediction accuracy at 93.9%.
  • Support Vector Regression (SVR) demonstrated comparable performance with 93.4% accuracy.
  • Both neural networks and SVR provided the lowest error values among the tested models.

Conclusions:

  • Neural networks and SVR are highly effective for predicting cutting tool lifespan in milling processes.
  • The findings support the integration of advanced machine learning models in Industry 4.0 for enhanced manufacturing.
  • Accurate tool lifespan prediction enables better service planning and minimizes material and tool damage.