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Improved Drill State Recognition during Milling Process Using Artificial Intelligence.

Jarosław Kurek1, Artur Krupa1, Izabella Antoniuk1

  • 1Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland.

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Summary

This study introduces an automated tool condition monitoring system that classifies drill wear into three states (green, yellow, red). The Extreme Gradient Boosting algorithm achieved 93.33% accuracy, optimizing manufacturing processes and reducing waste.

Keywords:
artificial intelligencedrill wear classificationtool state recognition

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Area of Science:

  • Manufacturing Engineering
  • Machine Learning
  • Condition Monitoring

Background:

  • Tool wear is critical in mass production, impacting product quality and costs.
  • Premature tool replacement increases downtime; continued use of worn tools leads to defects and financial losses.

Purpose of the Study:

  • To develop an automated method for classifying drill wear states.
  • To evaluate multiple algorithms for accuracy and misclassification errors in tool condition monitoring.

Main Methods:

  • Collected signal data for drill wear classification training.
  • Implemented and evaluated ten different classification algorithms.
  • Classified drill wear into three states: green, yellow, and red, representing decreasing quality.

Main Results:

  • Three algorithms achieved over 85% accuracy.
  • The Extreme Gradient Boosting algorithm demonstrated the highest accuracy at 93.33%.
  • The best-performing algorithm avoided critical green-red and red-green misclassifications, with only yellow-green errors.

Conclusions:

  • The developed automated tool condition monitoring system is effective.
  • The Extreme Gradient Boosting algorithm offers a robust solution for industrial tool wear classification.
  • This method can be applied to optimize tool management in manufacturing settings.