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Digital Twin-Driven Tool Condition Monitoring for the Milling Process.

Sriraamshanjiev Natarajan1, Mohanraj Thangamuthu1, Sakthivel Gnanasekaran2

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This summary is machine-generated.

A novel Digital Twin (DT) approach enhances milling machine tool condition monitoring. This method uses machine learning algorithms to predict tool health with 91% accuracy, improving machining precision and reducing costs.

Keywords:
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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Systems Engineering

Background:

  • Accurate monitoring of cutting tool conditions is crucial for machining performance, workpiece accuracy, and cost reduction.
  • Existing methods struggle with the dynamic and unpredictable nature of tool wear, limiting real-time oversight.
  • Digital Twins (DT) offer a promising solution for advanced, real-time tool condition monitoring and prediction.

Purpose of the Study:

  • To propose and validate a Digital Twin (DT) based methodology for accurate real-time monitoring and forecasting of tool conditions in milling operations.
  • To develop a synchronized virtual model of the physical milling system for enhanced condition assessment.
  • To leverage machine learning for predicting tool health status based on sensory data.

Main Methods:

  • A Digital Twin framework was established, mirroring a physical milling machine.
  • Vibration and sound data were collected using National Instruments DAQ and microphone sensors.
  • Machine learning classification algorithms, including a Probabilistic Neural Network (PNN), were trained and evaluated using statistical features from vibration data.

Main Results:

  • The Probabilistic Neural Network (PNN) achieved a maximum prediction accuracy of 91% for tool condition monitoring.
  • The developed Digital Twin model, created using a data-driven approach in MATLAB-Simulink, demonstrated physical-virtual system balance.
  • The DT model successfully predicted various tool conditions based on the analyzed sensory data.

Conclusions:

  • The proposed Digital Twin approach, integrated with machine learning, provides a highly accurate method for real-time tool condition monitoring.
  • This DT-based system enhances machining accuracy and contributes to reduced operational costs.
  • The methodology validates the effectiveness of data-driven DT modeling for predictive maintenance in manufacturing.