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<p>Industry 4.0 takes precision machining into account in manufacturing processes. Deep Learning approaches are transforming Cutting Tool Condition Monitoring (CTCM) into error detection and machining precision performance. A Convolutional Neural Network (CNN) based solution has been presented for Tool Condition Monitoring (TCM), efficiently handling different operating conditions and noisy environment. The CNN was trained on real-time vibration data to classify multiple tool fault categories through multi-class classification. The CNN architecture was designed to classify cutting tool conditions based on hyperparameter tuning of machining vibrations, achieving 98% accuracy compared to other classifiers, highlighting the robustness and efficiency of CNN for fault detection in machining operations, therefore it is suggested for condition monitoring of a cutting tool.</p>

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In CNC: Cutting Tool Health Monitoring Using Convolutional Neural Networks

  • 期刊论文

  • 发表日期：

    2025年5月6日

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  • Sunil M. Pondkule,
  • Sachin M. Bhosle

摘要

Industry 4.0 takes precision machining into account in manufacturing processes. Deep Learning approaches are transforming Cutting Tool Condition Monitoring (CTCM) into error detection and machining precision performance. A Convolutional Neural Network (CNN) based solution has been presented for Tool Condition Monitoring (TCM), efficiently handling different operating conditions and noisy environment. The CNN was trained on real-time vibration data to classify multiple tool fault categories through multi-class classification. The CNN architecture was designed to classify cutting tool conditions based on hyperparameter tuning of machining vibrations, achieving 98% accuracy compared to other classifiers, highlighting the robustness and efficiency of CNN for fault detection in machining operations, therefore it is suggested for condition monitoring of a cutting tool.