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Non-destructive Tests for Concrete Strength01:12

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The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
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A Study on Tool Breakage Detection Technology Based on Current Sensing and Non-Contact Signal Analysis.

Chia-Hung Lai1, Sih-Hao Huang1, Ting-En Wu2

  • 1Department of Intelligent Automation Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan.

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This study introduces a non-contact method for detecting tool breakage in CNC machining using spindle current signals. The system identifies spectral anomalies for reliable, early detection, improving productivity and reducing costs.

Keywords:
current sensingdeep learningnon-contact monitoringpredictive maintenancetool condition monitoring

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Area of Science:

  • Manufacturing Engineering
  • Mechanical Engineering
  • Signal Processing

Background:

  • Tool breakage in CNC machining significantly impacts productivity and incurs high maintenance expenses.
  • Existing detection methods often require complex hardware modifications or are unreliable.

Purpose of the Study:

  • To propose and validate a non-contact tool breakage detection method for CNC machines.
  • To utilize spindle current signals and frequency domain analysis for real-time monitoring.

Main Methods:

  • Employed an SCT013 current sensor for non-invasive capture of spindle motor current signals.
  • Applied Fast Fourier Transform (FFT) for spectral feature extraction, focusing on high-frequency anomalies.
  • Evaluated Artificial Neural Network (ANN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) for automated detection.

Main Results:

  • Consistent spectral anomalies were observed in spindle current signals during tool breakage events across 20 experiments.
  • The proposed system reliably detected tool breakage by identifying frequency domain anomalies within 1-3 seconds post-event.
  • Deep learning models showed varying inference times (15-58 s) but the core detection mechanism identified breakage characteristics early.

Conclusions:

  • The non-contact tool breakage detection method using spindle current signals is effective and reliable.
  • Early detection of tool breakage enables timely tool condition evaluation and proactive maintenance strategies.
  • This approach offers a cost-effective solution for enhancing CNC machining efficiency and reducing operational costs.