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Machine learning-enabled resolution-lossless tomography for composite structures with a restricted sensing

Jianwei Yang1, Yiyin Su2, Yi He1

  • 1Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region.

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|July 13, 2022
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Summary

This study introduces a machine learning (ML) approach using convolutional neural networks (CNNs) to improve ultrasound tomography for composite structural health monitoring. The method enhances imaging accuracy despite limited sensor data, reducing false alarms.

Keywords:
Algebraic Reconstruction TechniqueCarbon Fibre-reinforced PolymerConvolutional Neural NetworkImplanted Sensor NetworkMachine LearningUltrasound Tomography

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Area of Science:

  • Materials Science
  • Non-destructive Testing
  • Machine Learning Applications

Background:

  • Precise ultrasound tomography requires dense sensor networks, often impractical for in-situ monitoring.
  • Machine learning (ML), particularly CNNs, excels at complex data modeling and image analysis.
  • Existing methods struggle with limited sensor data in structural health monitoring.

Purpose of the Study:

  • To develop an ML-based ultrasound tomography approach for composite structural health monitoring with restricted sensing capabilities.
  • To enhance the accuracy and reliability of in-situ tomographic imaging using limited sensor data.
  • To minimize false alarms and image artifacts in defect detection.

Main Methods:

  • Utilized a CNN with an encoder-decoder architecture to process blurry Algebraic Reconstruction Technique (ART) images.
  • Employed convolution and max-pooling for feature extraction and max-unpooling with transposed convolution to enhance image resolution.
  • Validated the approach on a carbon fibre-reinforced polymer laminate with a purposefully restrained piezoresistive sensor network.

Main Results:

  • The ML-based approach accurately imaged artificial anomalies and delamination in the composite laminate.
  • Demonstrated effective tomographic image construction despite inadequate training data from the restricted sensor network.
  • Significantly minimized false alarms by effectively eliminating image artifacts.

Conclusions:

  • The developed ML-facilitated ART approach enables accurate in-situ ultrasound tomography for structural health monitoring of composites with limited sensors.
  • This method overcomes the limitations of sparse sensor networks in achieving precise defect detection.
  • The technique offers a promising solution for reliable composite structural integrity assessment, reducing false positives.