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Impact Damage Localization in Composite Structures Using Data-Driven Machine Learning Methods.

Can Tang1, Yujie Zhou1, Guoqian Song2

  • 1College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China.

Materials (Basel, Switzerland)
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach for real-time impact source localization on plate structures, overcoming limitations of traditional methods. The data-driven technique accurately identifies impact locations without needing material property data.

Keywords:
arrival timeback propagation neural networkcomposite structuresdamage localizationmachine learning

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Area of Science:

  • Mechanical Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Traditional impact source localization methods struggle with real-time detection on plate structures due to material property uncertainties.
  • Accurate impact localization is crucial for assessing damage and ensuring the structural integrity of plate-like systems.

Purpose of the Study:

  • To propose and validate a data-driven machine learning method for real-time impact source localization on plate-like structures.
  • To demonstrate the method's effectiveness across structures with varying material properties.
  • To provide a robust technique for impact damage detection in composite materials.

Main Methods:

  • An experimental platform using piezoelectric transducers and an oscilloscope was developed for impulse response testing.
  • Impact source locations were generated by releasing a steel ball onto test plates.
  • Eigenvalues of arrival times from time-domain signals were extracted to create machine learning datasets.
  • A Back Propagation (BP) neural network was employed to analyze sensor arrival time differences and predict impact locations.

Main Results:

  • The proposed machine learning method successfully predicted impact source locations on plate-like structures.
  • The technique demonstrated high accuracy and robustness, independent of specific material properties.
  • Effectiveness was validated across three distinct plate-like structures with different material characteristics.

Conclusions:

  • The developed data-driven machine learning approach offers a reliable solution for real-time impact source localization in plate structures.
  • This method eliminates the need for prior knowledge of material properties, enhancing its applicability.
  • The research provides valuable experimental methodologies and testing techniques for impact damage assessment, particularly in composite structures.