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<p>This study presents a novel methodology that integrates piezoelectric actuation and sensing with regression models and neural networks for high-accuracy detection and localization of multiple delamination and crack defects in composite laminates. An eight-layer graphite/epoxy composite plate, instrumented with piezoelectric patches, is excited using random voltage stimuli, generating structural responses captured by sensors. The proposed framework employs six regression techniques and artificial neural networks, achieving localization accuracy with <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_19571_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> values exceeding 99.6%. Additionally, combining five signal decomposition methods with four classifiers enables defect type identification with up to 98.26% accuracy. The methodology demonstrates exceptional precision in detecting previously unseen delamination and crack defects down to the lamina level, optimized through grid search cross-validation. A comparative analysis highlights that piezoelectric sensor voltage signals outperform acceleration signals for defect characterization. This integrated piezoelectric-regression/neural network framework establishes a robust foundation for non-destructive evaluation, offering significant potential for real-time structural health monitoring applications.</p>

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High-precision multiple defect detection and localization in composite laminates using integrated piezoelectric sensing, regression and neural networks methods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月13日

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  • Majid Ghazali,
  • Morteza Karamooz Mahdiabadi

摘要

This study presents a novel methodology that integrates piezoelectric actuation and sensing with regression models and neural networks for high-accuracy detection and localization of multiple delamination and crack defects in composite laminates. An eight-layer graphite/epoxy composite plate, instrumented with piezoelectric patches, is excited using random voltage stimuli, generating structural responses captured by sensors. The proposed framework employs six regression techniques and artificial neural networks, achieving localization accuracy with \(R^2\) values exceeding 99.6%. Additionally, combining five signal decomposition methods with four classifiers enables defect type identification with up to 98.26% accuracy. The methodology demonstrates exceptional precision in detecting previously unseen delamination and crack defects down to the lamina level, optimized through grid search cross-validation. A comparative analysis highlights that piezoelectric sensor voltage signals outperform acceleration signals for defect characterization. This integrated piezoelectric-regression/neural network framework establishes a robust foundation for non-destructive evaluation, offering significant potential for real-time structural health monitoring applications.