Hybrid physics-based and data-driven impact localisation for composite laminates

The current challenges facing data-driven impact localisation methods primarily involve accurately localising impacts occurring outside the training impact coverage area, particularly for anisotropic composite structures. This study addresses these challenges by integrating the physical dispersion r...

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Published inInternational journal of mechanical sciences Vol. 274; p. 109222
Main Authors Xiao, Dong, Sharif-Khodaei, Zahra, Aliabadi, M.H.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 15.07.2024
Subjects
Online AccessGet full text
ISSN0020-7403
1879-2162
DOI10.1016/j.ijmecsci.2024.109222

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Abstract The current challenges facing data-driven impact localisation methods primarily involve accurately localising impacts occurring outside the training impact coverage area, particularly for anisotropic composite structures. This study addresses these challenges by integrating the physical dispersion relations of impact-induced waves into the localisation process, thereby enhancing overall accuracy and reducing reliance on extensive training impact data. The dominant impact-induced flexural waves, derived from first-order shear deformation theory and classical laminate theory, exhibit significant dispersion. The phase velocity of these waves is governed by a dispersion equation dependent on structural stiffness, wave frequency, and direction. Solving this dispersion equation for a given wave frequency and known structural stiffness yields a wave velocity profile (WVP). Unlike conventional data-driven methods, which approximate the WVP with a black box and relate the time difference of arrival to the impact location, this study explicitly formulates and optimises the WVP with respect to structural stiffness and wave frequency based on the training impacts. Leveraging the partial derivatives of the dispersion equation, optimisation is performed using fast gradient-based techniques such as the Newton’s method and the trust-region reflective algorithm. The optimised WVP is then employed for impact localisation using the triangulation method. Experimental validation of the proposed hybrid physics-based and data-driven impact localisation method involves conducting drop-mass impact testing on a composite laminate flat panel and a stringer-stiffened panel. The localisation results confirm the efficiency and accuracy of the proposed hybrid method with minimal training impact data, demonstrating its capability to accurately localise impacts even for stiffened panels, including those occurring outside the coverage area of the training impacts. [Display omitted] •A novel hybrid physics-based and data-driven impact localisation method.•Dispersion relations explicitly parameterise impact-induced wave velocity profile.•Optimising wave velocity profile solely involves fast gradient-based algorithms.•Accurately localise impacts occurring outside the training coverage area.•Impact localisation with minimal training impacts, even for stiffened panels.
AbstractList The current challenges facing data-driven impact localisation methods primarily involve accurately localising impacts occurring outside the training impact coverage area, particularly for anisotropic composite structures. This study addresses these challenges by integrating the physical dispersion relations of impact-induced waves into the localisation process, thereby enhancing overall accuracy and reducing reliance on extensive training impact data. The dominant impact-induced flexural waves, derived from first-order shear deformation theory and classical laminate theory, exhibit significant dispersion. The phase velocity of these waves is governed by a dispersion equation dependent on structural stiffness, wave frequency, and direction. Solving this dispersion equation for a given wave frequency and known structural stiffness yields a wave velocity profile (WVP). Unlike conventional data-driven methods, which approximate the WVP with a black box and relate the time difference of arrival to the impact location, this study explicitly formulates and optimises the WVP with respect to structural stiffness and wave frequency based on the training impacts. Leveraging the partial derivatives of the dispersion equation, optimisation is performed using fast gradient-based techniques such as the Newton’s method and the trust-region reflective algorithm. The optimised WVP is then employed for impact localisation using the triangulation method. Experimental validation of the proposed hybrid physics-based and data-driven impact localisation method involves conducting drop-mass impact testing on a composite laminate flat panel and a stringer-stiffened panel. The localisation results confirm the efficiency and accuracy of the proposed hybrid method with minimal training impact data, demonstrating its capability to accurately localise impacts even for stiffened panels, including those occurring outside the coverage area of the training impacts. [Display omitted] •A novel hybrid physics-based and data-driven impact localisation method.•Dispersion relations explicitly parameterise impact-induced wave velocity profile.•Optimising wave velocity profile solely involves fast gradient-based algorithms.•Accurately localise impacts occurring outside the training coverage area.•Impact localisation with minimal training impacts, even for stiffened panels.
ArticleNumber 109222
Author Aliabadi, M.H.
Xiao, Dong
Sharif-Khodaei, Zahra
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Keywords Impact localisation
Composite laminates
Triangulation method
Structural health monitoring
Dispersion relations
Wave velocity profile
Language English
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Snippet The current challenges facing data-driven impact localisation methods primarily involve accurately localising impacts occurring outside the training impact...
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StartPage 109222
SubjectTerms Composite laminates
Dispersion relations
Impact localisation
Structural health monitoring
Triangulation method
Wave velocity profile
Title Hybrid physics-based and data-driven impact localisation for composite laminates
URI https://dx.doi.org/10.1016/j.ijmecsci.2024.109222
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