Magnetic Field?Based Strategy for Damage Detection and Stratigraphy Assessment for Structural Health Monitoring Applications

This study presents a strategy for damage detection and stratigraphy assessment based on the use of hard magnetic particles. The validation of the concept is based on the production of composite laminates containing NdFeB particles on the coating layer.Strong efforts are being carrying out to implement efficient and accurate structural health monitoring (SHM) strategies for large surfaces. The criticality of high demanding structures, such as vessels, pipelines, or aircrafts, requires costly integrity inspections to ensure their performance and security. This study presents a novel SHM approach using magnetic particles for precise damage detection and coating stratigraphy assessment. The methodology is demonstrated on an industrially produced sheet molding compound (SMC) laminate composite, incorporating an epoxy coating layer doped with 20?wt% NdFeB particles. The coating achieves a coercive field (Hc) of 7000?Oe and a saturation magnetization (Ms) of 136.8?emu?g?1. Uniform dispersion of magnetic particles is confirmed, while surface characterization indicates minimal impact on roughness and wettability. Mechanical testing shows a negligible effect on flexural modulus but a reduction in bending strength due to constrained molecular mobility. Magnetic flux mapping enables fast and accurate damage detection and coating depth assessment. Perpendicular magnetization enhances damage detection and sensitivity, while longer acquisition times (3?s) and higher magnetization fields (1.1?T) improve resolution and facilitate thickness evaluation. These findings highlight the potential of these coatings for SHM, offering precise damage detection and insights into coating uniformity, ultimately supporting advanced monitoring solutions for critical structures.

» Author: Luis Amorim, Pedro Martins, Carmen R. Tubio, Ruben Seoane?Rivero, Senentxu Lanceros?Mendez

» Publication Date: 21/09/2025

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


                   




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