Inducing Damage Diagnosis Capabilities in Carbon Fiber Reinforced Polymer Composites by Magnetoelastic Sensor Integration via 3D Printing
This study investigates the possibility of inducing damage diagnosis capabilities in carbon fiber reinforced polymer composite slabs using custom-built integrated sensors and conventional, affordable equipment. The concept utilizes magnetoelastic strips integrated via 3D printing procedures in compo...
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Veröffentlicht in: | Applied sciences 2020-02, Vol.10 (3), p.1029 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This study investigates the possibility of inducing damage diagnosis capabilities in carbon fiber reinforced polymer composite slabs using custom-built integrated sensors and conventional, affordable equipment. The concept utilizes magnetoelastic strips integrated via 3D printing procedures in composite slabs. Under external mechanical loading, the strip magnetization changes due to the magnetoelastic phenomenon. Accordingly, electrical signals may be passively induced in conventional reception coil circuits placed at a distance from the slab. Since these signals quantify the vibrating slab’s response, which is affected by the slab’s structural integrity, damage may be detected when specific signal characteristics change. Two main issues are examined, namely the ability of receiving meaningful (with respect to noise) electrical signals from the built-in strips despite their contact-less passive reception, and the potential of diagnosing damage using such signals. Hence, slabs of various sizes and levels of structural damage (notches) have been vibrated at different frequencies and amplitudes. Treating the experimental data from integrated strips by applying the proposed processing framework allows for calculating eigenfrequencies sensitive to occurring damage (and its severity), as verified by finite element models of the vibrating slabs. Accordingly, damage may be detected and evaluated via the currently proposed experimental testing and analysis framework. |
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ISSN: | 2076-3417 2076-3417 |
DOI: | 10.3390/app10031029 |