In recent times, Fiber Reinforced Plastics’ (FRPs) popularities are ever-increasing in the aviation, automobile, marine, and power generation application, due to their superior mechanical properties and weight-saving potentials. These led to the growing concern about FRPs' reliability, being the complex failure modes, manufacturing defects, and poor out-of-plane properties. In this regard, carbon nanotubes (CNTs) have been widely used for Structure Health Monitoring (SHM) purposes, thanks to their excellent electrical properties and piezoresistive behavior. The materials with CNT nanofiller are called ‘Smart Composite’ or ‘multi-functional composite’. These materials can be prepared either using dispersed CNTs into resin matrix or with Buckypapers in an interlaminar region of laminates. In this framework, studies in the literature have proven that the CNT percolating network is sensitive to strains and damages that include tensile, compression, bending, and low-velocity tests. Alternatively, the literature review suggests that Buckypaper-based smart composites are less explored. Moreover, the behaviors of sensitive networks of the damaged specimens are unexplored. And, the abilities to detect damage precursor for Compression After Impacts (CAIs) tests were never attempted. In this study, the signals from GFRP specimens with buckypaper have been acquired in real-time during an in-plane tensile loading. Then the signals are correlated with simultaneous measurements of force, displacement, strain, and surface temperature, aimed at studying the electromechanical behavior identifying the occurrence of damage and damage mode. The study also involves the influence of Buckypaper concentration on the electromechanical behaviors. In the second phase, the signals from the dispersed CNT-GFRP plates and buckypaper-GFRP plates have been acquired during CAI tests, analyzing the behavior of damage networks, studying electromechanical behavior, and identifying precursors of specimen failures. The research framework shows the capabilities of smart composite materials to monitor strain, detect damage, identify damage mode, and reveal damage precursors, encouraging industrial applications and recommending future research plans to guarantee the required reliability with data driven diagnosis.
In tempi recenti, le popolarità delle fibre rinforzate con fibre di plastica (FRP) sono in costante aumento nell'applicazione aeronautica, automobilistica, navale e di produzione di energia, grazie alle loro proprietà meccaniche superiori e ai potenziali di risparmio di peso. Ciò ha portato alla crescente preoccupazione per l'affidabilità degli FRP, a causa delle complesse modalità di guasto, dei difetti di fabbricazione e delle scarse proprietà fuori dal piano. A questo proposito, i nanotubi di carbonio (CNT) sono stati ampiamente utilizzati per scopi di monitoraggio dello stato della struttura (SHM), grazie alle loro eccellenti proprietà elettriche e comportamento piezoresistivo. I materiali con nanofiller CNT sono chiamati "Smart Composite" o "composito multifunzionale". Questi materiali possono essere preparati utilizzando CNT dispersi in matrice di resina o con Buckypapers in una regione interlaminare di laminati. In questo quadro, studi in letteratura hanno dimostrato che la rete di percolazione CNT è sensibile a deformazioni e danni che includono prove di trazione, compressione, flessione e bassa velocità. In alternativa, la revisione della letteratura suggerisce che i compositi intelligenti basati su Buckypaper sono meno esplorati. Inoltre, i comportamenti delle reti sensibili degli esemplari danneggiati sono inesplorati. Inoltre, non è mai stata tentata la capacità di rilevare precursori del danno per i test CAI (Compression After Impacts). In questo studio, i segnali dai campioni GFRP con buckypaper sono stati acquisiti in tempo reale durante un carico di trazione in piano. Quindi i segnali sono correlati con misurazioni simultanee di forza, spostamento, deformazione e temperatura superficiale, allo scopo di studiare il comportamento elettromeccanico identificando il verificarsi di danno e la modalità di danno. Lo studio coinvolge anche l'influenza della concentrazione di Buckypaper sui comportamenti elettromeccanici. Nella seconda fase, i segnali dalle piastre CNT-GFRP disperse e dalle piastre buckypaper-GFRP sono stati acquisiti durante i test CAI, analizzando il comportamento delle reti di danni, studiando il comportamento elettromeccanico e identificando i precursori dei guasti dei campioni. Il framework di ricerca mostra le capacità dei materiali compositi intelligenti per monitorare la deformazione, rilevare i danni, identificare la modalità di danno e rivelare i precursori dei danni, incoraggiare le applicazioni industriali e raccomandare piani di ricerca futuri per garantire l'affidabilità richiesta con la diagnosi basata sui dati.
Experimental investigations of smart composites for in-situ structural health monitoring
Patel, Bhavik
2019/2020
Abstract
In recent times, Fiber Reinforced Plastics’ (FRPs) popularities are ever-increasing in the aviation, automobile, marine, and power generation application, due to their superior mechanical properties and weight-saving potentials. These led to the growing concern about FRPs' reliability, being the complex failure modes, manufacturing defects, and poor out-of-plane properties. In this regard, carbon nanotubes (CNTs) have been widely used for Structure Health Monitoring (SHM) purposes, thanks to their excellent electrical properties and piezoresistive behavior. The materials with CNT nanofiller are called ‘Smart Composite’ or ‘multi-functional composite’. These materials can be prepared either using dispersed CNTs into resin matrix or with Buckypapers in an interlaminar region of laminates. In this framework, studies in the literature have proven that the CNT percolating network is sensitive to strains and damages that include tensile, compression, bending, and low-velocity tests. Alternatively, the literature review suggests that Buckypaper-based smart composites are less explored. Moreover, the behaviors of sensitive networks of the damaged specimens are unexplored. And, the abilities to detect damage precursor for Compression After Impacts (CAIs) tests were never attempted. In this study, the signals from GFRP specimens with buckypaper have been acquired in real-time during an in-plane tensile loading. Then the signals are correlated with simultaneous measurements of force, displacement, strain, and surface temperature, aimed at studying the electromechanical behavior identifying the occurrence of damage and damage mode. The study also involves the influence of Buckypaper concentration on the electromechanical behaviors. In the second phase, the signals from the dispersed CNT-GFRP plates and buckypaper-GFRP plates have been acquired during CAI tests, analyzing the behavior of damage networks, studying electromechanical behavior, and identifying precursors of specimen failures. The research framework shows the capabilities of smart composite materials to monitor strain, detect damage, identify damage mode, and reveal damage precursors, encouraging industrial applications and recommending future research plans to guarantee the required reliability with data driven diagnosis.| File | Dimensione | Formato | |
|---|---|---|---|
|
Experimental investigations of smart composites for insitu structural health monitoring.pdf
non accessibile
Descrizione: Thesis
Dimensione
6.34 MB
Formato
Adobe PDF
|
6.34 MB | Adobe PDF | Visualizza/Apri |
I documenti in POLITesi sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/10589/164714