LH
L. Hohaus
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
1 records found
1
The use of fiber-reinforced composite materials in marine applications is limited by uncertainty surrounding their long-term fatigue behavior and micro-damage tolerance. This thesis aims to present and validate an experimental framework to detect and classify mechanical micro-damage in unidirectional carbon-fiber composites using acoustic emission (AE) monitoring and X-ray micro-computed tomography (micro-CT). AE monitoring provides real-time insight into the evolution of internal damage by capturing elastic waves emitted during micro-structural failure events, while micro-CT offers high-resolution visualization of internal damage states before and after mechanical loading. A comprehensive analysis was conducted involving signal processing, (normalized) frequency spectrum characterization, and unsupervised machine learning to classify AE events by damage type. This classification was subsequently validated against micro-CT scans. Results challenge the common assumption that AE signals with dominant low-frequency contributions are reliably indicative of matrix cracking. The proposed AE framework, when validated with micro-CT, shows promise for enabling accurate in-situ damage monitoring of composite structures in offshore environments. This approach supports the broader adoption of composites by improving confidence and knowledge about their structural integrity over time.
...
The use of fiber-reinforced composite materials in marine applications is limited by uncertainty surrounding their long-term fatigue behavior and micro-damage tolerance. This thesis aims to present and validate an experimental framework to detect and classify mechanical micro-damage in unidirectional carbon-fiber composites using acoustic emission (AE) monitoring and X-ray micro-computed tomography (micro-CT). AE monitoring provides real-time insight into the evolution of internal damage by capturing elastic waves emitted during micro-structural failure events, while micro-CT offers high-resolution visualization of internal damage states before and after mechanical loading. A comprehensive analysis was conducted involving signal processing, (normalized) frequency spectrum characterization, and unsupervised machine learning to classify AE events by damage type. This classification was subsequently validated against micro-CT scans. Results challenge the common assumption that AE signals with dominant low-frequency contributions are reliably indicative of matrix cracking. The proposed AE framework, when validated with micro-CT, shows promise for enabling accurate in-situ damage monitoring of composite structures in offshore environments. This approach supports the broader adoption of composites by improving confidence and knowledge about their structural integrity over time.