Publications

Natural fiber composites have gained attention as sustainable alternatives to their synthetic counterparts due to their biodegradability and renewable origins. However, their heterogeneous properties often lead to higher failure rates, making reliable quality assessment crucial. Non-destructive evaluation (NDE) therefore plays a key role in assessing these materials. While significant progress has been made in applying machine learning to NDE, further research is needed to assess its effectiveness with natural fiber-reinforced polymers (NFRPs).

To address this gap, this study investigates the use of machine learning, particularly convolutional neural networks (CNNs), to improve the defect detection process in NFRPs. For this purpose, flax/epoxy composite laminates subject to diverse damage scenarios were manufactured and scanned using phased array ultrasonic testing (PAUT). Three distinct datasets were created: the raw data, the raw data processed using the Hilbert transform, and reconstructed images derived from the raw data using Principal Component Analysis (PCA). These datasets were used to fine-tune separate pre-trained ResNet50 models to evaluate and compare their performance in classifying the images and distinguishing between those containing visible defects and those without. 

Experimental results showed that the proposed imaging system can accurately detect and classify a significant range of material defects in NFRPs of diverse dimensions, size and in-depth location through the laminate. Furthermore, the results highlight also the potential of CNN-based methods in automating and enhancing defect detection in NFRPs, offering a pathway to more reliable and efficient inspection of these materials.

With increasing interest in the recovery of agricultural waste for circular solutions, this study investigates Opuntia ficus-indica agricultural waste as a sustainable, low-cost source of reinforcement fibers for biocomposites. Opuntia ficus-indica cladodes contain a complex three-dimensional hierarchical network of fibers that have biologically evolved to provide mechanical support against bending stresses. Instead of relying on single-fiber performance, these fibrous networks offer planar reinforcement through structural configuration. Fibrous networks were obtained from Opuntia ficus-indica cladodes via two extraction techniques. The comparison between water retting and solid–liquid extraction highlights relevant trade-offs between fiber quality and processing efficiency. Water retting requires significantly longer processing times but produces fibers with higher crystallinity and lower residual parenchyma content. In contrast, solid–liquid extraction substantially reduces extraction time (up to 90%), offering potential advantages in operational efficiency and scalability; however, incomplete removal of non-fibrous tissue can compromise fiber quality and composite compatibility. Older cladodes yield fibrous networks with ideal characteristics for natural fibers intended for biocomposite applications. Further work will explore the mechanical properties of the fibrous networks obtained via water retting of old cladodes and surface treatments to improve the interfacial adhesion between the fibrous networks and polymer matrix.