The aim of this study was to investigate the effect of recycling on polypropylene (PP) and wood-fiber thermoplastic composites (WPCs) using a co-rotating twin-screw extruder. After nine extrusion passes microscopy studies confirmed that the fiber length decreased with the increased number of recycling passes but the increased processing time also resulted in excellent dispersion and interfacial adhesion of the wood fibers in the PP matrix. Thermal, rheological, and mechanical properties were studied. The repeated extrusion passes had minimal effect on thermal behavior and the viscosity decreased with an increased number of passes, indicating slight degradation. The recycling processes had an effect on the tensile strength of WPCs while the effect was minor on the PP. However, even after the nine recycling passes the strength of WPC was considerably better (37 MPa) compared to PP (28 MPa). The good degree of property retention after recycling makes this recycling strategy a viable alternative to discarding the materials. Thus, it has been demonstrated that, by following the most commonly used extrusion process, WPCs can be recycled several times and this methodology can be industrially adapted for the manufacturing of recycled products.

In this study, the possibility of adding nanocellulose and its dispersion to polyamide 6 (PA6), a polymer with a high melting temperature, is investigated using melt extrusion. The main challenges of the extrusion of these materials are achieving a homogeneous dispersion and avoiding the thermal degradation of nanocellulose. These challenges are overcome by using an aqueous suspension of never-dried nanocellulose, which is pumped into the molten polymer without any chemical modification or drying. Furthermore, polyethylene glycol is tested as a dispersant for nanocellulose. The dispersion, thermal degradation, and mechanical and viscoelastic properties of the nanocomposites are studied. The results show that the dispersant has a positive impact on the dispersion of nanocellulose and that the liquid-assisted melt compounding does not cause the degradation of nanocellulose. The addition of only 0.5 wt.% nanocellulose increases the stiffness of the neat polyamide 6 from 2 to 2.3 GPa and shifts the tan δ peak toward higher temperatures, indicating an interaction between PA6 and nanocellulose. The addition of the dispersant decreases the strength and modulus but has a significant effect on the elongation and toughness. To further enhance the mechanical properties of the nanocomposites, solid-state drawing is used to create an oriented structure in the polymer and nanocomposites. The orientation greatly improves its mechanical properties, and the oriented nanocomposite with polyethylene glycol as dispersant exhibits the best alignment and properties: with orientation, the strength increases from 52 to 221 MPa, modulus from 1.4 to 2.8 GPa, and toughness 30 to 33 MJ m−3 in a draw ratio of 2.5. This study shows that nanocellulose can be added to PA6 by liquid-assisted extrusion with good dispersion and without degradation and that the orientation of the structure is a highly-effective method for producing thermoplastic nanocomposites with excellent mechanical properties.

Structures can be subjected to damage, leading to catastrophic failures and significant financial losses. Thus, researchers have been studying several tools to ensure reliability and safety. Thus, structural health monitoring has drawn attention, mainly by using tools such as vibration-based model and artificial neural networks. So, this work aims to develop a methodology to identify and classify damage in glass fiber-reinforced plastic composite beams through vibration data and artificial neural network. For this, healthy and damaged beams were manufactured considering different delamination sizes. Then, dynamic tests were performed to obtain both time and frequency domain data. As the large dimension of the data obtained by the vibrational tests, hinders its direct use to feed the neural networks, a strategy called dislocated series is used to reduce the raw signal size in mini batches, without losing important information to detect the damage. Results show that the artificial neural network topology and the parameters of the dislocated time series are crucial to the success of the proposed methodology. When these parameters are properly selected, it is possible to successfully detect and classify damage with less computational cost when compared to the direct use of the vibration-based model data.

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