Non-Destructive and Destructive Testing to Analyse the Effects of Processing Parameters on the Tensile and Flexural Properties of FFF-Printed Graphene-Enhanced PLA

Enhancing the tribological performance of PLA-based biocomposites reinforced with graphene oxide

Poly(lactic acid) (PLA) reinforced with graphene has gained substantial interest as a biomaterial, where the tribological and mechanical behavior of PLA/graphene composites are major concerns. This study aims to develop PLA-based biocomposites reinforced with graphene oxide (GO) that have enhanced tribological capabilities. First, homogenous dispersions of GO and GO treated with the anionic surfactant dioctyl sulfosuccinate sodium salt (AOT) were retained. Then, poly(L-lactic acid) (PLLA) biopolymer and PLLA/GO, PLLA/GO(AOT), PLA/GO(AOT), and PLLA/polyethylene glycol (PEG)/GO biocomposite samples were produced via hot pressing, and their tribological behavior was examined in detail. The worn surface characteristics were examined using scanning electron microscopy (SEM), 3D confocal microscopy, and atomic force microscopy (AFM). Results showed that GO reinforcement considerably affected the sliding wear behavior of PLA. Contrary to anticipated, surface treatment of GO does not improve the PLLA/GO wear resistance; rather, it increases the wear rate. PEG positively affects the sliding wear performance of PLLA/GO. PLLA/GO and PLLA/PEG/GO biocomposites exhibited the lowest wear rate at normal loads of 5 and 8 N, respectively, which was decreased by about 50% compared to unreinforced PLLA samples. With the addition of GO, the wear mechanisms of the PLA-based biocomposites changed from adhesive wear to abrasive wear. These findings might increase the applicability of PLA-based biocomposites where tribological performance is the main concern, such as biodegradable implants for load-bearing bone fractures or scaffolds, opening up new opportunities for their use.

3D Printed Graphene and Graphene/Polymer Composites for Multifunctional Applications

Three-dimensional (3D) printing, alternatively known as additive manufacturing, is a transformative technology enabling precise, customized, and efficient manufacturing of components with complex structures. It revolutionizes traditional processes, allowing rapid prototyping, cost-effective production, and intricate designs. The 3D printed graphene-based materials combine graphene's exceptional properties with additive manufacturing's versatility, offering precise control over intricate structures with enhanced functionalities. To gain comprehensive insights into the development of 3D printed graphene and graphene/polymer composites, this review delves into their intricate fabrication methods, unique structural attributes, and multifaceted applications across various domains. Recent advances in printable materials, apparatus characteristics, and printed structures of typical 3D printing techniques for graphene and graphene/polymer composites are addressed, including extrusion methods (direct ink writing and fused deposition modeling), photopolymerization strategies (stereolithography and digital light processing) and powder-based techniques. Multifunctional applications in energy storage, physical sensor, stretchable conductor, electromagnetic interference shielding and wave absorption, as well as bio-applications are highlighted. Despite significant advancements in 3D printed graphene and its polymer composites, innovative studies are still necessary to fully unlock their inherent capabilities.

Temperature and Infill Density Effects on Thermal, Mechanical and Shape Memory Properties of Polylactic Acid/Poly(ε-caprolactone) Blends for 4D Printing

Polylactic acid (PLA)/poly(ε-caprolactone) (PCL) blends have exhibited good shape memory properties and degradable characteristics in various 4D printing fields such as biomedicine, flexible electronics, and soft robotics, where the service temperature fluctuates easily by environment temperature and polymer characteristics. In this work, printed PLA/PCL 4D samples with different infill densities were prepared by material extrusion printing of pre-extruded filaments and characterized under different temperatures. The results show that the microstructures of printed samples are not influenced by printing process and have similar unique orientation as that of filaments. The thermal properties are stable and show obvious phase transition temperatures, while the mechanical properties decrease slightly in low temperature region and then decrease rapidly when temperature is over 60 °C. The increase in infill density can further improve the storage modulus more than 40% and have no significant influence on the thermal properties. The printed samples also exhibit good shape memory performances with fast recovery speeds less than 22 s. Furthermore, a two-step model is provided to predict the effective modulus of printed PLA/PCL samples and agrees well with experimental data. The results prove that temperature and infill density have different influences on the thermal, mechanical and shape memory properties of PLA/PCL blends.

Object Detection: Custom Trained Models for Quality Monitoring of Fused Filament Fabrication Process

Process reliability and quality output are critical indicators for the upscaling potential of a fabrication process on an industrial level. Fused filament fabrication (FFF) is a versatile additive manufacturing (AM) technology that provides viable and cost-effective solutions for prototyping applications and low-volume manufacturing of high-performance functional parts, yet is defect-prone due to the inherent aspect of parametrization. A systematic yet parametric workflow for quality inspection is therefore required. The work presented describes a versatile and reliable framework for automatic defect detection during the FFF process, enabled by artificial intelligence-based computer vision. Specifically, state-of-the-art deep learning models are developed for in-line inspection of individual thermoplastic strands’ surface morphology and weld quality, thus defining acceptable limits for FFF process parameter values. We examine the capabilities of an NVIDIA Jetson Nano, a low-power, high-performance computer with an integrated graphical processing unit (GPU). The developed deep learning models used in this analysis use a pre-trained model combined with manual configurations in order to efficiently identify the thermoplastic strands’ surface morphology. The proposed methodology aims to facilitate process parameter selection and the early identification of critical defects, toward an overall improvement in process reliability with reduced operator intervention.