Additive manufacturing (AM) of composite materials is promising to create customizable products with enhanced properties, utilizing materials like carbon fibers (CFs). To increase their circularity, composite recycling has been proposed to re-introduce the recovered components in AM. A careful evaluation of recycling is necessary, considering the sustainability and functionality (i.e., mechanical properties) of the recovered components. Thus, Life Cycle Assessment (LCA) is applied to estimate the environmental impacts of AM via Fused Filament Fabrication (FFF), using virgin or recycled CFs via solvolysis at a laboratory scale. This study aims to provide a detailed Life Cycle Inventory (LCI) of FFF and evaluate the sustainability of using recycled CFs in AM. For both virgin CF manufacturing and CF recycling, electricity consumption was the main contributor to environmental impacts. CF recovery via solvolysis resulted in lower impacts across most impact categories compared to AM with virgin CFs. Different scenarios were examined to account for the mechanical properties of recycled CFs. AM with 75% recycled CFs, compared to 100% virgin CFs undergoing landfilling, resulted in over 22% reduction in climate change potential, even after a 50% loss of recycled CF functionality. Overall, this study offers insights into the LCI of FFF and shows that CF recycling from composites is worth pursuing.

Additive manufacturing (AM) technologies enable the production of customized and personalized medical devices that facilitate users’ comfort and rehabilitation requirements according to their individual conditions. The concept of a tailor-made orthopedic device addresses the accelerated recovery and comfort of the patient through the utilization of personalized rehabilitation equipment. Direct modeling, with an increasing number of approaches and prototypes, has provided many successful results until now. The modeling procedure for 3D-printed orthoses has emerged as the execution of steady and continuous tasks with several design selection criteria, such as cutting, thickening the surface, and engraving the shell of the orthosis. This publication takes into consideration the aforementioned criteria and proposes the creation of a holistic methodology and automated computational design process for the customization of orthotic assistive devices, considering aspects such as material properties, manufacturing limitations, recycling, and patients’ requirements. This proposal leads to the designing and manufacturing of a wrist orthopedic device based on reverse engineering, Design for AM (DfAM), and Design for Recycling (DfR) principles. The proposed methodology can be adjusted for different limbs. A dual-material approach was attained utilizing rigid, mechanically enhanced feedstock material and soft elastic material with reduced skin irritation risks to achieve both mechanical requirements and adequate cushioning for user comfort during rehabilitation. Recyclable thermoplastic matrices were selected, which also allow for the option to create washable devices for product life extension. Then, 3D scanning procedures were implemented to acquire the initial anatomic measurements for the design of the WHO and ensure and assess the dimensional accuracy of the final product. Physical mechanical testing was implemented to evaluate the WHO’s mechanical behavior and verify its functionality during basic wrist movements. The extracted dimensional data for the two main orthosis components that indicated approximately 50% and 25% of the tolerance values, respectively, were within the range (−0.1 mm, 0.1 mm).

A polyamide (PA) 12-based thermoplastic composite was modified with carbon nanotubes (CNTs), CNTs grafted onto chopped carbon fibers (CFs), and graphene nanoplatelets (GNPs) with CNTs to improve its thermal conductivity for application as a heat sink in electronic components. The carbon-based nanofillers were examined by SEM and Raman. The laser flash method was used to measure the thermal diffusivity in order to calculate the thermal conductivity. Electrical conductivity measurements were made using a Keithley 6517B electrometer in the 2-point mode. The composite structure was examined by SEM and micro-CT. PA12 with 15 wt% of GNPs and 1 wt% CNTs demonstrated the highest thermal conductivity, and its processability was investigated, utilizing sequential interdependence tests to evaluate the composite material behavior during fused filament fabrication (FFF) 3D printing processing. Through this assessment, selected printing parameters were investigated to determine the optimum parametric combination and processability window for the composite material, revealing that the selected composition meets the necessary criteria to be processable with FFF.

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.

Fused Filament Fabrication (FFF) has been established as a widely practiced Additive Manufacturing technique, using various thermoplastic filaments. Carbon fibre (CF) additives enhance mechanical properties of the materials. The main operational hazard of the FFF technique explored in the literature is the emission of Ultrafine Particles and Volatile Organic Compounds. Exposure data regarding novel materials and larger scale operations is, however, still lacking. In this work, a thorough exposure assessment measurement campaign is presented for a workplace applying FFF 3D printing in various setups (four different commercial devices, including a modified commercial printer) and applying various materials (polylactic acid, thermoplastic polyurethane, copolyamide, polyethylene terephthalate glycol) and CF-reinforced thermoplastics (thermoplastic polyurethane, polylactic acid, polyamide). Portable exposure assessment instruments are employed, based on an established methodology, to study the airborne particle exposure potential of each process setup. The results revealed a distinct exposure profile for each process, necessitating a different safety approach per setup. Crucially, high potential for exposure is detected in processes with two printers working simultaneously. An updated engineering control scheme is applied to control exposures for the modified commercial printer. The establishment of a flexible safety system is vital for workplaces that apply FFF 3D printing.

Stereolithography (SL) additive manufacturing process provides increased dimensional precision, smooth surface finish and printing resolution range in the order of magnitude of 100 μm, allowing to obtain intricate 3D geometries. The incorporation of ceramic-based inclusions within liquid resins enhances the thermal and mechanical properties of the final 3D printed component while improving the surface finishing of the final parts; in this way, it expands the range of process applications and reduces the post-processing steps. The proposed approach investigates the bulk modification of commercial SLA resins mixed with ceramic powders of Al₂O₃ (grain size 1–10 μm) and SiO₂ (grain size 55–75 nm) aiming to improve 3D printed parts performance in terms of mechanical properties, dimensional stability and surface finishing compared with pure, unmodified resins. The produced materials were used for the development of inserts for injection moulding and were examined for their performance during the injection moulding process. The addition of particles in the nano- and micro-range is being employed to improve parts performance for rapid tooling applications whilst maintaining 3D printing accuracy, thermal and mechanical properties as well as achieving a smooth surface finishing compared with unmodified resins.