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.

Three-dimensional (3D) printing has introduced a paradigm shift in the manufacturing world, and it is increasing in popularity. In cases of such rapid and widespread acceptance of novel technologies, material or process safety issues may be underestimated, due to safety research being outpaced by the breakthroughs of innovation. However, a definitive approach in studying the various occupational or environmental risks of new technologies is a vital part of their sustainable application. In fused filament fabrication (FFF) 3D printing, the practicality and simplicity of the method are juxtaposed by ultrafine particle (UFP) and volatile organic compound (VOC) emission hazards. In this work, the decision of selecting the optimal material for the mass production of a microfluidic device substrate via FFF 3D printing is supported by an emission/exposure assessment. Three candidate prototype materials are evaluated in terms of their comparative emission potential. The impact of nozzle temperature settings, as well as the microfluidic device’s structural characteristics regarding the magnitude of emissions, is evaluated. The projected exposure of the employees operating the 3D printer is determined. The concept behind this series of experiments is proposed as a methodology to generate an additional set of decision-support decision-making criteria for FFF 3D printing production cases.

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.

In the present study, a series of carbon-based nanocomposites based on recycled thermoplastic polyurethane (TPU) matrix and MWCNT fillers synthesized in a laboratory environment were prepared at various loadings and assessed in terms of their functional thermal, dielectric, and rheological properties, as well as their ohmic heating capability, for self-healing applications in extrusion-based 3D printing technologies. The synthesis of nanomaterials focused on the production of two different types of carbon nanotubes (CNTs) via the chemical vapor deposition (CVD) method. A comparative assessment and benchmarking were conducted with nanocomposite filaments obtained from commercial nanomaterials and masterbatches with MWCNTs. For all the polymer nanocomposites, samples were prepared at additive contents up to 15 wt.% and filament feedstock was produced via the melt-extrusion process for 3D printing; these were previously characterized by rheological tests. The measurements of thermal and electrical conductivity resulted in a selected composition with promising ohmic heating capability. As a preliminary assessment of the self-healing ability of the above samples, artificial cracks were introduced on the surface of the samples and SEM analysis took place at the crack location before and after applying voltage as a measure of the effectiveness of the material remelting due to the Joule effect. Results indicate a promising material response with a partial restoration of artificial cracks.

In the present study, the feasibility to achieve localized induction heating and debonding of multi-material composite structures is assessed in testing coupons prepared by Automated Fiber Placement (AFP) and extrusion-based additive manufacturing (AM) technologies. Nano-compounds of Polyether-ketone-ketone (PEKK) with iron oxide nanoparticles acting as electromagnetic susceptors have been processed in a parallel co-rotating twin-screw extruder to produce filament feedstock for extrusion-based AM. The integration of nanocomposite interlayers as discrete debonding zones (DZ) by AFP-AM manufacturing has been investigated for two types of sandwich-structured laminate composites, i.e., laminate-DZ-laminate panels (Type I) and laminate-DZ-AM gyroid structures (Type II). Specimens were exposed to an alternating magnetic field generated by a radio frequency generator and a flat spiral copper induction coil, and induction heating parameters (frequency, power, heating time, sample standoff distance from coil) have been investigated in correlation with real-time thermal imaging to define the debonding process window without compromising laminate quality. For the optimized process parameters, i.e., 2–3 kW generator power and 20–25 mm standoff distance, corresponding to magnetic field intensities in the range of 3–5 kA m⁻¹, specimens were effectively heated above PEKK melting temperature, exhibiting high heating rates within the range of 5.3–9.4 °C/s (Type I) and 8.0–17.5 °C/s (Type II). The results demonstrated that localized induction heating successfully facilitated debonding, leading to full unzipping of the debonding zones in both laminate structures. Further insight on PEKK nanocomposites debonding performance was provided by thermal, morphological characterization and non-destructive inspection via X-ray micro-computed tomography at different processing stages. The developed framework aims to contribute to the development of rapid, on-demand joining, repair and disassembly technologies for thermoplastic composites, towards more efficient maintenance, repair and overhaul operations in the aviation sector and beyond.