Optimization of Extrusion-Based 3D Printing Process Using Neural Networks for Sustainable Development

Using particle dimensionality-based modeling to estimate lung carcinogenicity of 3D printer emissions

The use of 3D printing technologies by industry and consumers is expanding. However, the approaches to assess the risk of lung carcinogenesis from the emissions of 3D printers have not yet been developed. The objective of the study was to demonstrate a methodology for modeling lung cancer risk related to specific exposure levels as derived from an experimental study of 3D printer emissions for various types of filaments (ABS, PLA, and PETG). The emissions of 15 filaments were assessed at varying extrusion temperatures for a total of 23 conditions in a Class 1,000 cleanroom following procedures described by ANSI/CAN/UL 2904. Three approaches were utilized for cancer risk estimation: (a) calculation based on PM2.5 and PM10 concentrations, (b) a proximity assessment based on the pulmonary deposition fraction, and (c) modeling based on the mass-weighted aerodynamic diameter of particles. The combined distribution of emitted particles had the mass median aerodynamic diameter (MMAD) of 0.35 μm, GSD 2.25. The average concentration of PM2.5 was 25.21 μg/m3 . The spline-based function of aerodynamic diameter allowed us to reconstruct the carcinogenic potential of seven types of fine and ultrafine particles (crystalline silica, fine TiO2 , ultrafine TiO2 , ambient PM2.5 and PM10, diesel particulates, and carbon nanotubes) with a correlation of 0.999, P < 0.00001. The central tendency estimation of lung cancer risk for 3D printer emissions was found at the level of 14.74 cases per 10,000 workers in a typical exposure scenario (average cumulative exposure of 0.3 mg/m3 - years), with the lowest risks for PLA filaments, and the highest for PETG type.

Improved Optimization of a Coextrusion Die with a Complex Geometry Using the Coupling Inverse Design Method

The main challenge in a polymer coextrusion process is to have a good die design prior to the process, which can minimize the geometric errors that are caused by extrusion swell and interface motion. For this purpose, a coupling method of optimization and inverse design for a coextrusion die was studied for a medical striped catheter. In the study, the main material was thermoplastic polyurethane (TPU), and the auxiliary material was TPU filled with 30 wt% barium sulfate. An overall optimization design method was used to optimize the geometry of the extrusion die channel for the striped catheter, which had a complex geometry. In the global optimization process, the local inverse design method was used to design the inlet of the auxiliary material. The non-linear programming by quadratic Lagrangian (NLPQL) algorithm was used to obtain the optimal geometric solution of the coextrusion die runner. The experimental verification results showed that the coupling method for coextrusion die design improved the design efficiency of the coextrusion die remarkably. The value of the objective function, which was used to measure the geometric error of the product, was reduced by 72.3% compared with the initial die design.

Direct-Ink-Write Printing and Electrospinning of Cellulose Derivatives for Conductive Composite Materials

The aim of this study is to realize the controlled construction and modulation of micro-/nanostructures of conductive composite materials (CCMs) in a facile way. Herein, interdigital electrodes are prepared by direct-ink-write printing co-blended inks made of ethyl cellulose and carbon nanotubes on cellulose paper. The cellulose nanofibers (CFs) are prepared by electrospinning cellulose acetate on to an aluminum foil, followed by deacetylation in NaOH/ethanol. All co-blended inks exhibit a typical non-Newtonian shear thinning behavior, enabling smooth extrusion and printing. The above electrodes and the conductive CF films with excellent thermal stability are assembled into a pressure sensor, which has a high sensitivity (0.0584 KPa⁻¹) to detect the change in external loading pressure. The obtained porous CFs film is further endowed with conductivity by in situ polymerization of polypyrrole (PPy), which are uniformly distributed on the CFs surface as particles; a triboelectric nanogenerator is constructed by using the CF@PPy film as a tribo-positive friction layer to achieve efficient energy harvesting (output voltage = 29.78 V, output current = 2.12 μA). Therefore, the construction of CCMs with micro-/nanostructures based on cellulose derivatives have essential application prospects in emerging high-tech fields, such as green electronics for sensing and energy harvesting.

Reducing Waste in 3D Printing Using a Neural Network Based on an Own Elbow Exoskeleton

Traditional rehabilitation systems are evolving into advanced systems that enhance and improve rehabilitation techniques and physical exercise. The reliable assessment and robotic support of the upper limb joints provided by the presented elbow exoskeleton are important clinical goals in early rehabilitation after stroke and other neurological disorders. This allows for not only the support of activities of daily living, but also prevention of the progression neuromuscular pathology through proactive physiotherapy toward functional recovery. The prices of plastics are rising very quickly, as is their consumption, so it makes sense to optimize three dimensional (3D) printing procedures through, for example, improved artificial intelligence-based (AI-based) design or injection simulation, which reduces the use of filament, saves material, reduces waste, and reduces environmental impact. The time and cost savings will not reduce the high quality of the products and can provide a competitive advantage, especially in the case of thinly designed mass products. AI-based optimization allows for one free print after every 6.67 prints (i.e., from materials that were previously wasted).

A Review on Filament Materials for Fused Filament Fabrication

Fused filament fabrication (FFF) is one of the most popular additive manufacturing (AM) processes that utilize thermoplastic polymers to produce three-dimensional (3D) geometry products. The FFF filament materials have a significant role in determining the properties of the final part produced, such as mechanical properties, thermal conductivity, and electrical conductivity. This article intensively reviews the state-of-the-art materials for FFF filaments. To date, there are many different types of FFF filament materials that have been developed. The filament materials range from pure thermoplastics to composites, bioplastics, and composites of bioplastics. Different types of reinforcements such as particles, fibers, and nanoparticles are incorporated into the composite filaments to improve the FFF build part properties. The performance, limitations, and opportunities of a specific type of FFF filament will be discussed. Additionally, the challenges and requirements for filament production from different materials will be evaluated. In addition, to provide a concise review of fundamental knowledge about the FFF filament, this article will also highlight potential research directions to stimulate future filament development. Finally, the importance and scopes of using bioplastics and their composites for developing eco-friendly filaments will be introduced.