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This work deals with the experimental investigation and multi-objective optimization of mean kerf angle (A) and mean surface roughness (Ra) in laser cutting (LC) fused filament fabrication (FFF) 3D-printed (3DP), 4 mm-thick polylactic acid (PLA) plates by considering laser feed (F) and power (P) as the independent control parameters. A CO2 laser apparatus was employed to conduct machining experiments on 27 rectangular workpieces. An experimental design approach was adopted to establish the runs according to full-combinatorial design with three repetitions, resulting in 27 independent experiments. A customized response surface experiment was formulated to proceed with regression equations to predict the responses and examine the solution domain continuously. After examining the impact of F and P on mean A and mean Ra, two reliable prediction models were generated to model the process. Furthermore, since LC is a highly intricate, non-conventional machining process and its control variables affect the responses in a nonlinear manner, A and Ra were also predicted using an artificial neural network (NN), while its resulting performance was compared to the predictive regression models. Finally, the regression models served as objective functions for optimizing the responses with an intelligent algorithm adopted from the literature. [ABSTRACT FROM AUTHOR]

Material extrusion (MEX), frequently known as fused filament fabrication (FFF), is a material extrusion 3D printing process for fabricating cost-effectively functional polymeric parts. Even if the MEX process fabricates fully dense or lightweight specimens with a relatively complex geometry in a reduced time and cost, it presents some drawbacks in shape accuracy, surface roughness, and anisotropic mechanical response. Herewith, the interaction between material design, 3D printing, and laser post-processing is investigated for efficient printable nanocomposite materials. Polylactic acid (PLA)/carbon black (CB) nanocomposites were prepared with a thermomechanical process and characterized for their mechanical and electrical properties. Then, they were processed with a low-cost CO2 laser, to assess their behavior in the process. Critical geometrical characteristics were measured, and results were analyzed with statistical modeling, to optimize the process and evaluate the effect of the laser cutting parameters on the quality of the samples. This paper points out new data on low-cost CO2 laser cutting of MEX thin plates fabricated using PLA/CB nanocomposite filament and an affordable material extrusion MEX 3D printing process. The data produced are helpful for the improvement of the shape accuracy and surface roughness of parts and components used in nanocomposite-based innovative assemblies and systems. [ABSTRACT FROM AUTHOR]

This Special Issue of AIMS Materials Science was devoted to the topic "Materials for Additive Manufacturing". It attracted significant attention from scholars and practitioners from ten different countries (Spain, Greece, France, Portugal, Italy, Finland, Ethiopia, Canada, Vietnam, and Iraq) and published five manuscripts of a total of ten submissions between April 2021 and March 2022. In addition, new materials, methodologies, and analysis approaches are presented in materials for additive manufacturing. [ABSTRACT FROM AUTHOR]

This work investigates the effect of 3D-printing parameters on surface roughness in polylactic acid printed material by adopting Taguchi's design of experiments approach. The control parameters under study were: number of shells, printing temperature, infill rate, and printing pattern. As the response, mean surface roughness (Ra) was selected. The control parameters were assigned to an L9 orthogonal array to organize the experiments and obtain the mean surface roughness results. It is concluded that printing temperature is the dominant parameter that affects surface roughness when it comes to 3D printing of polylactic acid material followed by printing pattern, infill rate, and the number of shells. [ABSTRACT FROM AUTHOR]

This study investigates the mechanical response of antibacterial PA12/TiO2 nanocomposite 3D printed specimens by varying the TiO2 loading in the filament, raster deposition angle, and nozzle temperature. The prediction of the antibacterial and mechanical performance of such nanocomposites is a challenging field, especially nowadays with the covid-19 pandemic dilemma. The experimental work in this study utilizes a fully factorial design approach to analyze the effect of three parameters on the mechanical response of 3D printed components. Therefore, all combinations of these three parameters were tested, resulting in twenty-seven independent experiments, in which each combination was repeated three times (a total of eighty-one experiments). The antibacterial performance of the fabricated PA12/TiO2 nanocomposite materials was confirmed, and regression and arithmetic artificial neural network (ANN) models were developed and validated for mechanical response prediction. The analysis of the results showed that an increase in the TiO2% loading decreased the mechanical responses but increased the antibacterial performance of the nanocomposites. In addition, higher nozzle temperatures and zero deposition angles optimize the mechanical performance of all TiO2% nanocomposites. Independent experiments evaluated the proposed models with mean absolute percentage errors (MAPE) similar to the ANN models. These findings and the interaction charts show a strong interaction between the studied parameters. Therefore, the authors propose the improvement of predictions by utilizing artificial neural network models and genetic algorithms as future work and the spreading of the experimental area with extra variable parameters and levels. [ABSTRACT FROM AUTHOR]

The feasibility of joining material extrusion (MEX) 3D-printed acrylonitrile butadiene styrene (ABS) plates with the friction stir welding (FSW) process was investigated herein as a promising topic of hybrid additive manufacturing (HAM). The influence of three process parameters on the mechanical strength of the joints was thoroughly examined and analyzed with a full factorial experimental design and statistical modeling. Hereto, the welding tool pin geometry, travel speed, and rotational speed were investigated. The joint's efficiency and quality are evaluated through tensile tests and morphological characterization. More specifically, specimens' areas of particular interest were investigated with stereoscopic, optical, and scanning electron microscopy. Throughout the FSW experimental course, the welding temperature was monitored to evaluate the state of the ABS material during the process. The majority of the welded specimens exhibited increased mechanical strength compared with the respective ones of non-welded 3D printed specimens of the same geometry. Statistical modeling proved that all processing parameters were significant. The feasibility of the FSW process in 3D printed ABS workpieces was confirmed, making the FSW a cost-effective process for joining 3D printing parts, further expanding the industrial merit of the approach. [ABSTRACT FROM AUTHOR]

The effect of Cellulose NanoFiber (CNF) addition to a medical-grade resin in Stereolithography (SLA) Additive Manufacturing (AM) technology is reported, aiming to elaborate an easily processable, highly stiff bio-compound. CNFs were shear stir blended at various weight ratios with liquid resin. The fabricated nanocomposite materials were introduced in an SLA 3D printer for specimens manufacturing. The mechanical performance was studied according to international standards. Charpy Toughness and Vickers microhardness were calculated for all tested materials. A microscopic and surface analysis was conducted on fractured tensile specimens by Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM), respectively. The thermal and thermomechanical properties were investigated by Thermogravimetric Analysis (TGA), Differential Calorimetry (DSC), and Dynamic Mechanical Analysis (DMA). Significant reinforcement of the medical-grade nanocomposites is reported, with the highest values calculated to be at 1.0 wt% concentration (more than 100% at the tensile strength), while brittleness and rigidity were increased. [Display omitted] • Vat photopolymerization biomedical resins. • Facile processing of cellulose sustainable nano-enhanced composites. • Mechanical, thermal and fractographical properties were fully characterized. • Nanocomposites showed significant improvement in mechanical performance. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, wide use of 3D printing technologies has been enabled. Fused filament fabrication (FFF) is the most widely used technique in 3D printing communities worldwide for the fabrication of medical components such as face shields and respiratory valves. In the current study, the potential of Polyamide 12 (PA12) silver-doped antibacterial nanopowder (AgDANP) nanocomposites is evaluated for everyday FFF usage. Filling loadings of 1.0-2.0-3.0 and 4.0 wt.% were selected for nanocomposite preparation. Mechanical performance analysis was conducted on the basis of tensile, flexural, impact, and Vickers microhardness measurements in FFF 3D-printed specimens. Scanning Electron Microscopy (SEM) images were used for morphology and processing evaluation, as well as thermal performance measurements, conducted by Thermogravimetric Analysis (TGA) tests. Finally, the antibacterial performance was tested using the agar-well diffusion screening method, and the shape effect of the specimens was also investigated. The addition of 2.0 wt.% AgDANPs resulted in an enhancement of approximately 27% for both tensile and flexural stresses, while the antibacterial performance was sufficiently high among the nanocomposites tested. The shape effect exhibited the potential for antibacterial performance at low filling ratios, while the effect was diminished with increasing filler of AgDANPs. [ABSTRACT FROM AUTHOR]