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The present study focuses on an experimental investigation aiming at simultaneously optimizing tensile strength and elastic modulus of 3D-printed polymethyl methacrylate (PMMA) filament material while considering raster angle, printing speed, and layer thickness as the independent process-related control parameters. The Box-Behnken design of experiments (BBD) was applied to design the necessary number of experiments and establish the experimental design for fabricating experimental dog-bone samples of standard geometry. Further on, second-order regression models for tensile strength and elastic modulus were generated and employed to predict the three independent structural parameters and maximize tensile strength and elastic modulus. A neural network model was examined for its efficiency in terms of predicting the two responses. Results show a high correlation between inputs and outputs, enabling reliable modeling towards the objective of optimizing both the strength and elasticity of PMMA-fabricated parts. Regression models exhibited high correlation (R2) equal to 97.81% and 97.26% for tensile strength and elastic modulus, respectively. Simulation results referring to the neural network suggest a high correlation between outputs and targets during training (R = 0.9762), validation (R = 0.9974), and testing (R = 0.9808). [ABSTRACT FROM AUTHOR]

As a typical 3D printing process, fused filament fabrication still has disadvantages when operating on manufacturing lines due to the non-uniform textures of the oriented surfaces of the 3D-printed components. This work investigates the effects of structural parameters, i.e., orientations angle, ABS and PLA materials, three different layer thicknesses, three different perimeters, and three different infill rates utilizing a balanced modified Taguchi experimental design and 63 different parametric combinations to characterize the surface roughness parameters: average Ra, mean roughness depth Rz, root mean square Rq, skewness Rsk, and kurtosis Rku. The analysis of the experimental results, i.e., the levels mean values analysis plots and linear residual analysis of variances, showed that the layer thickness strongly influences all surface parameters and interacts considerably with all orientations. In contrast, material type, number of perimeters, and infill rate had insignificant impacts on surface roughness parameters. Finally, the additive linear modelling approach was utilized and validated for proper predictions, making it helpful for surface engineering applications. [ABSTRACT FROM AUTHOR]

New developments in manufacturing processes impose the need for experimental studies concerning the determination of beneficial process-related parameter settings and optimization of objectives related to quality and efficiency. This work aims to improve cutting geometry, surface texture, and arithmetic surface roughness average in the case of post-processing of filament material extrusion 3D-printed acrylonitrile styrene acrylate (ASA) thin plates by a low-power CO2 laser cutting apparatus. This material was selected owing to its unique properties for thin-walled customized constructions. Three parameters, namely focal distance, plate thickness, and cutting speed, were examined with reference to the Box-Behnken design of experiments (BBD) and regression modeling. Four responses were considered: mean kerf width, Wm (mm); down width, Wd (mm); upper width, Wu (mm); and average surface roughness Ra (μm) of cut surfaces. Different regression models were tested for their efficiency in terms of predicting the objectives with an emphasis on full quadratic regression. The results showed that a focal distance of 6.5 mm and 16 mm/s speed optimizes all quality metrics for the three plate thicknesses. The regression models achieved adequate correlation among independent process-related parameters and optimization objectives, proving that they can be used to improve the laser cutting process and support practical applications. [ABSTRACT FROM AUTHOR]

The weldability of 3D printed (3DP), through material extrusion (MEX), of Polyamide 6 (PA6) plates joined with FSW is investigated. FSW has its challenges in polymers, especially for 3D printed parts, while it is used for various industrial applications in the automotive and airspace sector, in joints, and in other types of parts. Herein, a full factorial experimental course was deployed, to quantitatively document the impact of three critical process parameters (e.g., the rotational speed and the travel speed of the tool, as well as the pin geometry of the tool) and to optimize their levels. A set of identical PA6 prismatic workpieces was prepared and then welded. Throughout the welding process, the temperature profiles were monitored and logged, to ensure the solid state of the workpiece material. The welding efficiency of the joints was then determined through mechanical tests, while unwelded 3D printed specimens were employed as control samples. Thorough morphological evaluations and characterization with microscopy (Scanning Electron and optical Microscopy) were performed for the welding zones. The evaluation of the metrics with statistical modeling tools led to the quantitative correlation of the process parameters, as well as their interactions, and finally optimization. The feasibility of joining 3DP PA6 with FSW was verified, reaching a welding efficiency of up to 120.40% for threaded cylindrical pin profile, rotational speed 1200 rpm, and travel speed 3 mm/min. The results of the study provide valuable information and merit for the FSW of 3DP PA6, which can be exploited in various industrial applications. [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 work investigates the efficiency of the Box-Behnken design (BBD) in contrast with the full-factorial design (FFD) in ultimate tensile strength (UTS) of PA12 material extrusion 3D printing (ME-3DP) specimens. Three input parameters, i.e., the raster angle (A), layer thickness (B), and nozzle temperature (C) with three levels each, were employed to compare the BBD and FFD efficiency. The 81 full-factorial UTS initial experimental data used in this research have been produced in a previous work published by the authors. Fifteen (15) out of 81 experiments were selected for the BBD design with three repetitions on the central point (0,0,0). Main effect plots (MEP), interaction plots, surface plots, ANOVA analysis, normality plots, mean absolute percentage error (MAPE), and the root-mean-square error (RMSE) evaluate the BBD and FFD approaches. The BBD MAPE and RMSE indexes show that the Box-Behnken design is appropriate for parameter analysis and processing investigation resulting in a 5.3% MAPE and 2.75 RMSE, close to 5.2% and 2.44 of the full-factorial MAPE and RMSE indexes. [ABSTRACT FROM AUTHOR]

In this work, material extrusion (MEX) 3D-printed polylactic acid (PLA) thin workpieces were joined via the friction stir welding (FSW) process to evaluate the feasibility and the key features of the process. To ensure the reliability of the process, a special fixture was designed and manufactured. Three critical parameters were investigated, i.e., the welding tool geometry, the travel speed, and the tool rotational speed. Two different tool geometries were manufactured and tested. Specimens were welded with various welding parameters values, to calibrate the experimental ranges of the subsequent full factorial course. The results were recorded and evaluated with an optical microscope, a stereoscope, and scanning electron microscopy (SEM). The thermal field and the mechanical performance of the joints were measured and evaluated. In the majority of the welding scenarios, the welded specimens' mechanical performance was increased compared to the identical not welded 3D-printed samples. The travel speed proved to be the most critical parameter affecting the mechanical strength of the parts. The highest tensile strength is reported for a specimen welded with 6 mm/min travel speed, 1400 rpm rotational speed, and weld tool with the cylindrical pin. The results were analyzed and optimized with statistical modeling tools, to evaluate and document the impact of each parameter studied herein. Herewith, a cost-effective and efficient FSW joining process of MEX-made polymeric pieces enables a new possibility to permanently assemble 3D-printed parts of limited size to larger assemblies, with the aid of simple tools and a milling machine. [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]

This study investigates the effects of four variables during fused filament fabrication of organic biocompatible composite material, PLA with coconut flour, at the ultimate tensile strength (UTS), and elasticity module (E) of the printed parts. The parameter optimization uses Taguchi L18 design and regression models. The examined deposition variables are the layer thickness, the nozzle temperature, the raster deposition angle, and filament printing speed. The effects of the above variables on the strength of the parts are essential to enhance the mechanical response of the printed parts. The experimental outcomes are investigated using the ANOM and ANOVA and modeled utilizing linear regression models. In addition, an independent experiment was repeated three times at optimum parameters' levels to evaluate the methodology, giving predictions errors less than 3%. The observed results showed that the raster deposition angle dominates among the other variables in the studied experimental area. [ABSTRACT FROM AUTHOR]

3D printing (3DP) of polymers using fused filament fabrication (FFF) is not yet massively adopted in industrial environments due to shape accuracy and surface integrity limitations. However, the FFF process combined with laser processing (hybrid manufacturing) or other precision machining processes is under investigation nowadays and is a promising combination for high quality 3DP final parts. This work investigates the effects on the dimensional accuracy and the surface roughness of process parameters, during low power CO2 laser cutting (LC) of thin acrylonitrile butadiene styrene (ABS) plates manufactured with the FFF process. Even though LC is highly flexible and the quality of the machined surfaces is versatile, certain problems are associated with LC, such as the kerf angle formation and the surface texture quality, which need optimization in respect to the LC parameters. The full factorial design methodology is adopted, having four parameters as input, i.e., stand-off distance (SoD), raster deposition angle (RDA), laser speed (LS), and laser power (LP), and kerf geometry characteristics as output, i.e., kerf widths (upper, middle, and down; Wu, Wm, and Wd), kerf angle (KA), and average and max height surface roughness (Ra and Rt). The statistical analysis is used to investigate the relations between the input and the output parameters. Seventy-two independent experiments were utilized. The analysis of variances (ANOVA), the interaction study, and the quadratic regression models (QRM) were adapted to fit the input with the output parameters, optimize the process KA close to 0°, and minimize the Ra close to 0 μm. The optimum parameter values (7.5 mm SoD, zero RDA, 14.4 mm/s LS, and 105 W LP) are validated by seven independent experiments, which show that only the Wu and Ra predicted values are close to the actual values following the ANOVA analysis and the mean average percengtage error (MAPE) index. [ABSTRACT FROM AUTHOR]