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The main scope of this work is to exposit good practices of incorporating the power of experimental design in stochastic problems. The filament material extrusion (f-ME) 3D printing process and the acrylonitrile butadiene styrene (ABS) filament were adopted for this scope owing to many processing parameters affecting 3D printed parts quality performance and specifically on flexural strength. Therefore, infill density, raster orientation, printing speed, layer thickness, and nozzle and bed temperatures are optimized using Taguchi orthogonal array and general linear analysis of variances (ANOVA). Linear ANOVA analysis was explained in detail for the first and second runs separately, and all experiment runs, and the residuals were examined for their normality distribution. The contribution of the factors turned out to be similar in each case, showing that the one-run Taguchi design can be safely used at half the cost and time compared to the two-repetition case. [ABSTRACT FROM AUTHOR]

The properties of fused deposition modeling (FDM) products exhibit strong dependence on process parameters which may be improved by setting suitable levels for parameters related to FDM. Anisotropic and brittle nature of 3D-printed components makes it essential to investigate the effect of FDM control parameters to different performance metrics related to resistance for improving strength of functional parts. In this work the flexural strength of polyethylene terephthalate glycol (PET-G) is examined under by altering the levels of different 3D-printing parameters such as layer height, infill density, deposition angle, printing speed and printing temperature. A response surface experiment was established having 27 experimental runs to obtain the results for flexural strength (MPa) and to further investigate the effect of each control parameter on the response by studying the results using statistical analysis. The experiments were conducted as per the ASTM D790 standard. The regression model generated for flexural strength adequately explains the variation of FDM control parameters on flexural strength and thus, it can be implemented to find optimal parameter settings with the use of either an intelligent algorithm, or neural network.

In this study, the dimensional accuracy of parts fabricated with PolyJet 3D Printing Direct process is investigated. An L4 orthogonal array was utilized as the design of experiments, while the process parameters examined are layer thickness, build style and scale. A simple prototype was proposed and specified external and internal dimensions were measured using a digital vernier calliper. Grey-Taguchi method was applied for optimizing all dimensional measurements. The effect of each parameter on dimensional accuracy has been identified using ANOM (Analysis of Means), while ANOVA (Analysis of Variances) has been performed to determine each parameter’s dominance. Additionally, the results of this study were compared with the findings of a previous optimization study in which the usual Taguchi method was used. It was concluded that 16 μm of layer thickness, glossy style and 50% scale provide the optimum dimensional results, while scale is the most important factor.

This investigation concerns the cutting force magnitude during turning of AISI D6 tool steel using both Taguchi Experimental Design (TED) and full factorial design (FFD). Three main cutting parameters namely spindle speed, feed rate and depth of cut were considered as the cutting parameters, each one having three levels, while the cutting force (Fc) was selected as the machinability process output. The full factorial design of the 27 (33) experiments was splitted in three sub-arrays, each one having 9 experiments. These three sub-arrays were orthogonal and were treated as Taguchi L9 orthogonal arrays. The performance of the FFD and each TED was analysed using stem-and-leaf plots, box plots, as well as analysis of means (ANOM) and analysis of variance (ANOVA). The results obtained indicate the suitability of all proposed experimental designs for machinability studies.

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 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]