Key parameters controlling surface quality and dimensional accuracy: a critical review of FFF process

Kechagias, John; Chaidas, Dimitrios; Vidakis, Nectarios; Salonitis, Konstantinos; Vaxevanidis, Nikolaos M.

doi:10.1080/10426914.2022.2032144

Cited by 103 publications

(59 citation statements)

References 224 publications

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“…Previous works in the literature [ 73 ] inspired the analysis of experimental data. The three best values reported of the six experiments conducted were considered in the modeling process.…”

Section: Resultsmentioning

confidence: 99%

Mechanical response assessment of antibacterial PA12/TiO2 3D printed parts: parameters optimization through artificial neural networks modeling

Vidakis

Petousis

Mountakis

et al. 2022

Int J Adv Manuf Technol

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This study investigates the mechanical response of antibacterial PA12/TiO 2 nanocomposite 3D printed specimens by varying the TiO 2 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/TiO 2 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 TiO 2 % 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 TiO 2 % 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.

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Section: Resultsmentioning

confidence: 99%

Mechanical response assessment of antibacterial PA12/TiO2 3D printed parts: parameters optimization through artificial neural networks modeling

Vidakis

Petousis

Mountakis

et al. 2022

Int J Adv Manuf Technol

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“…MEX 3D printing is well known for its flexibility to control several building parameters, such as layer height, strands' width, infill density, raster pattern, etc., which all imply high inhomogeneity and anisotropy to the outcome, in a stochastic manner. On the other hand, the porosity (even for 100% infill density), the surface roughness, and the dimensional inaccuracy are unavoidable, especially when compared with other fabrication processes [28]. Therefore, the adoption of the aforementioned analytical and/or numerical thermo-mechanical models will not produce reliable or somehow acceptable results.…”

Section: Methodsmentioning

confidence: 99%

“…Statistical modeling tools have been introduced for the analysis and optimization of material extrusion (MEX) 3D printing parameters for materials, such as Polyamides [26] and Thermoplastic Polyurethane (TPU) [27]. Additionally, such modeling tools have been used for the optimization of the surface quality of 3D printed parts of different materials [28], and in hybrid additive manufacturing (HAM), for laser cutting of polymers 3D printed with MEX, such as polylactic acid (PLA) [29], and polyethylene terephthalate glycol (PETG) [30]. These modeling tools revealed the critical parameters in each case, leading to useful conclusions for the improvement of the results of these processes.…”

Section: Introductionmentioning

confidence: 99%

Friction Stir Welding Optimization of 3D-Printed Acrylonitrile Butadiene Styrene in Hybrid Additive Manufacturing

et al. 2022

Self Cite

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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.

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“…However, the anisotropy and mechanical shortage handicap extensive engineering applications, especially in structures enduring the key loads. Moreover, higher printing temperatures, suitable printing speed, and thinner printing thickness are important factors for the quality performance of the 3D printed models in this paper [ 39 , 40 ]. The bionic helicoidal structures might provide the potential solution to enhance the FFF product load-carrying capacity.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Low-Velocity Impact Resistance of Helicoidal Composites by Fused Filament Fabrication (FFF)

Zhang

et al. 2022

Polymers

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Bioinspired composites, capable of tailoring mechanical properties by the strategy of making full use of their advantages and bypassing their drawbacks, are vital for numerous engineering applications such as lightweight ultrahigh-strength, enhanced toughness, improved low-/high- velocity impact resistance, wave filtering, and energy harvesting. Helicoidal composites are examples of them. However, how to optimize the geometric structure to maximize the low-velocity impact resistance of helicoidal composites has been ignored, which is vital to the lightweight and high strength for aerospace, defense, ship, bridge, dam, vessel, and textile industries. Here, we combined experiments and numerical simulations to report the dynamic response of helicoidal composites subjected under low-velocity impact (0–10 m/s). Our helicoidal structures, inspired by the Stomatopod Dactyl club, are fabricated using polylactic acid (PLA) by FFF in a single-phase way. The helicoidal strategy aims to exploit, to a maximum extent, the axial tensile strength of filaments and simultaneously make up the shortage of inter-filament contact strength. We demonstrate experimentally that the low-velocity impact resistance has been enhanced efficiently as the helicoidal angle varies, and that the 15° helicoidal plate is better than others, which has also been confirmed by the numerical simulations. The findings reported here provide a new routine to design composites systems with enhanced impact resistance, offering a method to improve impact performance and expand the application of 3D printing.

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Key parameters controlling surface quality and dimensional accuracy: a critical review of FFF process

Cited by 103 publications

References 224 publications

Mechanical response assessment of antibacterial PA12/TiO2 3D printed parts: parameters optimization through artificial neural networks modeling

Mechanical response assessment of antibacterial PA12/TiO2 3D printed parts: parameters optimization through artificial neural networks modeling

Friction Stir Welding Optimization of 3D-Printed Acrylonitrile Butadiene Styrene in Hybrid Additive Manufacturing

Enhanced Low-Velocity Impact Resistance of Helicoidal Composites by Fused Filament Fabrication (FFF)

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