Multi-parameter optimization of PLA/Coconut wood compound for Fused Filament Fabrication using Robust Design

Kechagias, John; Zaoutsos, Stefanos; Chaidas, Dimitrios; Vidakis, Nectarios

doi:10.1007/s00170-022-08679-2

Cited by 48 publications

(18 citation statements)

References 57 publications

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“…On the other hand, the raster deposition angle decrease or the nozzle temperature increase increases the σ b of the investigated material and experimental region. These results are similar to the literature for other composite materials, e.g., PLA wood composites [ 11 ].…”

Section: Discussionsupporting

confidence: 91%

“…The RDA and NT have also been considered since they have been proved important in the literature for the mechanical strength of the FFF 3D printed parts [ 11 ]. So, various parameter values were studied in the work.…”

Section: Methodsmentioning

confidence: 99%

“…For ‘line’ type infill structures, the zero RDA increases the UTS, but the variability is different for each working filament. Also, for the nozzle temperature, the effects depend on the composite matrix material and filler content wt% [ 11 ]. Therefore, twenty-seven (27) combinations of the three independent parameters with three discrete values each (levels; see Table 1 ) were tested three (3) times each, resulting in eighty-one experiments (Table 2 ).…”

Section: Methodsmentioning

confidence: 99%

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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: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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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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“…As a result, scientists are continually making extreme efforts to use municipal, agricultural and forest industry waste in useful applications, which facilitates not only reducing environmental pollution but also lowering manufacturing costs [6][7][8][9]. One of the impending applications is polymer biocomposites, where incorporating agricultural or forest industry waste has proven to be quite beneficial from the techno-commercial and ecological point of view [10][11][12][13]. As a result, in recent years, the use of agricultural and forest industry waste in the manufacturing of biocomposites has gained popularity and has become a regular practice [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Wood/Rice Husk-Waste-Filled PLA Biocomposites Using Integrated CRITIC–MABAC-Based Decision-Making Algorithm

et al. 2022

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Based on the criteria importance through inter-criteria correlation (CRITIC) and the multi-attributive border approximation area comparison (MABAC), a decision-making algorithm was developed to select the optimal biocomposite material according to several conflicting attributes. Poly(lactic acid) (PLA)-based binary biocomposites containing wood waste and ternary biocomposites containing wood waste/rice husk with an overall additive content of 0, 2.5, 5, 7.5 and 10 wt.% were manufactured and evaluated for physicomechanical and wear properties. For the algorithm, the following performance attributes were considered through testing: the evaluated physical (density, water absorption), mechanical (tensile, flexural, compressive and impact) and sliding wear properties. The water absorption and strength properties were found to be the highest for unfilled PLA, while modulus performance remained the highest for 10 wt.% rice husk/wood-waste-added PLA biocomposites. The density of PLA biocomposites increased as rice husk increased, while it decreased as wood waste increased. The lowest and highest density values were recorded for 10 wt.% wood waste and rice husk/wood-waste-containing PLA biocomposites, respectively. The lowest wear was exhibited by the 5 wt.% rice husk/wood-waste-loaded PLA biocomposite. The experimental results were composition dependent and devoid of any discernible trend. Consequently, prioritizing the performance of PLA biocomposites to choose the best one among a collection of alternatives became challenging. Therefore, a decision-making algorithm, called CRITIC–MABAC, was used to select the optimal composition. The importance of attributes was determined by assigning weight using the CRITIC method, while the MABAC method was employed to assess the complete ranking of the biocomposites. The results achieved from the hybrid CRITIC–MABAC approach demonstrated that the 7.5 wt.% wood-waste-added PLA biocomposite exhibited the optimal physicomechanical and wear properties.

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“…There are many recent works regarding laser cutting of thin thermoplastic materials, but very few concerning 3D printed materials [7][8][9][10]. However, the 3D printed thin plates of organic, eco-friendly composite materials combined with laser processing could be an alternative for low-cost tailored products and assemblies [22][23][24]. Furthermore, the systematic investigation of the influence of the deposition angle during laser cutting of PLA with wood flour filler (PLA/WF) plates is investigated for the first time.…”

mentioning

confidence: 99%

Neural networks for predicting kerf characteristics of CO₂ laser-machined FFF PLA/WF plates

et al. 2022

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The current work is a follow-up of previous research published by the authors and investigates the effect of CO2 laser cutting with variable cutting parameters of thin 3D printed wood flour mixed with poly-lactic-acid (PLA/WF) plates on kerf angle (KA) and mean surface roughness (Ra). The full factorial experiments previously conducted, followed a custom response surface methodology (RSM) to formulate a continuous search domain for statistical analysis. Cutting direction, standoff distance, travel speed and beam power were the independent process parameters with mixed levels, resulting to a set of 24 experiments. The 24 experiments were repeated three times giving a total of 72 experimental tryouts. The results analyzed using analysis of variance (ANOVA) and regression, to study the synergy and effect of the parameters on the responses. Thereby, several neural network topologies were tested to achieve the best results and find a suitable neural network to correlate inputs and outputs, thus; contributing to related academic research and actual industrial applications.

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Multi-parameter optimization of PLA/Coconut wood compound for Fused Filament Fabrication using Robust Design

Cited by 48 publications

References 57 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

Optimal Design of Wood/Rice Husk-Waste-Filled PLA Biocomposites Using Integrated CRITIC–MABAC-Based Decision-Making Algorithm

Neural networks for predicting kerf characteristics of CO₂ laser-machined FFF PLA/WF plates

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Multi-parameter optimization of PLA/Coconut wood compound for Fused Filament Fabrication using Robust Design

Cited by 48 publications

References 57 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

Optimal Design of Wood/Rice Husk-Waste-Filled PLA Biocomposites Using Integrated CRITIC–MABAC-Based Decision-Making Algorithm

Neural networks for predicting kerf characteristics of CO2 laser-machined FFF PLA/WF plates

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Neural networks for predicting kerf characteristics of CO₂ laser-machined FFF PLA/WF plates