This paper presents the results of research on the technical-economic optimization of FDM parameters (L-layer height and I-infill density percentage) for the manufacture of tensile and compression samples from recycled materials (r) of PETG (polyethylene terephthalate glycol) and ASA (acrylonitrile styrene acrylate) in the context of the transition to a circular economy. To carry out our technical-economic study, the fundamental principle of value analysis was used, which consists of maximizing the ratio between and , where represents the mechanical characteristic (tensile strength or compressive strength) and represents the production cost. The results of this study showed that, in the case of tensile samples manufactured by recycled PETG (rPETG), the parameter that significantly influences the results of the ratios is L (the height of the layer), while for the samples manufactured additively from recycled ASA (rASA), the parameter that decisively influences the tensile strength is I (the infill density percentage). In the case of compression samples manufactured by FDM from recycled PETG (rPETG) and recycled ASA (rASA), the parameter that signified influences the results of the ratios is I (the infill density percentage). Following the optimization of the FDM parameters, using multiple-response optimization, we identified the optimal parameters for the manufacture of parts by FDM from rPETG and rASA: L = 0.20 mm and I = 100%. The results of this study demonstrated that the use of recycled plastics from PETG and ASA lends itself to a production and consumption model based on a circular economy.
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http://dx.doi.org/10.3390/polym17010122 | DOI Listing |
Polymers (Basel)
January 2025
Department of Mechanical Engineering, Doctoral School, Petroleum-Gas University of Ploiesti, 100680 Ploiesti, Romania.
Polymers (Basel)
January 2025
Department of Statistics and Operations Research, Barcelona School of Industrial Engineering (ETSEIB), Universitat Politècnica de Catalunya, Av. Diagonal, 647, 08028 Barcelona, Spain.
Artificial neural network (ANN) models have been used in the past to model surface roughness in manufacturing processes. Specifically, different parameters influence surface roughness in fused filament fabrication (FFF) processes. In addition, the characteristics of the networks have a direct impact on the performance of the models.
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December 2024
CESTER-Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
This study explores the experimental and theoretical optimization of process parameters to improve the quality of 3D-printed parts produced using the Fused Deposition Modeling technique. To ensure the cost-effective production of high-quality components, advancements in printing strategies are essential. This research identifies optimal 3D printing strategies to enhance the quality of finished products.
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December 2024
Department of Mechanical Engineering, University of Nevada, Las Vegas, NV 89154, USA.
Polymers (Basel)
December 2024
Metallurgical and Materials Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey.
The introduction of 3D printing technology has broadened manufacturing possibilities, allowing the production of complex cellular geometries, including auxetic and curved plane structures, beyond the standard honeycomb patterns in sandwich composite materials. In this study, the effects of cell design parameters, such as cell geometry (honeycomb and auxetic) and cell size (cell thickness and width), are examined on acrylonitrile butadiene styrene (ABS) core materials produced using fusion deposition modeling (FDM). They are produced as a result of the epoxy bonding of carbon epoxy prepreg composite materials to the surfaces of core materials.
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