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Mathematical Modeling and Optimization of Fused Filament Fabrication (FFF) Process Parameters for Shape Deviation Control of Polyamide 6 Using Taguchi Method

Article dans une revue avec comité de lecture
Author
SHAKERI, Zohreh
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
BENFRIHA, Khaled
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
SHIRINBAYAN, Mohammadali
127758 Laboratoire Conception de Produits et Innovation [LCPI]
AHMADIFAR, Mohammad
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
127758 Laboratoire Conception de Produits et Innovation [LCPI]
TCHARKHTCHI, Abbas
127758 Laboratoire Conception de Produits et Innovation [LCPI]

URI
http://hdl.handle.net/10985/21221
DOI
10.3390/polym13213697
Date
2021
Journal
Polymers

Abstract

Fused filament fabrication (FFF) is a layer-by-layer additive manufacturing (AM) process for producing parts. For industries to gain a competitive advantage, reducing product development cycle time is a basic goal. As a result, industries’ attention has turned away from traditional product development processes toward rapid prototyping techniques. Because different process parameters employed in this method significantly impact the quality of FFF manufactured parts, it is essential to optimize FFF process parameters to enhance component quality. The paper presents optimization of fused filament fabrication process parameters to improve the shape deviation such as cylindricity and circularity of 3D printed parts with the Taguchi optimization method. The effect of thickness, infill pattern, number of walls, and layer height was investigated as variable parameters for experiments on cylindricity and circularity. The MarkForged® used Nylon White (PA6) to create the parts. ANOVA and the S/N ratio are also used to evaluate and optimize the influence of chosen factors. As a result, it was concluded that the hexagonal infill pattern, the thickness of 5 mm, wall layer of 2, and a layer height of 1.125 mm were known to be the optimal process parameters for circularity and cylindricity in experiments. Then a linear regression model was created to observe the relationship between the control variables with cylindricity and circularity. The results were confirmed by a confirmation test.

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