Mechanical strength and shape accuracy optimization of polyamide FFF parts using grey relational analysis

Zohreh Shakeri; Khaled Benfriha; Nader Zirak; Mohammadali Shirinbayan

doi:10.1038/s41598-022-17302-z

Mechanical strength and shape accuracy optimization of polyamide FFF parts using grey relational analysis

Sci Rep. 2022 Jul 30;12(1):13142. doi: 10.1038/s41598-022-17302-z.

Authors

Zohreh Shakeri¹, Khaled Benfriha², Nader Zirak³, Mohammadali Shirinbayan³

Affiliations

¹ Laboratoire Conception de Produits et Innovation (LCPI), HESAM University, 75013, Paris, France. zohreh.shakeri@ensam.eu.
² Laboratoire Conception de Produits et Innovation (LCPI), HESAM University, 75013, Paris, France.
³ Procédés Et Ingénierie en Mécanique et Matériaux (PIMM), HESAM University, 75013, Paris, France.

Abstract

This paper investigates the effect of different additive manufacturing process parameters such as chamber temperature, Printing temperature, layer thickness, and print speed on five essential parameters that characterize the manufactured components: cylindricity, circularity, strength, and Young's modulus, and deformation by Gray Relational Analysis method simultaneously. Taguchi method was used to design the experiments, and the PA6 cylindrical parts were fabricated using a German RepRap X500® 3D printer. Then the Gray Relational Grade (GRG) values were calculated for all experiments. In the 8th trial, the highest value of GRG was observed. Then, to discover the optimal parameters, the GRG data were analyzed using ANOVA and S/N analysis, and it was determined that the best conditions for enhancing GRG are 60 °C in the chamber temperature, 270 °C in the printing temperature, 0.1 mm layer thickness, and 600 mm/min print speed. Finally, by using optimal parameters, a verification test was performed, and new components were investigated. Finally, comparing the initial GRG with the GRG of the experiments showed an improvement in the gray relational grade (14%) which is accompanying with improving of GRG value.