The Influence of Fused Deposition Modeling Parameters on the Properties of PA6/PA66 Composite Specimens by the Taguchi Method and Analysis of Variance

3D PRINTING AND ADDITIVE MANUFACTURING(2023)

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摘要
Fused deposition modeling (FDM) is widely used in the rapid prototyping of polymers. Polyamide (PA) has excellent mechanical properties, but its application in FDM is limited due to its high water absorption, warpage, and forming shrinkage. The material of the filament and the printing parameters of the printer are two critical aspects that affect the performance of a component. The prepared PA6/PA66 (composite polyamide [COPA], PA6:PA66 = 85:15) composite (COPA: acrylonitrile butadiene styrene [ABS]: maleic anhydride grafted acrylonitrile butadiene styrene [ABS-g-MAH]: polyethylene = 800:133:67:100) has low water absorption (0.39%) and high dimensional stability, which has a good application prospect in FDM. The influence of eight FDM parameters, including three rarely reported, on the properties of PA6/PA66 composite specimens was investigated by the Taguchi method. The significance of influencing factors was evaluated by analysis of variance (ANOVA) and the stability by signal-noise ratio. When the layer thickness was 0.15 mm, the infill pattern was zigzags, the build plate adhesion type was brim, and the distance from the nozzle to the printing platform and the layer thickness (Delta L) was 0.05 mm; the specimens' dimensional accuracy, surface quality, and mechanical properties were better than other levels. The layer thickness and infill pattern were the two most important factors. The switch of the cooling fan and the temperature printing platform played a significant role in the specimens' dimensional accuracy and surface quality. Delta L tremendously influenced the thickness and warping degree of the specimens. The preparation of high-performance PA composites and the investigation of multiparameters by the Taguchi method provide a possible solution for applying polyamide in FDM.
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关键词
PA6,PA66 composite,fused deposition modeling,Taguchi method,parameter optimization,ANOVA
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