4.5 Article

Optimization of fused filament fabrication process parameters for mechanical responses of weather-resistant polymer (acrylonitrile styrene acrylate)

Journal

POLYMER ENGINEERING AND SCIENCE
Volume 63, Issue 1, Pages 139-153

Publisher

WILEY
DOI: 10.1002/pen.26192

Keywords

additive manufacturing; design of experiments (DOE); FFF; optimization; Taguchi; weather resistant polymer (ASA)

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This research investigates the application of additively manufactured polymeric products in outdoor environments, as well as the study of a novel weather-resistant polymer ASA. By optimizing process parameters, the strength of ASA specimens was enhanced, and a regression model was developed to explore the correlation between process parameters and strength.
Additively manufactured polymeric products for automotive, aerospace, and biomedical applications are usually intended for service in an outdoor environment with high mechanical loading conditions. The strength and sustainability of the products can be significantly degraded due to the outdoor environmental conditions such as UV light, moisture, heat, and so forth. In this research work, a novel weather-resistant polymer (WRP) material, that is, acrylonitrile styrene acrylate (ASA), has been studied. Furthermore, this work aims to study the effect of process parameters and enhance the strength of WRP (ASA) specimens using the FFF process. The optimized process parameters, that is, build orientation (BO), extrusion temperature (ET), layer thickness (LT), and printing speed (PS), were identified based on the tensile and flexural strength using the Taguchi technique and statistical analysis. The best tensile and flexural strengths for the specimen were achieved at both orientations (XYZ and ZXY) TS: 255 degrees C ET, 0.14 mm LT, 50 mm/s PS; and FS: 245 degrees C ET, 0.28 mm LT, 50 mm/s PS, respectively. Regression model was developed to investigate the correlation between the process parameters with tensile and flexural strength. A validation test confirmed the findings, and the error between the actual and predicted values is less than +/- 10%.

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