Process optimization of material extrusion additive manufacturing with ASA: robust design and predictive models for engineering response metrics.
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| Title: | Process optimization of material extrusion additive manufacturing with ASA: robust design and predictive models for engineering response metrics. |
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| Authors: | Petousis, Markos1 (AUTHOR) markospetousis@hmu.gr, Mountakis, Nikolaos1 (AUTHOR) mountakis@hmu.gr, Spyridaki, Maria1 (AUTHOR) mspyridaki@hmu.gr, Gkagkanatsiou, Katerina1 (AUTHOR), Valsamos, Ioannis1 (AUTHOR) valsamos@hmu.gr, Moutsopoulou, Amalia1 (AUTHOR) amalia@hmu.gr, Stratakis, Emmanuel2,3 (AUTHOR), Vidakis, Nectarios1 (AUTHOR) vidakis@hmu.gr |
| Source: | International Journal of Advanced Manufacturing Technology. Jun2025, Vol. 138 Issue 9, p4431-4453. 23p. |
| Subjects: | Elastic modulus, Flexural modulus, Tensile strength, Engineering models, Flexural strength |
| Abstract: | Acrylonitrile styrene acrylate (ASA) is increasingly popular among traditional engineering polymers for material extrusion (MEX) additive manufacturing (AM). Satisfactory printability and engineering metrics and excellent stability in hard environmental conditions explain this status. To achieve the highest possible performance levels, the optimization of the generic printing control settings is critical. This paper focuses on delivering enhanced mechanical and morphological characteristics when printing with ASA. To meet these goals, an L25 orthogonal robust design was compiled and executed, enabling the simultaneous assessment of six (6) control parameters, i.e., the strand width (SW), infill orientation (angle) (AINF), layer thickness (TL), printing speed (PS), nozzle temperature (TN), and bed temperature (TB). A massive experimental course with two hundred and ninety specimens was engaged to deliver response metrics, such as tensile ultimate strength ( σ B T ), tensile yield strength ( σ B Y ), tensile modulus of elasticity ( E T ), flexural strength ( σ B F ), and flexural modulus of elasticity ( E F ). Three modeling methods were applied to compare their efficacy in these loading scenarios: the Reduced Quadratic Regression Model (RQRM), Linear Regression Model (LRM), and Quadratic Regression Model (QRM). RQRM and QRM achieved similar prediction accuracies, whereas LRM yielded poorer predictive results than the other methods. Confirmation runs validated the validity of the compiled prediction functions, thereby making them suitable for direct industrial use. The most influential parameter was the AINF. The optimized 3D printing settings improved all mechanical performance metrics by more than 40%, while the flexural strength was improved by more than 60%. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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