Critical quality indicators of high-performance polyetherimide (ULTEM) over the MEX 3D printing key generic control parameters: Prospects for personalized equipment in the defense industry
Additive Manufacturing (AM) can provide customized parts that conventional techniques fail to deliver. One important parameter in AM is the quality of the parts, as a result of the material extrusion 3D printing (3D-P) procedure. This can be very important in defense-related applications, where opti...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-01-01
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| Series: | Defence Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914724001855 |
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| Summary: | Additive Manufacturing (AM) can provide customized parts that conventional techniques fail to deliver. One important parameter in AM is the quality of the parts, as a result of the material extrusion 3D printing (3D-P) procedure. This can be very important in defense-related applications, where optimum performance needs to be guaranteed. The quality of the Polyetherimide 3D-P specimens was examined by considering six control parameters, namely, infill percentage, layer height, deposition angle, travel speed, nozzle, and bed temperature. The quality indicators were the root mean square (Rq) and average (Ra) roughness, porosity, and the actual to nominal dimensional deviation. The examination was performed with optical profilometry, optical microscopy, and micro-computed tomography scanning. The Taguchi design of experiments was applied, with twenty-five runs, five levels for each control parameter, on five replicas. Two additional confirmation runs were conducted, to ensure reliability. Prediction equations were constructed to express the quality indicators in terms of the control parameters. Three modeling approaches were applied to the experimental data, to compare their efficiency, i.e., Linear Regression Model (LRM), Reduced Quadratic Regression Model, and Quadratic Regression Model (QRM). QRM was the most accurate one, still the differences were not high even considering the simpler LRM model. |
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| ISSN: | 2214-9147 |