Prediction of interlayer strength in material extrusion additive manufacturing
[Display omitted] •In-line rheometer and melt pressure transducers were used to understand the Material Extrusion process.•Melt pressure and contact pressures correlate with interlayer contact.•Relaxation times and transient heat analysis enable prediction of interlayer diffusion.•In-line sensors pl...
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Veröffentlicht in: | Additive manufacturing 2020-10, Vol.35, p.101368, Article 101368 |
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Format: | Artikel |
Sprache: | eng |
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•In-line rheometer and melt pressure transducers were used to understand the Material Extrusion process.•Melt pressure and contact pressures correlate with interlayer contact.•Relaxation times and transient heat analysis enable prediction of interlayer diffusion.•In-line sensors plus contact and diffusion equations provide real-time calculation and monitoring of interlayer strength.
The interlayer strengths of parts produced through material extrusion (also referred to as fused filament fabrication or FFF) suffer due to poor interlayer contact and insufficient diffusion. A model for predicting interlayer contact, based on pressure-driven flow, is combined with a model for polymer chain diffusion to predict the interlayer strength (aka, bond strength) of material extrusion parts. Interlayer contact is predicted based on in-line pressure measurements while diffusion is predicted based on in-line temperature and viscosity measurements, demonstrating that a combination of the appropriate in-line sensors and models can be used for real-time monitoring and process control. The interlayer strength model is successfully validated against strength measurements of parts made with high impact polystyrene, indicating that the strength of all parts suffers due to incomplete interlayer contact while only some parts suffered from incomplete diffusive healing. The melt pressure and in-line rheological measurements have proven extremely valuable for understanding the material extrusion process, optimizing quality, and monitoring consistency. Practical insights from the model are provided about how to select appropriate materials and processing conditions, and it concludes with a demonstration of using the in-line sensors and real-time modeling for defect detection. |
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ISSN: | 2214-8604 2214-7810 |
DOI: | 10.1016/j.addma.2020.101368 |