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Three-dimensional printing (3DP) of functional materials is increasingly important for advanced applications requiring objects with complex or custom geometries or prints with gradients or zones with different properties. A common 3DP technique is direct ink writing (DIW), in which printable inks are comprised of a fluid matrix filled with solid particles, the latter of which can serve a dual purpose of rheology modifiers to enable extrusion and functional fillers for performance-related properties. Although the relationship between filler loading and viscosity has been described for many polymeric systems, a thorough description of the rheological properties of three-dimensional (3D) printable composites is needed to expedite the creation of new materials. In this manuscript, the relationship between filler loading and printability is studied using model paraffin/photopolymer composite inks containing between 0 and 73 vol. % paraffin microbeads. The liquid photopolymer resin is a Newtonian fluid, and incorporating paraffin microbeads increases the ink viscosity and imparts shear-thinning behavior, viscoelasticity, and thixotropy, as established by parallel plate rheometry experiments. Using Einstein and Batchelor's work on colloidal suspension rheology, models were developed to describe the thixotropic behavior of inks, having good agreement with experimental results. Each of these properties contributes to the printability of highly filled ([Formula: see text]43 vol. % paraffin) paraffin/photopolymer composite inks. Through this work, the ability to quantify the ideal rheological properties of a DIW ink and to selectively control and predict its rheological performance will facilitate the development of 3D printed materials with tunable functionalities, thus, advancing 3DP technology beyond current capabilities.
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Toward 3D printability prediction for thermoplastic polymer nanocomposites: Insights from extrusion printing of PLA-based systems
The development of new thermoplastic-based nanocomposites for, as well as using, 3D printing requires extensive experimental testing. One typically goes through many failed, or otherwise sub-optimal, iterations before finding acceptable solutions (e.g. compositions, 3D printing parameters). It is desirable to reduce the number of such iterations as well as exclude failed experiments that often require laborious disassembly and cleaning of the 3D printer. This issue could be addressed if we were able to understand, and ultimately predict ahead of experiments if a given material can be 3D printed successfully. Herein, we report on our investigations into forecasting the printing and resultant properties of polymer nanocomposites while encompassing both material properties and printing parameters, enabling the model to generalize across various thermoplastics and additives. To do so, nanocomposites of two different commercially available bio-based PLAs with varying concentrations of nanoclay (NC) and graphene nanoplatelets (GNP) were prepared using a twin-screw extruder. The thermal and rheological properties of the nanocomposites were analyzed. These materials were printed at varying temperature and flow using a pellet printer. The quality of the printing was evaluated by measuring weight fluctuation, internal diameter of cylindrical specimen, and surface uniformity. The interactions between material properties and printing parameters are complex but captured effectively by a machine learning model, specifically we demonstrate such a predictive model to forecast printability and, printing quality utilizing a Random Forest algorithm. Printability was predicted by developing a classification model with constraints based on the weight fluctuation (W ) of the printed sample w.r.t. the optimal print; defining ‘‘not printable’’ for −1.0 d W < −0.8 and ‘‘printable’’ for W e −0.8. The classification model for predicting printability, performed well with an accuracy of 92.8% and identified flow index and complex viscosity, contributing 52% to the model’s importance. Another model to predict W of the only on successful prints also showed strong performance, emphasizing the importance of viscoelastic properties, thermal stability, and printing temperature. For diameter change (Di), the Random Forest model identified flow consistency index, complex viscosity, and thermal stability as influential parameters, with crystallization enthalpy gaining increased importance, reflecting its role in crystallization and shrinkage. In contrast, the surface roughness average (RA) model had lower performance, yet revealed remarkable insights regarding the feature importance with crystallization enthalpy and complex viscosity being most significant.
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- Award ID(s):
- 2153316
- PAR ID:
- 10556413
- Publisher / Repository:
- elsevier
- Date Published:
- Journal Name:
- Additive Manufacturing
- Volume:
- 95
- Issue:
- C
- ISSN:
- 2214-8604
- Page Range / eLocation ID:
- 104533
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.addma.2024.104533
