Gum Arabic as a Binder for Paste Extrusion Additive Manufacturing Using Rice Husks
Résumé
This study evaluates Gum Arabic (GA) powder as a biodegradable binder for rice-husk-based composites fabricated using Material Extrusion with Chemical Reaction Bonding (MEX-CRB). The MEX-CRB technique deposits visco-plastic or bio-based pastes in sequential layers through a nozzle, directed by G-codes derived from STL files. This method enables the processing of materials beyond traditional polymer-based systems and applies to ceramics, biomedical scaffolds, construction, and sustainable manufacturing. MEX-CRB still primarily employs synthetic binders, raising sustainability concerns; therefore, this study investigates GA as an environmentally responsible alternative. The effects of particle size, binder ratio, flowability, rheological properties, drying conditions, and printing parameters on printability and mechanical performance were systematically examined. GA showed a higher tap density (0.87 ± 0.01 g/cm³) than rice husks (0.50 ± 0.25 g/cm³), indicating a more compact structure. Printing tests indicated that pastes with particle sizes of 90–125 µm achieved optimal extrusion, characterized by continuous filament deposition, minimal nozzle clogging, and stable geometry at 60 mm³/s. Extrusion energy decreased as binder content increased, reflecting improved material flow. These properties enabled smooth extrusion and effective shape retention following deposition. Printing experiments confirmed that both GA and rice husk particles within the 0 < d < 250 µm range were printable, with the 125 µm < d < 250 µm fraction yielding the most consistent results at flow rates of 40 –72 mm³/s and drying at 45–55 °C for 24 h. Printing experiments confirmed that both GA and rice husk particles within the 0 < d < 250 µm range were printable using a 4 mm diameter Nozzle, with the 125 µm < d < 250 µm exhibited the highest flexural (8.29 ± 0.96 MPa) and compressive strengths (5.05 ± 1.56 MPa) due to effective load transfer and reduced binder demand.
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