Influence of Particle Dimensions in Liquid Deposition Modeling 3D Printing with Wood-based Materials

Abstract

Additive manufacturing (AM) using wood-based materials offers new bioeconomic possibilities for sustainable product design and waste valorization. This study examined the influence of wood particle size and shape on the properties of pastes processed by Liquid Deposition Modeling (LDM). Softwood sawdust separated into nine particle size ranges and ground wood powder were analyzed. The materials were mixed with water and methylcellulose as a natural binder, printed into test specimens, and evaluated for shrinkage, density, and flexural strength. Finer particles led to higher densities (up to 0.38 g/cm³), flexural strengths (up to 4.8 MPa), and elastic moduli (up to 436 MPa), while larger particles limited the filler content and reduced mechanical performance. Grinding decreased particle slenderness and promotes greater material density, thereby improving mechanical strength. These findings demonstrate that particle characteristics strongly influence paste printability and mechanical performance, providing a basis for the further development of resource-efficient wood-based materials for sustainable manufacturing.