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The combination of continuous natural fiber and fused filament fabrication (FFF) 3D printing enables the manufacturing of low carbon emitting, environment friendly, lightweight, and high strength biomass composites with designated geometry characteristics. In current literature, reviews associated with continuous fiber 3D printing primarily cover synthetic fibers, such as carbon fiber, Kevlar, and glass fiber. Very few pieces of literature on the FFF printing of continuous natural fibers are available. Techniques/methodologies for incorporating continuous natural fiber reinforcements in FFF is an emerging field of research. A comprehensive review and discussion on current progress and the future prospects of continuous natural fiber 3D printing would be beneficial to its development. This article summarizes the current research status of continuous natural fiber 3D printing, including information on printing techniques, materials, and the influence of printing parameters on composite properties, so as to provide reference for the future development of FFF technology using continuous natural fiber and thermoplastic composites. [ABSTRACT FROM AUTHOR]

Graph transformer has achieved remarkable success in graph‐based segmentation tasks. Inspired by this success, we propose a novel method named MeshFormer for applying the graph transformer to the semantic segmentation of high‐resolution meshes. The main challenges are the large data size, the massive model size, and the insufficient extraction of high‐resolution semantic meanings. The large data or model size necessitates unacceptably extensive computational resources, and the insufficient semantic meanings lead to inaccurate segmentation results. MeshFormer addresses these three challenges with three components. First, a boundary‐preserving simplification is introduced to reduce the data size while maintaining the critical high‐resolution information in segmentation boundaries. Second, a Ricci flow‐based clustering algorithm is presented for constructing hierarchical structures of meshes, replacing many convolutions layers for global support with only a few convolutions in hierarchy structures. In this way, the model size can be reduced to an acceptable range. Third, we design a graph transformer with cross‐resolution convolutions, which extracts richer high‐resolution semantic meanings and improves segmentation results over previous methods. Experiments show that MeshFormer achieves gains from 1.0% to 5.8% on artificial and real‐world datasets. [ABSTRACT FROM AUTHOR]