journal article·2026-05Open access

Fused filament fabrication additive manufacturing of thermoplastic composites reinforced with continuous natural fibers: A systematic review

Oswaldo Rivero-Romero, Jimy Unfried-Silgado, Henry A. Colorado

Abstract

In the field of additive manufacturing (AM), fused filament fabrication (FFF) of thermoplastic composites reinforced with continuous natural fibers (CNFs) has emerged as a promising sustainable alternative to synthetic fiber composites. This review provides a systematic analysis of the FFF process for continuous natural fiber-reinforced thermoplastic composites (CNFRTCs), focusing on materials, printing techniques, mechanical properties, and applications. A bibliometric analysis of publications (SR) method following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was conducted in this research. The most used fibers are flax and ramie, while polylactic acid (PLA) is the predominant matrix. Key techniques include in-situ impregnation, pre-preg filament, and dual-nozzle printing. The review highlights that CNFRTCs exhibit significantly improved tensile and flexural properties compared to unreinforced polymers, though performance varies due to factors such as fiber content, printing parameters, and interfacial adhesion. Applications range from lightweight structures and impact-resistant components to shape-changing and shape-memory materials. Despite these advances, challenges remain in fiber variability, interfacial bonding, and process optimization. Future research should focus on enhancing impregnation quality, expanding material characterization, and promoting interdisciplinary collaboration to facilitate the industrial adoption of CNFRTCs.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Innovations in Concrete and Construction Materials · Natural Fiber Reinforced Composites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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