Digital Twin for Fused Filament Fabrication (FFF)

Fused Filament Fabrication (FFF) is one of the most accessible and cost-effective additive manufacturing (AM) techniques; however, it is frequently hindered by process instability, print defects, and reliance on heuristic parameter tuning. This study presents the development and implementation of a Digital Twin framework for FFF, designed to provide real-time process insight, predictive control, and automated decision support. The proposed architecture integrates a sensorized physical FFF printer with a virtual environment comprising physics-based simulation modules—including thermal, structural, and fluid flow models—as well as machine learning algorithms for defect prediction and process optimization. Data acquisition is achieved through thermal, mechanical, and visual sensors, while control feedback is enabled via dynamic G-code adaptation and predictive maintenance mechanisms. The framework is validated through representative use cases demonstrating enhanced print quality, reduced failure rates, and improved repeatability across different geometries and process conditions. Furthermore, future extensions are proposed, including integration with blockchain for secure material passports, support for multi-material and 5D printing processes, and compliance with ISO/ASTM 52900 standards to ensure cross-platform interoperability. The Digital Twin paradigm introduced here establishes a foundational step toward autonomous, intelligent, and traceable FFF manufacturing systems.