Towards Intelligent Fused Filament Fabrication: Computational Verification of a Monitoring and Early-Warning Framework for Instability Mitigation

Fused Filament Fabrication (FFF) plays a critical role in several application fields due to its affordability and manufacturing versatility. However, FFF reliability remains vulnerable to rapid environmental and operational variations, which directly influence the dimensional precision and mechanical properties of printed parts. To address these challenges, this study presents a simulation-based computational framework for the real-time early-warning supervision of FFF systems. The proposed multilayer architecture integrates high-throughput data acquisition, distributed computing, and dynamic analysis to proactively detect deviations from optimal conditions. Architectural verification follows a simulation-first methodology designed to replicate the operational dynamics of standard FFF hardware. By employing telemetry streams to test the decision-making pipeline, the study isolates computational performance, such as throughput and latency, from the confounding variables of physical hardware. This approach enables a precise, deterministic assessment of the system’s responsiveness, serving as a foundational de-risking step prior to empirical implementation. Numerical results of this study show that the integrated distributed computing model successfully manages high-frequency telemetry with a response time within the operational safety margins, confirming the architectural viability of the proposed solution. By providing insights into system behavior prior to physical deployment, this simulation-first strategy mitigates implementation risks and offers practical guidance for developing autonomous additive manufacturing workflows, advancing the transition toward intelligent industrial FFF.