Shomenov, KemelAli, Md. HazratJyeniskhan, NursultanAl-Ashaab, AhmedShehab, Essam2025-07-102025-07-102025-12-31Shomenov K, Ali MH, Jyeniskhan N, et al., (2025) Cost-effective sensor-based digital twin for fused deposition modeling 3D printers. International Journal of Computer Integrated Manufacturing, Available online 13 May 20250951-192Xhttps://doi.org/10.1080/0951192x.2025.2504085https://dspace.lib.cranfield.ac.uk/handle/1826/24178In a highly digitalized world, Digital Twin (DT) technology is becoming vital in manufacturing, especially in additive manufacturing. This research work presents cost-effective digital twin development and implementation for Fused Deposition Modeling (FDM) 3D printers. This developed system enhances real-time process monitoring, anomaly detection, and autonomous control through a logical approach. Sensor data that measures and tracks filament flow, vibrations, and nozzle position are processed in real-time for the detection of anomalies such as layer shifting, under- and over-extrusion, and excessive vibrations. Magnetic encoders enable monitoring the filament flow with 0.73% error, position sensors identify nozzle displacement to monitor layer shifts. The sensor data are stored in the Firebase and visualized in the Unity interface with a 500- 700 ms data lag. In contrast to traditional systems, this developed system works independently without the need for external hosts, providing a very low-cost, modular solution (less than $50) appropriate for small-scale applications. This research also addresses the gap in FDM 3D printers and sensor-based DT by demonstrating practical, real-time interventions for quality assurance in FDM processes.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/3D printingfused deposition modelingmaterial extrusiondigital twinsensors4014 Manufacturing Engineering40 Engineering9 Industry, Innovation and InfrastructureIndustrial Engineering & AutomationArticle1362-3052673363ahead-of-printahead-of-print