Cost-effective sensor-based digital twin for fused deposition modeling 3D printers

Abstract

In 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.