Movement Tracking-Based In-Situ Monitoring System for Additive Manufacturing

Monitoring and identification of defects during additive manufacturing is mostly done by bespoke optical or acoustic measurement systems. These in-situ monitoring technologies are either intrusive or sensitive to noisy manufacturing environments. We propose a movement tracking-based in-situ monitoring system for additive manufacturing, which is non-intrusive, less sensitive to environmental factors, and easier to operate and maintain. It evaluates the hypothesis that extruder nozzle temperature can be predicted from printer head movement, since temperature and acceleration are correlated due to the printers control unit. Subsequently, this provides an indication of print quality as the extruder temperature plays a vital role. We collected data from experiments using the MakerBot Replicator to examine the hypothesis. Results show that a Random Forest algorithm is more accurate in predicting the temperature variation using head acceleration and time lag temperature data as input parameters, and outperforms a k-Nearest Neighbors and a Vector Autoregression algorithm.