A digital twin strategy for major failure detection in fused deposition modeling processes

Part distortion during additive manufacturing (AM) may lead to catastrophic failure and significant waste of resources. Existing work often focuses on identification and detection of individual root causes such as melt pool geometries or extruder clogging to prevent part failures. Since the end-effect of major print failures can be the result of multiple error sources (including unknowns), relying on detection of individual root causes may misclassify some failed prints as successful. Instead, detecting end-effects or part distortion could provide early warning of major failures regardless of potential error sources. Distortion detection, however, currently involves computationally expensive simulation and analysis of sensing data. One promising solution is to adopt digital twin strategy to quickly compare model prediction to features extracted from in situ sensing data. This study extends the digital twin strategy to major distortion detection by developing (1) a multi-view optical sensing system for movable print beds and (2) failure detection methods by analyzing multi-view of part images layer by layer. Since the digital twin of actual prints at specific layers are generated offline, delay can be reduced to determine if a significant enough quality departure has occurred to justify termination of the print. In the experimental evaluation of this approach for a FDM machine with a moving print bed, failure was rapidly detected in two of the three test prints, while in the remaining print, failure was successfully detected after a short delay.