Defect-free Ceramic Hybrid-AM using Intelligent Layer Reworking

Hybrid additive manufacturing of advanced ceramics facilitates the production of highly dense and precise parts by combining additive and subtractive processes. However, extrusionbased processes are susceptible to stochastic defects such as voids, or under extrusions, which can degrade material properties, leading to premature failure and lower yield. This research demonstrates deep learning informed selective layer reworking for a ceramic hybrid additive manufacturing platform. Each layer was evaluated in-situ using a vision-based monitoring system, consisting of a camera and a laser profilometer. Through closed-loop control, a decision was made autonomously to pause production, allowing for defect repair prior to reprinting the layer. The deep learning model detected voids with a precision of 90%, and the laser algorithm achieved F scores greater than 98% across a range of parts, facilitating future corrective actions to repair these regions. This unlocks new opportunities for regulated industries aiming to exploit quality-assured ceramic components that benefit from freeform fabrication.