Monitoring and Control
This research program addresses the finished part quality roadblock, process repeatability/reliability, cost and productivity challenges that inhibit industry use of AM in part manufacturing.
AM processes suffer from random and environmental disturbances that undermine process quality and repeatability. Disturbances are mainly associated with laser power, beam quality, beam product parameter, spatial power intensity, powder morphology, energy distribution, non-linear thermodynamic properties of material, motion systems, and plasma formation. MSAM researchers are developing solutions to overcome the effects of these disturbances to create closed-loop control systems and algorithms to monitor the process, and to tune actuating signals accordingly.
MSAM researchers are also developing novel in- and off-line quality assurance protocols to establish the relationship between in-process metrology feedback data and post-process part characterization, providing in-situ quality assurance quantifiers that detect defects, and that will support industry to enable the certification of process and products. Machine learning algorithms, along with sophisticated monitoring devices, will effectively monitor defects and disturbances in real-time, allowing for adjustments in process parameters through advanced controllers.
This Project includes solutions, protocols and quantifiers to aid in the creation of a “Certify-as-you-build” AM platform.