John Bernardin

Balark Tiwari, Nishan Khadka, Nicholas Capps et al.

Digital Glass Forming (DGF) is a new manufacturing process for low-batch glass fabrication. The work zone temperature in DGF processes must be maintained in the glass's working range to ensure good fabrication. If the temperature is too low, the filament will not wet to the substrate or previously deposited material and, if the temperature is too high, the filament may disengage from the substrate or previously deposited material, or it may partially vaporize. In this work, a real-time temperature control system capable of synchronizing process parameter, thermal camera, and visual camera data for the DGF process is introduced. A process parameter map for a scan velocity of 0.5 mm/s is constructed, as is a data-driven dynamic temperature process model. A digital controller is designed to regulate the work zone temperature. The temperature controller is a closed loop tracking controller that adjusts the commanded laser power to regulate the measured temperature. Two sets of experiments are conducted to analyze the controller performance. In the first set of experiments, single tracks on a substrate are fabricated with constant laser power and with the closed loop temperature controller. It is seen that the closed loop controller is able to extend the process parameter map into regions where using a constant laser power will result in a failed build. In the second set of experiments, walls are fabricated. Using constant laser power results in a failed build (i.e., material vaporization at the corners and the filament prematurely detaching from the substrate) as the temperature process dynamics change with layer and at the corners. The closed loop controller successfully fabricated the wall without vaporization at the corners and premature filament detachment as the controller adjusts the laser power to account for the changing temperature process dynamics.

David Mascareñas, Andre Green, Ashlee Liao et al.

We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manufacturing is of high interest. However, the extreme light environment and high speed dynamics of metallic melt pools have made this a difficult environment in which to make measurements. Event-based sensing is an alternative measurement paradigm where data is only transmitted/recorded when a measured quantity exceeds a threshold resolution. The result is that event-based sensors consume less power and less memory/bandwidth, and they operate across a wide range of timescales and dynamic ranges. Event-driven driven imagers stand out from conventional imager technology in that they have a very high dynamic range of approximately 120 dB. Conventional 8 bit imagers only have a dynamic range of about 48 dB. This high dynamic range makes them a good candidate for monitoring manufacturing processes that feature high intensity light sources/generation such as metallic additive manufacturing and welding. In addition event based imagers are able to capture data at timescales on the order of 100 μs, which makes them attractive to capturing fast dynamics in a metallic melt pool. In this work we demonstrate that event-driven imagers have been shown to be able to observe tungsten inert gas (TIG) and laser welding melt pools. The results of this effort suggest that with additional engineering effort, neuromorphic event imagers should be capable of 3D geometry measurements of the melt pool, and anomaly detection/classification/prediction.