Date of Award
Doctor of Philosophy (PhD)
In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex and cost-prohibitive. However, generative modes can provide new artificially generated data. Generative adversarial networks synthesize new computationally derived data through a process that learns the underlying features corresponding to the different laser process parameters in a generator network, then improves upon those artificial renderings by evaluating through the discriminator network. While this technique was effective at delivering high-quality images, modifications to the network through conditions showed improved capabilities at creating these new images. Using multiple evaluation metrics, it has been shown that generative models can be used to create new data for various laser process parameter combinations, thereby allowing a more comprehensive evaluation of ideal laser conditions for any particular build.
"Evaluation of Generative Models for Predicting Microstructure Geometries in Laser Powder Bed Fusion Additive Manufacturing"
(2022). Doctor of Philosophy (PhD), Dissertation, Computer Science, Old Dominion University, DOI: 10.25777/0jp3-hq36