Date of Award
Dr. Uttam Chakravarty
Dr. Martin Guillot
Dr. David Hui
A machine learning model is created to predict melt pool geometries of Ti-6Al-4V alloy created by the laser powder bed fusion process. Data is collected through an extensive literature survey, using results from both experiments and CFD modeling. The model focuses on five key input parameters that influence melt pool geometries: laser power, scanning speed, spot size, powder density, and powder layer thickness. The two outputs of the model are melt pool width and melt pool depth. The model is trained and tested by using the k fold cross validation technique. Multiple regression models are then applied to find the model that produces the least amount of error. Verification of the ML model was achieved by comparing the model results with experimental results and CFD results given the same parameter values throughout the models. The ML model results are consistent with the experimental and CFD results.
Ciaccio, Jonathan, "A Machine Learning Method for the Prediction of Melt Pool Geometries Created by Laser Powder Bed Fusion" (2021). University of New Orleans Theses and Dissertations. 2929.