Date of Award
12-2021
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Mechanical Engineering
Department
Mechanical Engineering
Major Professor
Dr. Uttam Chakravarty
Second Advisor
Dr. Martin Guillot
Third Advisor
Dr. David Hui
Abstract
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.
Recommended Citation
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.
https://scholarworks.uno.edu/td/2929
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.