SUBJECT: M.S. Thesis Presentation
BY: Caroline Massey
TIME: Wednesday, April 21, 2021, 11:00 a.m.
PLACE: MS Teams, Virtual
TITLE: Understanding the Role of Segmentation on Structure-Property Predictions Made via Machine Learning
COMMITTEE: Dr. Christopher Saldana, Chair (ME)
Dr. Katherine Fu (ME)
Dr. Thomas Kurfess (ME)
Mr. David G. Moore (Sandia National Laboratories)


Machine learning allows for the ability to predict an output from a diverse hyperspace of inputs. In the context of additive manufacturing, this class of approach could be useful in determining whether a specific measured defect field meets a given qualification requirement, this being particularly relevant for the aerospace and medical industries. The present study will investigate the effect of porosity surface determination on predicting the mechanical properties of AlSi10Mg processed by laser powder bed fusion and characterized using micro-computed tomography. A range of machine learning models will be applied that include support vector machine, neural networks, decision trees, Bayesian classifiers, etc. The effects of isosurface and local thresholding approaches for porosity segmentation, as well as image filtering schemes, on precision performance of the machine learning models will be evaluated for samples produced under differing levels of global energy density (GED).