|About this Abstract
||2023 TMS Annual Meeting & Exhibition
||Quantifying Microstructure Heterogeneity for Qualification of Additively Manufactured Materials
||3D Computer Vision and Deep Learning for Porosity Analysis in Additive Manufacturing
||Daniel Diaz, Xingyang Li, Yuheng Nie, Elizabeth Holm, Anthony Rollett
|On-Site Speaker (Planned)
Additive manufacturing (AM) is a promising novel technology that is revolutionizing the way we manufacture products, but properties are limited by porosity produced during processing. In order to better understand the relationship between pore morphologies and properties, it is necessary to accurately identify the characteristic classes of pores observed. To this end we leverage the tools of 3D computer vision and convolutional neural networks to examine the pore morphologies present in datasets collected using X-ray computed tomography (CT). A transfer learning approach is utilized where 3D versions of EfficientNet are initialized with weights that have been trained on community datasets, and converted to a 3D format. Segmented CT image stacks are fed into this pretrained network, and the results are used to divide the pores into clusters that aid in identifying the various morphologies. This has the potential to become a valuable tool for automating the characterization of AM products.
||Additive Manufacturing, Machine Learning, Characterization