Materials Informatics for Images and Multi-dimensional Datasets: Poster Session
Sponsored by: ACerS Basic Science Division, ACerS Electronics Division
Program Organizers: Amanda Krause, Carnegie Mellon University; Alp Sehirlioglu, Case Western Reserve University; Daniel Ruscitto, GE Research
Monday 5:00 PM
October 10, 2022
Room: Ballroom BC
Location: David L. Lawrence Convention Center
Session Chair: Alp Sehirlioglu, Case Western Reserve University
B-3: 3D Computer Vision and Machine Learning for Porosity Analysis in Additive Manufacturing: Daniel Diaz1; Yuheng Nie1; Anthony Rollett1; Elizabeth Holm1; 1Carnegie Mellon University
Additive manufacturing (AM) is a promising novel technology that is revolutionizing the way we manufacture products, but many 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 ImageNet 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.