Title
High-throughput feature extraction for measuring attributes of deforming open-cell foams.
Abstract
Metallic open-cell foams are promising structural materials with applications in multifunctional systems such as biomedical implants, energy absorbers in impact, noise mitigation, and batteries. There is a high demand for means to understand and correlate the design space of material performance metrics to the material structure in terms of attributes such as density, ligament and node properties, void sizes, and alignments. Currently, X-ray Computed Tomography (CT) scans of these materials are segmented either manually or with skeletonization approaches that may not accurately model the variety of shapes present in nodes and ligaments, especially irregularities that arise from manufacturing, image artifacts, or deterioration due to compression. In this paper, we present a new workflow for analysis of open-cell foams that combines a new density measurement to identify nodal structures, and topological approaches to identify ligament structures between them. Additionally, we provide automated measurement of foam properties. We demonstrate stable extraction of features and time-tracking in an image sequence of a foam being compressed. Our approach allows researchers to study larger and more complex foams than could previously be segmented only manually, and enables the high-throughput analysis needed to predict future foam performance.
Year
DOI
Venue
2020
10.1109/TVCG.2019.2934620
IEEE transactions on visualization and computer graphics
Keywords
Field
DocType
Ligaments,Junctions,Computed tomography,Image coding,Feature extraction,Image segmentation,Lattices
Design space,Computer vision,Compression (physics),Structural material,Computer science,Noise control,Feature extraction,Skeletonization,Artificial intelligence,Throughput,Workflow
Journal
Volume
Issue
ISSN
26
1
1077-2626
Citations 
PageRank 
References 
1
0.36
13
Authors
7
Name
Order
Citations
PageRank
Steve Petruzza133.79
Attila Gyulassy245323.11
Samuel Leventhal310.36
John J. Baglino410.36
Michael Czabaj510.36
Ashley D. Spear610.36
Valerio Pascucci73241192.33