About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Natural Fibers and Biocomposites
|
| Presentation Title |
Leveraging strain field mining and spatial statistics to identify critical damage and failure in cellulosic fiber networks |
| Author(s) |
Lilly M. Schroer, Christopher L. Muhlstein |
| On-Site Speaker (Planned) |
Lilly M. Schroer |
| Abstract Scope |
Damage accumulation in cellulosic fiber materials is driven by local variations in the network structure. Under uniaxial loading, the orthotropic, heterogeneous network has an uneven load distribution and high strain localization regions that cause damage and failure. A spatial analysis framework using digital image correlation, strain field mining, and spatial statistics can map and identify critical damage by capturing the spatial dependence of the network’s non-affine deformation. Local Indicators of Spatial Autocorrelation – particularly the Local Geary LISA statistic – identifies whether a location has positive (similar) or negative (dissimilar) spatial autocorrelation with its neighbors. The detection of negative spatial autocorrelation around local strain concentrations is used to identify the location of failure and characterize critical damage regions in a cellulosic material. Identifying failure and damage regions aids in failure prediction and provides insights into failure mechanisms for engineering damage tolerant networks and optimizing the performance of cellulosic packaging materials. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Mechanical Properties, Other, Other |