Engineering topics

Ashley Hilmas

Publications and source records attributed to Ashley Hilmas.

2 records

NASA NTRSDate not supplied

Synchrotron Tomography of SiC/SiC Minicomposites to Observe and Quantify Damage Evolution

Composite performance is dictated by its microstructure – property relationships. For example, the characteristic response of ceramic matrix composites (CMCs) depends on the distribution of its constituents (i.e. fibers, matrix, fiber coatings, matrix cracks, and voids). Micro x-ray computed tomography (micro-CT) provides an opportunity to characterize the distribution of these constituents and to quantify the CMC microstructure in three-dimensions. To this end, In-situ micro-CT was performed at the Advanced Light Source (ALS) in combination with uniaxial tensile testing on SiC/SiC minicomposites. This work will describe the tensile load-dependent damage evolution within the microstructure of the SiC/SiC CMC specimens. The results from the micro-CT datasets can be used to develop micromechanical models that effectively capture damage initiation and progression in CMCs. In addition, details regarding the segmentation tools and techniques employed to understand and quantify these large tomography datasets will be presented.

Ashley Hilmas↗

NASA NTRSDate not supplied

Trustworthy Machine Learning for Damage Identification in Composites

A challenging opportunity in structural health monitoring of composite materials is using machine learning (ML) methods to classify acoustic emissions (AE) according to the damage mechanism that emitted the signal. Although a wide variety of ML frameworks have been developed, there is a distinct lack of ground truth datasets which has precluded any direct assessment of their accuracy. Here, we present a novel ground truth dataset gathered on simplified unidirectional SiC/SiC composite structures. Herein, AE is collected from minicomposites which are loaded to targeted percentages of the ultimate tensile stress. These minicomposites are then volumetrically imaged with XCT and individual damage events, along with the mechanism, are correlated to AE. We explore the signal features that allow for mechanism discrimination, along with the feasibility of both unsupervised and supervised frameworks for use in the online monitoring of composite structures.

Machine learning, acoustic emission, ceramic matri↗