Engineering topics

James Kiser

Publications and source records attributed to James Kiser.

3 records

NASA NTRSDate not supplied

2021 CMH-17 Vol 5 Virtual Meeting CMC M&P Brief Overview

This overview (several slides) describes 1) the overall Goals of the CMH-17 Vol 5 CMC M&P (Materials and Processes) Working Group, 2) M&P sections in the existing CMH-17 Vol 5A that have been targeted for updating, 3) challenges to updating that content for the next edition of Vol 5, and 4) M&P plans for updating CMH-17 Vol 5A. M&P Working Group chairs Kiser and Lehman are leading a group of technical experts to address the sections that require the most attention in terms of updating information. A virtual meeting is providing an opportunity to recruit CMC, fiber, and EBC technical experts to address those sections. Goals will be defined for completing the updating of those sections, taking into account the need to have the updated content reviewed by SME (subject matter experts) well in advance of the publishing date.

Environmental Barrier Coating (EBC)↗

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↗

NASA NTRSDate not supplied

High-Temperature Mechanical Tensile Testing of Unidirectional Sic/Sic Composites Using A Versatile Lamp Furnace

Silicon carbide (SiC) based ceramic matrix composites (CMCs) are of interest for high-temperature structural applications, including use in turbine engine components. To understand damage mechanisms and predict durability of these systems, in-depth knowledge of their behavior at ambient and high temperatures is necessary. A method for high temperature testing of SiC/SiC minicomposite specimens up to 1500oC in both tensile fast fracture and creep conditions to investigate their thermomechanical behavior will be presented. This method utilizes a novel radiation heat furnace, equipped with infrared lamps with ellipsoidal reflectors, to heat the specimen at the furnace center in user-defined atmospheres. The housing structure of the lamps allows for observation of the specimen during testing using optical methods and for conducting other experimental studies. The goal is to employ nondestructive health monitoring techniques in-situ, including acoustic emission (AE), electrical resistance (ER), and digital image correlation (DIC) to evaluate damage initiation and evolution in the minicomposite specimens during tensile testing. Results from mechanical testing and nondestructive techniques will be presented. Electron microscopy and X-ray micro-computed tomography were used to further characterize specimen damage after testing.

Aeronautics↗