Document Type
Thesis - Open Access
Award Date
2014
Degree Name
Master of Science (MS)
Department / School
Mechanical Engineering
First Advisor
Fereidoon Delfanian
Second Advisor
Todd Letcher
Abstract
The study of fatigue and fracture mechanics is crucial to the health monitoring and overall safety of aerospace and civil structures. The materials used to manufacture these industry parts have inherent anomalies that eventually grow under cyclic loadings during regular operation. The ASTM and/or military testing guidelines used to analyze and qualify structures and materials for use are costly and time consuming. Therefore, any additional methods or knowledge that can be used to reduce the number of tests will be useful to the industry to avoid unnecessary costs. Acoustic emission (AE) monitoring is currently used in many applications to assess the structural health and remaining useful life in various civil and mechanical applications. Its capability to assess smaller crack growth well before any strain monitoring system can help for early structural lifetime estimation. The main goal of this thesis was to develop a method using acoustic emission monitoring for fatigue life predictions similar to a strain energy-based fatigue life prediction method. Experiments were conducted to determine acoustic energy parameters and patterns at various stress levels during constant stress fatigue testing. This information was used to make life predictions which were then compared to experimentally obtained data. Statistical evaluation of the correlation assured the dependencies between the energy variables.
Library of Congress Subject Headings
Acoustic emission testing
Materials -- Fatigue
Structural health monitoring
Description
Includes bibliographical references (pages 56-58)
Format
application/pdf
Number of Pages
70
Publisher
South Dakota State University
Rights
In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/
Recommended Citation
Nesaei, Sepehr, "Using Acoustic Emission Monitoring for Energy-based Fatigue Life Prediction" (2014). Electronic Theses and Dissertations. 1792.
https://openprairie.sdstate.edu/etd/1792