Title: THERMODYNAMIC AND INFORMATION ENTROPY-BASED PREDICTION AND DETECTION OF FATIGUE FAILURES IN METALLIC AND COMPOSITE MATERIALS USING ACOUSTIC EMISSION AND DIGITAL IMAGE CORRELATION
Author: Seyed Fouad Karimian
Date: Thursday, May 6th, 2:00 – 4:00 PM.
Zoom Link: https://umd.zoom.us/j/9968970907
Professor Mohammad Modarres, Advisor and Chair
Professor Hugh Bruck, Co-Advisor
Professor Aris Christou
Professor Abhijit Dasgupta
Professor Katrina Groth
Professor Norman Wereley, Dean’s Representative
Although assumed to be identical, manufactured components always present some variability in their performance while in service. This variability can be seen in their degradation path and time to failure as they are tested under identical conditions. In engineering structures and some components, fatigue is among the most common degradation mechanisms and has been under extensive study over the past century. A common characteristic of the fatigue life models is to rely on some observable or measurable markers of damage, such as crack length or modulus reduction. However, these markers become more pronounced and detectable toward the end of the component or structure’s life. Therefore, more advanced techniques would be needed to better account for a structure’s fatigue degradation. Several methods based on non-destructive testing techniques have developed over the past decades to decrease the uncertainty in fatigue degradation assessments. These methods seek to exploit the data collected by sensors during the operational life of a structure or component. Hence, the assessment of the health state can be constantly updated based on the operational conditions that allow for condition-based monitoring and maintenance. However, these methods are mostly context dependent and limited to specific experimental conditions. Therefore, a method to effectively characterize and measure fatigue damage evolution at multiple length scales based on the fundamental concept of entropy is studied in this dissertation. The two entropic-based indices used are: Thermodynamic entropy, and, Information entropy.
The objectives of this dissertation are to develop new methods for fatigue damage detection and failure prediction in metallic and FRP laminated composite materials by using AE and DIC techniques and converting them to information and thermodynamic entropy gains caused by fatigue damage.
1. Develop and experimentally validate fatigue damage detection, failure prediction, and prognosis approaches based on the information entropy of AE signal waveforms in both metallic and FRP laminated composite materials.
2. Develop and experimentally validate fatigue damage detection, failure prediction, and prognosis approaches based on thermodynamic entropy using the DIC technique in both metallic and FRP laminated composite materials.
3. Develop a framework for RUL estimation of metallic and FRP laminated composite structures based on the two entropic measures.