THERMODYNAMIC AND INFORMATION ENTROPY-BASED PREDICTION AND DETECTION OF FATIGUE FAILURES IN METALLIC AND COMPOSITE MATERIALS USING ACOUSTIC EMISSION AND DIGITAL IMAGE CORRELATION

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dc.contributor.advisorModarres, Mohammaden_US
dc.contributor.advisorBruck, Hugh A.en_US
dc.contributor.authorKarimian, Seyed Fouaden_US
dc.contributor.departmentMechanical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2021-09-16T05:38:56Z
dc.date.available2021-09-16T05:38:56Z
dc.date.issued2021en_US
dc.description.abstractAlthough 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.en_US
dc.identifierhttps://doi.org/10.13016/x774-8sfb
dc.identifier.urihttp://hdl.handle.net/1903/27759
dc.language.isoenen_US
dc.subject.pqcontrolledMechanical engineeringen_US
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pquncontrolledAcoustic Emissionen_US
dc.subject.pquncontrolledCondition-based monitoringen_US
dc.subject.pquncontrolledDigital Image Correlationen_US
dc.subject.pquncontrolledFatigueen_US
dc.subject.pquncontrolledInformation Entropyen_US
dc.subject.pquncontrolledThermodynamic Entropyen_US
dc.titleTHERMODYNAMIC AND INFORMATION ENTROPY-BASED PREDICTION AND DETECTION OF FATIGUE FAILURES IN METALLIC AND COMPOSITE MATERIALS USING ACOUSTIC EMISSION AND DIGITAL IMAGE CORRELATIONen_US
dc.typeDissertationen_US

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