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dc.contributor.authorAnderson, Chrisen
dc.date.accessioned2020-06-02T18:37:07Z
dc.date.available2020-06-02T18:37:07Z
dc.identifier.urihttp://hdl.handle.net/1974/27875
dc.description.abstractIt is well known that the presence of pores has an effect on the mechanical properties of materials. A figure case is X-750, a Ni-based alloys exposed to neutron irradiation in nuclear reactors. Interactions with neutrons lead to atom transmutations that spontaneously create nanoscale pores in otherwise sound material. Imaging these nanoscale features using transmission electron microscopy is key to predicting and assessing the mechanical behavior of structural materials in nuclear reactors. Analyzing these micrographs is often a tedious and time-consuming manual process. It is a prime candidate for automation. A region-based convolutional neural network is adapted to detect helium bubbles in micrographs of neutron-irradiated Inconel X-750 reactor spacer springs. This neural network produces analyses of similar accuracy and reproducibility to that produced by humans. Further, this method is shown as being four orders of magnitude faster than manual analysis allowing for generation of significant quantities of data. The proposed method can be used with micrographs of different Fresnel contrasts and magnification levels. To understand the effect that these pores have on mechanical properties, a lattice element method is introduced. The lattice element method uses a spring network to act as forces between nodes to predict the micro-mechanical response of the system. This computationally efficient model shows promise in the application of porous material mechanical testing to fracture. A two-dimensional isotropic linear-elastic model is developed with multiple spring networks and boundary conditions. The lattice element method (LEM) is validated in all model conditions for isotropic behaviour and for size effects. The LEM is then validated in the case of a slab containing a circular pore. The LEM predicts stress concentrations in good agreement with an analytical solution. In the future, the detection network and the LEM, used in tandem, could enable the development of high-throughput simulated material models from experimental porous samples.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.rightsAttribution-NonCommercial 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/*
dc.subjectObject Detectionen
dc.subjectNeural Networksen
dc.subjectLattice Element Methoden
dc.subjectX-750en
dc.subjectHelium Bubblesen
dc.titleAutomated Detection of Helium Bubbles in Inconel X-750 and Modeling Their Effect on its Mechanical Propertiesen
dc.typethesisen
dc.description.degreeM.A.Sc.en
dc.contributor.supervisorBeland, Laurent
dc.contributor.departmentMechanical and Materials Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
Except where otherwise noted, this item's license is described as Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada