{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/134227"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/134227","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Data-Driven Characterization of Micro-structural Shape and Topology in Engineering Materials","abstract":"This thesis presents a data-driven framework for the quantitative characterization of microstructural shape and topology in engineering materials, integrating invariant geometric descriptors and statistical dimensionality reduction techniques. Specifically, Hu moments, Principal Eigenvalue Moment (PEM), and Principal Component Analysis (PCA) are applied to a diverse dataset comprising experimental images of Titanium-Aluminum alloys and Inconel 718 superalloy, computationally designed meta-materials including unit cells and spinodoids, and synthetic microstructures generated via deep learning models such as Progressive Generative Adversarial Network (PGAN) and Denoising Diffusion Probabilistic Models (DDPM). Both Hu moments and PEM portray a high degree of invariance to rotation and scaling, showing considerable effectiveness in capturing morphologic features like grain size, ellipticity, and asymmetry. PCA provides a complementary perspective by revealing global variance patterns in pixel intensity distributions, although it is sensitive to rotation and color information. The result reflects that synthetic images generated from DDPM closely mimic real microstructure data in terms of both shape and texture, whereas images from the PGAN model align better with color-based PCA. The framework supports reproducible and scalable quantification of microstructures, which aids materials informatics, classification, and computational materials design.","abstract_html":"This thesis presents a data-driven framework for the quantitative characterization of microstructural shape and topology in engineering materials, integrating invariant geometric descriptors and statistical dimensionality reduction techniques. Specifically, Hu moments, Principal Eigenvalue Moment (PEM), and Principal Component Analysis (PCA) are applied to a diverse dataset comprising experimental images of Titanium-Aluminum alloys and Inconel 718 superalloy, computationally designed meta-materials including unit cells and spinodoids, and synthetic microstructures generated via deep learning models such as Progressive Generative Adversarial Network (PGAN) and Denoising Diffusion Probabilistic Models (DDPM). Both Hu moments and PEM portray a high degree of invariance to rotation and scaling, showing considerable effectiveness in capturing morphologic features like grain size, ellipticity, and asymmetry. PCA provides a complementary perspective by revealing global variance patterns in pixel intensity distributions, although it is sensitive to rotation and color information. The result reflects that synthetic images generated from DDPM closely mimic real microstructure data in terms of both shape and texture, whereas images from the PGAN model align better with color-based PCA. The framework supports reproducible and scalable quantification of microstructures, which aids materials informatics, classification, and computational materials design.","abstract_has_math":false,"creators":["Kuang, Kuang"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Acar, Pinar"],"committee_members":["Chen, Jie","West, Robert L."],"year":2025,"date_issued":"2025-05-25","date_published":"2025-05-25","updated_at":"2026-07-22T22:18:55Z","subjects":["Microstructure","Image Processing","Shape and Topology Quantification","Engineering Materials"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44020"],"render_values":[{"text":"vt_gsexam:44020","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/134227","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Acar, Pinar"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Chen, Jie","West, Robert L."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Kuang, Kuang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-05-26T08:00:27Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-26T08:00:27Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05-25"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Microstructure","Image Processing","Shape and Topology Quantification","Engineering Materials"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44020"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/134227"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents a data-driven framework for the quantitative characterization of microstructural shape and topology in engineering materials, integrating invariant geometric descriptors and statistical dimensionality reduction techniques. 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The result reflects that synthetic images generated from DDPM closely mimic real microstructure data in terms of both shape and texture, whereas images from the PGAN model align better with color-based PCA. The framework supports reproducible and scalable quantification of microstructures, which aids materials informatics, classification, and computational materials design."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Materials employed in aerospace and other engineering fields need to possess a unique combination of properties - being exceptionally strong, lightweight, and resistant to harsh environments. These attributes depend not only on the constituents of the material, but also on the internal structure, known as the microstructure, which demonstrates geometric features that are shaped and ordered. This research proposes novel computational methods for the quantification and comparison of microstructures. It seeks to improve our understanding of the relationship between microstructure morphology and material performance by applying image processing techniques and sophisticated mathematical algorithms to images of various metals and synthetic materials. The study uses a mix of real materials (like Titanium-Aluminum and Nickel alloys) and artificially generated materials with machine learning methods. Through pattern comparison, the work accelerates evaluation processes for new materials and enables the development of materials designed with enhanced safety, efficiency, and adaptability for emerging technologies."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Data-Driven Characterization of Micro-structural Shape and Topology in Engineering Materials"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Acar, Pinar"],"dc:contributor.committeemember":["Chen, Jie","West, Robert L."],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Kuang, Kuang"],"dc:date.accessioned":["2025-05-26T08:00:27Z"],"dc:date.available":["2025-05-26T08:00:27Z"],"dc:date.issued":["2025-05-25"],"dc:description.abstract":["This thesis presents a data-driven framework for the quantitative characterization of microstructural shape and topology in engineering materials, integrating invariant geometric descriptors and statistical dimensionality reduction techniques. 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The result reflects that synthetic images generated from DDPM closely mimic real microstructure data in terms of both shape and texture, whereas images from the PGAN model align better with color-based PCA. The framework supports reproducible and scalable quantification of microstructures, which aids materials informatics, classification, and computational materials design."],"dc:description.abstractgeneral":["Materials employed in aerospace and other engineering fields need to possess a unique combination of properties - being exceptionally strong, lightweight, and resistant to harsh environments. These attributes depend not only on the constituents of the material, but also on the internal structure, known as the microstructure, which demonstrates geometric features that are shaped and ordered. This research proposes novel computational methods for the quantification and comparison of microstructures. It seeks to improve our understanding of the relationship between microstructure morphology and material performance by applying image processing techniques and sophisticated mathematical algorithms to images of various metals and synthetic materials. The study uses a mix of real materials (like Titanium-Aluminum and Nickel alloys) and artificially generated materials with machine learning methods. 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