{"id":{"repo_id":"qucosa-diss","oai_identifier":"oai:qucosa:de:qucosa:71731"},"canonical_url":"https://search.dev.ndltd.org/etd/qucosa-diss/oai:qucosa:de:qucosa:71731","repository":{"repo_id":"qucosa-diss","name":"QUCOSA","base_url":"http://www.qucosa.de/oai/"},"display":{"title":"Quantifying Dislocation Microstructures","abstract":"In this work, we reconstructed and full characterized a dislocation microstructure that formed during an in situ micro-cantilever beam experiment. Based on this information, we were then able to infer how the dislocations propagated from the notch into the specimen. We propose using the so-called 'discrete-to-continuous' (D2C) method, which converts discrete dislocation data to continuum fields, as a means to quantify microstructures. With this method, we studied how different methods of initializing the microstructure in discrete dislocation dynamics simulations affects the resulting microstructure. We found that not considering cross-slip leads to very different microstructures, and that cross-slip results in more similar microstructures. Further, we used the continuum fields extracted via the D2C methods as input features for machine learning models for the classification of dislocation microstructures in nanoparticles. We found them to be well suited and that the combination of continuum fields is dependent on whether the microstructure is dominated by statistically stored or geometrically necessary dislocations.","abstract_html":"In this work, we reconstructed and full characterized a dislocation microstructure that formed during an in situ micro-cantilever beam experiment. Based on this information, we were then able to infer how the dislocations propagated from the notch into the specimen. We propose using the so-called 'discrete-to-continuous' (D2C) method, which converts discrete dislocation data to continuum fields, as a means to quantify microstructures. With this method, we studied how different methods of initializing the microstructure in discrete dislocation dynamics simulations affects the resulting microstructure. We found that not considering cross-slip leads to very different microstructures, and that cross-slip results in more similar microstructures. Further, we used the continuum fields extracted via the D2C methods as input features for machine learning models for the classification of dislocation microstructures in nanoparticles. We found them to be well suited and that the combination of continuum fields is dependent on whether the microstructure is dominated by statistically stored or geometrically necessary dislocations.","abstract_has_math":false,"creators":["Steinberger, Dominik"],"institution":"TU Bergakademie Freiberg","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Sandfeld, Stefan","Hartmaier, Alexander"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-07-20","date_published":"2020-07-20","updated_at":"2026-07-24T03:56:21Z","subjects":["dislocations","machine learning","Versetzung","Maschinelles Lernen"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sandfeld, Stefan","Hartmaier, Alexander"]},{"key":"dc:creator","label":"Author","values":["Steinberger, Dominik"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Technische Universität Bergakademie Freiberg"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["TU Bergakademie Freiberg"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["dislocations","machine learning","Versetzung","Maschinelles Lernen"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this work, we reconstructed and full characterized a dislocation microstructure that formed during an in situ micro-cantilever beam experiment. Based on this information, we were then able to infer how the dislocations propagated from the notch into the specimen. We propose using the so-called 'discrete-to-continuous' (D2C) method, which converts discrete dislocation data to continuum fields, as a means to quantify microstructures. With this method, we studied how different methods of initializing the microstructure in discrete dislocation dynamics simulations affects the resulting microstructure. We found that not considering cross-slip leads to very different microstructures, and that cross-slip results in more similar microstructures. Further, we used the continuum fields extracted via the D2C methods as input features for machine learning models for the classification of dislocation microstructures in nanoparticles. We found them to be well suited and that the combination of continuum fields is dependent on whether the microstructure is dominated by statistically stored or geometrically necessary dislocations."]},{"key":"dc:title","label":"Title","values":["Quantifying Dislocation Microstructures"]}]}],"canonical_facts":{"dc:contributor":["Sandfeld, Stefan","Hartmaier, Alexander"],"dc:creator":["Steinberger, Dominik"],"dc:description.abstract":["In this work, we reconstructed and full characterized a dislocation microstructure that formed during an in situ micro-cantilever beam experiment. Based on this information, we were then able to infer how the dislocations propagated from the notch into the specimen. We propose using the so-called 'discrete-to-continuous' (D2C) method, which converts discrete dislocation data to continuum fields, as a means to quantify microstructures. With this method, we studied how different methods of initializing the microstructure in discrete dislocation dynamics simulations affects the resulting microstructure. We found that not considering cross-slip leads to very different microstructures, and that cross-slip results in more similar microstructures. Further, we used the continuum fields extracted via the D2C methods as input features for machine learning models for the classification of dislocation microstructures in nanoparticles. We found them to be well suited and that the combination of continuum fields is dependent on whether the microstructure is dominated by statistically stored or geometrically necessary dislocations."],"dc:publisher":["Technische Universität Bergakademie Freiberg"],"dc:subject":["dislocations","machine learning","Versetzung","Maschinelles Lernen"],"dc:title":["Quantifying Dislocation Microstructures"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["TU Bergakademie Freiberg"]},"updated_at":"2026-07-24T03:56:21Z"}