{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/33407"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/33407","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Development and Design of Self-Sensing SMAs using Thermoelectric Effect","abstract":"Active research of SMAs has shown that its Seebeck coefficient is sensitive to its martensitic phase transformation and has the potential to determine the SMAs state of transformation. The combination of Shape Memory Alloys, which have a positive Seebeck coefficient, and Constantan which has a negative Seebeck coefficient (-35 mV/K) results in a thermocouple capable of measuring temperature. The work presented in this thesis is based on the development and design of this sensor. This sensor is used to study the hysteretic behaviour of SMAs. Although Shape Memory Alloys (SMAs) exhibit a myriad of nonlinearities, SMAs show two major types of nonlinear hysteresis. During cyclic loading of the SMAs, it is observed that one type of hysteretic behavior depends on the rate of heating the SMAs, whilst the variation of maximum temperature of an SMA in each cycle results in the other hysteretic behavior. This later hysteretic behavior gives rise to major and minor nonlinear loops of SMAs. The present work analyzes the nonlinearities of hysteretic envelopes which gives the different maximum temperatures reached for each hysteretic cycle with respect to stress and strain of the SMA. This work then models this behavior using Adaptive Neuro Fuzzy Inference System (ANFIS) and compares it to experimental results. The nonlinear learning and adaptation of ANFIS architecture makes it suitable to model the temperature path hysteresis of SMAs.","abstract_html":"Active research of SMAs has shown that its Seebeck coefficient is sensitive to its martensitic phase transformation and has the potential to determine the SMAs state of transformation. The combination of Shape Memory Alloys, which have a positive Seebeck coefficient, and Constantan which has a negative Seebeck coefficient (-35 mV/K) results in a thermocouple capable of measuring temperature. The work presented in this thesis is based on the development and design of this sensor. This sensor is used to study the hysteretic behaviour of SMAs. Although Shape Memory Alloys (SMAs) exhibit a myriad of nonlinearities, SMAs show two major types of nonlinear hysteresis. During cyclic loading of the SMAs, it is observed that one type of hysteretic behavior depends on the rate of heating the SMAs, whilst the variation of maximum temperature of an SMA in each cycle results in the other hysteretic behavior. This later hysteretic behavior gives rise to major and minor nonlinear loops of SMAs. The present work analyzes the nonlinearities of hysteretic envelopes which gives the different maximum temperatures reached for each hysteretic cycle with respect to stress and strain of the SMA. This work then models this behavior using Adaptive Neuro Fuzzy Inference System (ANFIS) and compares it to experimental results. The nonlinear learning and adaptation of ANFIS architecture makes it suitable to model the temperature path hysteresis of SMAs.","abstract_has_math":false,"creators":["Malladi, Vijaya Venkata Narasimha Sriram"],"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":["Tarazaga, Pablo Alberto"],"committee_members":["Kurdila, Andrew J.","Inman, Daniel J."],"year":2013,"date_issued":"2013-05-20","date_published":"2013-05-20","updated_at":"2026-07-22T22:18:42Z","subjects":["ANN","Position Control","ANFIS","Seebeck Coefficient","Thermoelectric Effects","Sensorless Control","Shape Memory Alloys"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-06032013-114013"],"render_values":[{"text":"etd-06032013-114013","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/33407","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Tarazaga, Pablo Alberto"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kurdila, Andrew J.","Inman, Daniel J."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Malladi, Vijaya Venkata Narasimha Sriram"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:39:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:39:18Z","2013-06-17"]},{"key":"dc:date.issued","label":"Date","values":["2013-05-20"]},{"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":["ANN","Position Control","ANFIS","Seebeck Coefficient","Thermoelectric Effects","Sensorless Control","Shape Memory Alloys"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"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":["etd-06032013-114013"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/33407"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Active research of SMAs has shown that its Seebeck coefficient is sensitive to its martensitic phase transformation and has the potential to determine the SMAs state of transformation. The combination of Shape Memory Alloys, which have a positive Seebeck coefficient, and Constantan which has a negative Seebeck coefficient (-35 mV/K) results in a thermocouple capable of measuring temperature. The work presented in this thesis is based on the development and design of this sensor. This sensor is used to study the hysteretic behaviour of SMAs. Although Shape Memory Alloys (SMAs) exhibit a myriad of nonlinearities, SMAs show two major types of nonlinear hysteresis. During cyclic loading of the SMAs, it is observed that one type of hysteretic behavior depends on the rate of heating the SMAs, whilst the variation of maximum temperature of an SMA in each cycle results in the other hysteretic behavior. This later hysteretic behavior gives rise to major and minor nonlinear loops of SMAs. The present work analyzes the nonlinearities of hysteretic envelopes which gives the different maximum temperatures reached for each hysteretic cycle with respect to stress and strain of the SMA. This work then models this behavior using Adaptive Neuro Fuzzy Inference System (ANFIS) and compares it to experimental results. The nonlinear learning and adaptation of ANFIS architecture makes it suitable to model the temperature path hysteresis of SMAs."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Development and Design of Self-Sensing SMAs using Thermoelectric Effect"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Tarazaga, Pablo Alberto"],"dc:contributor.committeemember":["Kurdila, Andrew J.","Inman, Daniel J."],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Malladi, Vijaya Venkata Narasimha Sriram"],"dc:date.accessioned":["2014-03-14T20:39:18Z"],"dc:date.available":["2014-03-14T20:39:18Z","2013-06-17"],"dc:date.issued":["2013-05-20"],"dc:description.abstract":["Active research of SMAs has shown that its Seebeck coefficient is sensitive to its martensitic phase transformation and has the potential to determine the SMAs state of transformation. The combination of Shape Memory Alloys, which have a positive Seebeck coefficient, and Constantan which has a negative Seebeck coefficient (-35 mV/K) results in a thermocouple capable of measuring temperature. The work presented in this thesis is based on the development and design of this sensor. This sensor is used to study the hysteretic behaviour of SMAs. Although Shape Memory Alloys (SMAs) exhibit a myriad of nonlinearities, SMAs show two major types of nonlinear hysteresis. During cyclic loading of the SMAs, it is observed that one type of hysteretic behavior depends on the rate of heating the SMAs, whilst the variation of maximum temperature of an SMA in each cycle results in the other hysteretic behavior. This later hysteretic behavior gives rise to major and minor nonlinear loops of SMAs. The present work analyzes the nonlinearities of hysteretic envelopes which gives the different maximum temperatures reached for each hysteretic cycle with respect to stress and strain of the SMA. This work then models this behavior using Adaptive Neuro Fuzzy Inference System (ANFIS) and compares it to experimental results. 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