{"id":{"repo_id":"aachen","oai_identifier":"oai:publications.rwth-aachen.de:62425"},"canonical_url":"https://search.dev.ndltd.org/etd/aachen/oai:publications.rwth-aachen.de:62425","repository":{"repo_id":"aachen","name":"RWTH Aachen University","base_url":"https://publications.rwth-aachen.de/oai2d"},"display":{"title":"Qualitätsmanagement und neuronale Netze - ein Ansatz zur prädiktiven Regelung thermischer Spritzprozesse","abstract":"The atmospheric plasma spraying process is established and provides cost reduction effects using qualified coatings. But the partly nonlinear and changing characteristics of this process are a challenge for process control.In this dissertation an approach is described, how a process control for atmospheric plasma spraying can be realised, using artificial neural networks. Base of this work is first the selection of a measurement system, the PFI-System, and second the choice of a control algorithm, the \"supervised control\". With the inductive procedure of Balzer an approach is described to define and optimise the artificial neural network. To train the artificial neural network design of experiments is used. With this a fast and targeted training can be done. An approach to update the artificial neural network regularly completes the theoretic research. By using a selected thermal spray process as a case study, the whole approach and the function of the controller is shown.","abstract_html":"The atmospheric plasma spraying process is established and provides cost reduction effects using qualified coatings. But the partly nonlinear and changing characteristics of this process are a challenge for process control.In this dissertation an approach is described, how a process control for atmospheric plasma spraying can be realised, using artificial neural networks. Base of this work is first the selection of a measurement system, the PFI-System, and second the choice of a control algorithm, the "supervised control". With the inductive procedure of Balzer an approach is described to define and optimise the artificial neural network. To train the artificial neural network design of experiments is used. With this a fast and targeted training can be done. An approach to update the artificial neural network regularly completes the theoretic research. By using a selected thermal spray process as a case study, the whole approach and the function of the controller is shown.","abstract_has_math":false,"creators":["Dören, Jens"],"institution":"Publikationsserver der RWTH Aachen University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Pfeifer, Tilo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007","date_published":"2007","updated_at":"2026-07-30T19:43:28Z","subjects":["info:eu-repo/classification/ddc/620","Neuronales Netz","Thermisches Spritzen","Regler","Reglerentwurf","Ingenieurwissenschaften","Statistische Versuchsmethodik","DoE","Artificial Neural Network","Thermal Spraying","Controller","Design of Experiments"],"languages":["ger"],"rights":["info:eu-repo/semantics/openAccess"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123994%22"],"render_values":[{"text":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123994%22","href":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123994%22","code":true}]}]},"links":{"outbound_url":"https://publications.rwth-aachen.de/record/62425","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Pfeifer, Tilo"]},{"key":"dc:creator","label":"Author","values":["Dören, Jens"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:coverage","label":"Dc Coverage","values":["DE"]},{"key":"dc:date","label":"Dc Date","values":["2007"]},{"key":"dc:publisher","label":"Institution","values":["Publikationsserver der RWTH Aachen University"]},{"key":"dc:relation","label":"Dc Relation","values":["info:eu-repo/semantics/altIdentifier/urn/urn:nbn:de:hbz:82-opus-19861"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis","info:eu-repo/semantics/publishedVersion"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["info:eu-repo/classification/ddc/620","Neuronales Netz","Thermisches Spritzen","Regler","Reglerentwurf","Ingenieurwissenschaften","Statistische Versuchsmethodik","DoE","Artificial Neural Network","Thermal Spraying","Controller","Design of Experiments"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["ger"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://publications.rwth-aachen.de/record/62425","https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123994%22"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The atmospheric plasma spraying process is established and provides cost reduction effects using qualified coatings. But the partly nonlinear and changing characteristics of this process are a challenge for process control.In this dissertation an approach is described, how a process control for atmospheric plasma spraying can be realised, using artificial neural networks. Base of this work is first the selection of a measurement system, the PFI-System, and second the choice of a control algorithm, the \"supervised control\". With the inductive procedure of Balzer an approach is described to define and optimise the artificial neural network. To train the artificial neural network design of experiments is used. With this a fast and targeted training can be done. An approach to update the artificial neural network regularly completes the theoretic research. By using a selected thermal spray process as a case study, the whole approach and the function of the controller is shown."]},{"key":"dc:source","label":"Dc Source","values":["Aachen : Publikationsserver der RWTH Aachen University VIII, 148 S. : graph. Darst. (2007). = Aachen, Techn. Hochsch., Diss., 2007"]},{"key":"dc:title","label":"Title","values":["Qualitätsmanagement und neuronale Netze - ein Ansatz zur prädiktiven Regelung thermischer Spritzprozesse"]}]}],"canonical_facts":{"dc:contributor":["Pfeifer, Tilo"],"dc:coverage":["DE"],"dc:creator":["Dören, Jens"],"dc:date":["2007"],"dc:description":["The atmospheric plasma spraying process is established and provides cost reduction effects using qualified coatings. But the partly nonlinear and changing characteristics of this process are a challenge for process control.In this dissertation an approach is described, how a process control for atmospheric plasma spraying can be realised, using artificial neural networks. Base of this work is first the selection of a measurement system, the PFI-System, and second the choice of a control algorithm, the \"supervised control\". With the inductive procedure of Balzer an approach is described to define and optimise the artificial neural network. To train the artificial neural network design of experiments is used. With this a fast and targeted training can be done. An approach to update the artificial neural network regularly completes the theoretic research. By using a selected thermal spray process as a case study, the whole approach and the function of the controller is shown."],"dc:identifier":["https://publications.rwth-aachen.de/record/62425","https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123994%22"],"dc:language":["ger"],"dc:publisher":["Publikationsserver der RWTH Aachen University"],"dc:relation":["info:eu-repo/semantics/altIdentifier/urn/urn:nbn:de:hbz:82-opus-19861"],"dc:rights":["info:eu-repo/semantics/openAccess"],"dc:source":["Aachen : Publikationsserver der RWTH Aachen University VIII, 148 S. : graph. Darst. (2007). = Aachen, Techn. Hochsch., Diss., 2007"],"dc:subject":["info:eu-repo/classification/ddc/620","Neuronales Netz","Thermisches Spritzen","Regler","Reglerentwurf","Ingenieurwissenschaften","Statistische Versuchsmethodik","DoE","Artificial Neural Network","Thermal Spraying","Controller","Design of Experiments"],"dc:title":["Qualitätsmanagement und neuronale Netze - ein Ansatz zur prädiktiven Regelung thermischer Spritzprozesse"],"dc:type":["info:eu-repo/semantics/doctoralThesis","info:eu-repo/semantics/publishedVersion"]},"updated_at":"2026-07-30T19:43:28Z"}