[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127826-en":3,"doc-seo-127826-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127826,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Creation and configuration of hybrid machine learning models for process optimization in the series production of complex optics - Final report","In recent years, sophisticated technical optics and thin glasses have expanded across German growth markets in automotive, electronics, and technology. Complex serial production can introduce irregularities, making reliable quality control essential, especially during the hot forming of thin glass where direct measurement is difficult due to sensor limitations. Traditional machine-learning approaches often require trained data scientists and large volumes of high-quality data, challenging for small and medium enterprises. This thesis proposes hybrid models that combine machine learning with physics-based models, reducing dependency on extensive historical data and specialized personnel, to build an automated framework for creating and configuring hybrid structures.","FEDERAL UNIVERSITY OF SANTA CATARINA  \nTECHNOLOGY CENTER  \nAUTOMATION AND SYSTEMS DEPARTMENT UNDERGRADUATE COURSE IN CONTROL AND AUTOMATION ENGINEERING  \nLaura Battistella Fiorini  \nCreation and configuration of hybrid machine learning models for process optimization in the series production of complex optics  \nAachen, Germany  \nLaura Battistella Fiorini  \nCreation and configuration of hybrid machine learning models for process optimization in the series production of complex optics  \nFinal report of the subject DAS5511 (Course Final Project) as a Concluding Dissertation of the Undergraduate Course in Control and Automation Engineering of the Federal University of Santa Catarina. Supervisor: Prof. Marcelo Ricardo Stemmer, Dr.  \nCo-supervisor: Henrik Heymann, M.Sc. M.Eng.  \nAachen, Germany  \nFicha de identificação da obra elaborada pelo autor, através do Programa de Geração Automática da Biblioteca Universitária da UFSC.  \nFiorini, Laura Battistella  \nCreation and configuration of hybrid machine learning models for process optimization in the series production of complex optics / Laura Battistella Fiorini ; orientador, Marcelo Ricardo Stemmer, coorientador, Henrik Heymann, 2023.  \n74 p .  \nTrabalho de Conclusão de Curso (graduação) -Universidade Federal de Santa Catarina, Centro Tecnológico, Graduação em Engenharia de Controle e Automação, Florianópolis, 2023.  \nInclui referências .  \n1. Engenharia de Controle e Automação . 2. Modeloshíbridos . 3. Aprendizado de máquina . 4. Conformação aquente . 5. Modelagem de vidro . I . Stemmer, Marcelo Ricardo . II . Heymann, Henrik . III . Universidade Federal de Santa Catarina . Graduação em Engenharia de Controle e Automação . IV . Título .  \nLaura Battistella Fiorini  \nCreation and configuration of hybrid machine learning models for process optimization in the series production of complex optics  \nThis dissertation was evaluated in the context of the subject DAS5511 (Course Final Project) and approved in its final form by the Undergraduate Course in Control and  \nAutomation Engineering  \nFlorianópolis, June 30th, 2023 .  \nProf. Hector Bessa Silveira, Dr.  \nCourse Coordinator  \nExamining Board:  \nProf. Marcelo Ricardo Stemmer, Dr.  \nAdvisor  \nUFSC/CTC/DAS  \nHenrik Heymann, M.Sc. M.Eng.  \nSupervisor  \nFraunhofer Institute for Production Technology IPT  \nBruno Eduardo Benetti  \nEvaluator  \nSeazone  \nProf. Eduardo Camponogara, Dr.  \nBoard President  \nUFSC/CTC/DAS  \nACKNOWLEDGEMENTS  \nFirst, I would like to express my deepest gratitude to my parents, Daisy Maria Battistella Fiorini and Ademar Sérgio Fiorini, and my close family, for their persistent support and encouragement throughout my academic journey. Their belief in me has been the foundation of my success.  \nI would like to thank the BRAACHEN program coordination and Fraunhofer Institute for Production Technology IPT for the opportunity of this internship and all the precious knowledge that I have acquired during this year in Germany. Especially to Henrik Heymann, my local supervisor, for his guidance, expertise, and mentorship throughout the evolution of this work.  \nTo professor Marcelo Ricardo Stemmer, my academic advisor, thank you for the valuable insights and feedback provided in different stages of development.  \nTo all the friends I made during this year in Germany, thank you for making this experience even more memorable. Especially to my friends from the 4th floor HiWi room, Fabian, Fernando, Gustavo, Henrique, Luiza, Michel, Pedro, and Victor, I am extremely grateful for all the times we have helped each other and expressed our concerns, and, mostly, for sharing many laughs and happy memories.  \nFinally, I would like to thank my friends from UFSC: Gustavo, Juliana, Leonardo, Maurici, Maurício, Rhanna, Vinícius, and Yuri. I am deeply grateful for the cheerful moments we have spent together, for the inspiration you provide me, and also for the support we always gave each other in difficult times.  \nDISCLAIMER  \nAachen, June ","cbCaieumfpOoiVbv","https://ap.wps.com/l/cbCaieumfpOoiVbv","pdf",5431119,1,74,"English","en",105,"# Acknowledgements\n# Disclaimer\n# Abstract","[{\"question\":\"Why is quality control important in the series production of thin glass and complex optics?\",\"answer\":\"Serial production is prone to irregularities, and quality control is crucial during hot forming of thin glass where direct measurement is difficult using sensors.\"},{\"question\":\"What problem do traditional machine-learning approaches have for small and medium enterprises?\",\"answer\":\"They typically require trained data scientists and a significant amount of high-quality historical data, which are hard for SMEs to obtain.\"},{\"question\":\"How does this thesis address the data and expertise limitations?\",\"answer\":\"It introduces hybrid models that combine machine learning with physics-based models, aiming to reduce prerequisites related to historical data and specialized personnel by building an automated framework to create and configure hybrid structures.\"}]","Creation and configuration of hybrid machine learning models for process optimization in the series production of complex optics - 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