[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128384-en":3,"doc-seo-128384-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128384,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","PHYSICS-INFORMED MACHINE LEARNING FOR SMART DECISION-MAKING IN ULTRASONIC METAL WELDING - Dissertation","Ultrasonic metal welding (UMW) is a solid-state joining method with critical industrial use cases, yet it is sensitive to process variations and disturbances such as tool degradation and surface contamination, which narrow the operating window and degrade joint quality. This dissertation develops physics-informed machine learning methods to support smart decision-making for UMW, including multiobjective parameter optimization, online joint-strength prediction from sensing data, and robust adaptation or generalization under limited or absent new-scenario data.","© 2023 Yuquan Meng  \nPHYSICS-INFORMED MACHINE LEARNING FOR SMART DECISION-MAKING IN ULTRASONIC METAL WELDING  \nBY  \nYUQUAN MENG  \nDISSERTATION  \nSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mechanical Engineering in the Graduate College of the University of Illinois Urbana-Champaign, 2023  \nUrbana, Illinois  \nDoctoral Committee:  \nAssociate Professor Chenhui Shao, Chair  \nProfessor Placid M. Ferreira  \nProfessor Srinivasa M. Salapaka  \nAssociate Professor Pingfeng Wang  \nABSTRACT  \nUltrasonic metal welding (UMW) is a versatile solid-state joining technique with various important industrial applications, including lithium-ion battery assembly, automotive body construction, and electronic packaging. Among the advantages of UMW over conventional fusion welding techniques are the ability to join dissimilar metals, short welding cycles, energy efficiency, and environmental friendliness. Despite of its numerous advantages, UMWis sensitive to variations in process conditions and has a narrow operating window. Moreover, process disturbances, including tool degradation and workpiece surface contamination, negatively impact the UMW joint quality and robustness. As such, industrial-scale UMW production calls for smart decision-making, e.g., process optimization, joint quality assessment, maintenance, real-time control. To this end, this dissertation develops a suite of physics-informed machine learning methods for intelligent decision-making in UMW.  \nA machine learning-based response surface method is developed for multiobjective optimization of peel and shear joint strengths of UMW. Machine learning models are employed to characterize the response surfaces of peel and shear joint strengths, which are shown to have different patterns. Using the established response surface models, an optimal combination of process parameters is obtained to co-optimize peel and shear joint strengths.  \nA hierarchical physics-informed ensemble learning (PIEL) framework is developed to incorporate both physical knowledge and in-situ sensing data for accurate online prediction of UMW joint strength. This framework decomposes the joint strength variability into a physics-informed global trend and a data-driven residual, which are modeled hierarchically. It is shown that the PIEL framework improves physical interpretability and prediction accuracy.  \nA multi-functional few-shot learning (MF-FSL) approach is created to en-  \nable fast and cost-effective adaptation of online monitoring algorithms to new production scenarios with very limited data availability. MF-FSL utilizes model-agnostic meta-learning to learn and transfer the meta-knowledge between source and target domains. It is demonstrated that MF-FSL is effective in a variety of decision-making problems.  \nTo deal with extremely data-scarce cases, where no data is available in the new production scenario, a Similarity-based Meta-Representation Learning (SMRL) method is created for domain generalization. Compared with stateof-the-art methods, SMRL achieves significantly better generalizability and prediction performance. It is expected that SMRL will advance the generalizability, adaptability, and agility of decision-making algorithms, which are critically needed in modern and future manufacturing.  \nTo my family, for their love and support.  \niv  \nACKNOWLEDGMENTS  \nI humbly express my sincere gratitude and appreciation to my esteemed advisor, Prof. Chenhui Shao, for his invaluable support and guidance throughout my doctoral research. His expertise in the field of machine learning, data science and smart manufacturing, specifically ultrasonic metal welding, has been pivotal in shaping my research work, and his insights and feedback have been invaluable in helping me navigate the challenges of my research. I am extremely fortunate to have him as my mentor and grateful for the knowledge and skills that I have gained under his guidance. His unwavering suppo","cbCaiiPYClh331Xe","https://ap.wps.com/l/cbCaiiPYClh331Xe","pdf",15791189,3,1,126,"English","en",105,"# Table of Contents\n## Chapter 1 Introduction\n### 1.1 Research Background and Motivation\n### 1.2 Summary of Literature Review","[{\"question\":\"What key problem does the dissertation address in ultrasonic metal welding?\",\"answer\":\"UMW process conditions are highly sensitive, and disturbances like tool degradation and surface contamination can reduce joint quality within a narrow operating window, motivating smart decision-making approaches.\"},{\"question\":\"How does the dissertation approach multiobjective optimization of joint performance?\",\"answer\":\"It develops a machine learning-based response surface method to model peel and shear joint strength patterns, then determines an optimal process-parameter combination to co-optimize both strengths.\"},{\"question\":\"How does the dissertation enable online prediction and adaptation with limited data?\",\"answer\":\"It proposes a hierarchical physics-informed ensemble learning framework for accurate online joint-strength prediction using physical knowledge and sensing data, and introduces few-shot learning and similarity-based meta-representation learning for adaptation and domain generalization under scarce or missing new-scenario data.\"}]","PHYSICS-INFORMED MACHINE LEARNING FOR SMART DECISION-MAKING IN ULTRASONIC METAL WELDING - 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