[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118381-en":3,"doc-seo-118381-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118381,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning for Assembly Modeling in Computer-Aided Design - Dissertation","Assembly modeling relies on CAD to create complex assemblies, yet designers face growing difficulty when manufacturers provide large, non-standardized part catalogs that must be searched among many options. This dissertation proposes a machine-learning-driven methodology that transfers expert knowledge from prior assemblies to assist inexperienced designers. It targets global and localized part recommendation and anomaly handling, using graph-based modeling, part embeddings derived from cross-assembly usage, and self-supervised learning instance generation plus synthetic anomalous assemblies.","Machine Learning for Assembly Modeling in Computer-Aided Design  \nDissertation  \nZur Erlangung des Doktorgrades Dr. rer. nat.  \nInstitut für Software & Systems Engineering  \nFakultät für Angewandte Informatik  \nUniversität Augsburg  \nCarola Anna Lenzen  \nMachine Learning for Assembly Modeling in Computer-Aided Design  \nReviewers: Prof. Dr. Wolfgang Reif  \nProf. Dr. Bernhard Bauer  \nProf. Dr. Alexander Schiendorfer  \nDay of Defense: December 09, 2024  \nI  \nAbstract  \nIn the domain of assembly modeling, design engineers utilize computer-aided design (CAD) software to develop complex assemblies. However, the increasing volume and size of non-standardized part catalogs from various manufacturers pose significant challenges in the selection of suitable parts from the vast number of options. This thesis addresses these challenges by proposing a novel methodology that leverages machine learning techniques to support inexperienced designers in the assembly design process by extracting expert knowledge from previous assemblies. Specifically, it investigates the use cases of global part recommendation, localized part recommendation and handling anomalies in assemblies.  \nThe first contribution is a generic, data-driven approach to extract patterns of proven part combinations across multiple real-world assemblies. The core methodology utilizes a graph-based representation of assemblies, wherein parts are represented as nodes and their connections as edges. Leveraging this representation, the methodology provides means and guidelines for modeling arbitrary part recommendation tasks by applying graph machine learning.  \nAs second contribution, we developed an embedding technique to learn the similarity of parts in terms of their usage across multiple assemblies. This technique adapts a method from the field of natural language processing to general graph structures. The resulting embeddings provide features for the parts in all learning tasks to enhance the models’generalization capabilities.  \nThe third contribution comprises an automated approach to generate learning instances for self-supervised machine learning from assembly data which can be tailored to a specific learning task on assemblies. This method addresses the scarcity of labeled data in real-world assembly datasets without involving domain experts. Moreover, this thesis introduces an algorithm for generating synthetic anomalous assemblies by extracting regular part combinations from a dataset of assemblies.  \nFinally, this thesis is the first to address part recommendation during assembly modeling by analyzing previous assemblies through machine learning. It proposes a general framework for a recommendation task, modeled as a classification problem to generate a fixed number of recommendations. The experimental results across all use cases demonstrate that machine learning-based methods can greatly enhance the efficiency of assembly modeling, improve knowledge transfer among engineers, and reduce time for perusing extensive part catalogs.  \nII  \nIII  \nAcknowledgments  \nFirst, I would like to express my gratitude to my doctoral supervisor Prof. Dr. Wolfgang Reif for his guidance and support throughout the years, the freedom he gave me in shaping my research topic and the excellent technical equipment he provided. I am also grateful for the critical questions in our discussions and the different perspectives he pointed out. In addition, I would like to thank Prof. Dr. Bernhard Bauer and Prof. Dr. Alexander Schiendorfer for their work as reviewers of this thesis.  \nI am very grateful to both Prof. Dr. Alexander Knapp and Dr. Gerhard Schellhorn for their contributions to the quality assurance of this thesis. I would like to thank Dr. Stefan Bodenmüller in particular for his persistent proofreading work. Additional thanks go to Dr. Constantin Wanninger for sharing his expertise in CAD design and his support in revising the figures of this thesis.  \nMany thanks to all former and cu","cbCaijAE26mYt8R7","https://ap.wps.com/l/cbCaijAE26mYt8R7","pdf",8984702,1,167,"English","en",105,"# 1 Introduction\n## 1.1 Challenges in Assembly Modeling\n## 1.2 Goals of this Thesis\n## 1.3 Research Project KOGNIA\n## 1.4 Scientific Contribution\n## 1.5 Thesis Outline\n# 2 Computer-Aided Design Foundations\n## 2.1 Part Modeling\n## 2.2 Assembly Modeling\n## 2.3 Enhancing Reusability in Assembly Modeling via Part Catalogs\n# 3 Data and Machine Learning Foundations\n## 3.1 Basic Concepts of Machine Learning\n## 3.1.1 Artificial Neural Networks\n## 3.1.2 Deep Learning","[{\"question\":\"What problem does the dissertation address in assembly modeling?\",\"answer\":\"Non-standardized and increasingly large part catalogs make it difficult to select suitable parts from many available options during assembly design.\"},{\"question\":\"How does the thesis model assemblies for machine learning?\",\"answer\":\"It uses a graph-based representation where parts are nodes and connections are edges, enabling graph machine learning for recommendation tasks.\"},{\"question\":\"What key contributions enable learning without heavy labeled data?\",\"answer\":\"It introduces an embedding technique for part similarity across assemblies and a self-supervised approach that generates tailored learning instances from assembly data, plus an algorithm for synthetic anomalous assemblies.\"}]","Machine Learning for Assembly Modeling in Computer-Aided Design - 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