[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125149-en":3,"doc-seo-125149-105":30,"detail-sidebar-cat-0-en-105":95},{"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},125149,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Investigating the Effects of a Machine-Learning-Powered Framework on Interactive Engineering Design - Thesis","A collaborative project with re:3D, Austin Habitat for Humanity, The University of Texas at Austin, and other partners develops a system enabling non-professionals to create custom 3D-printed furniture and home goods by upcycling local plastic waste. The work studies integrating an AI-powered interactive design framework that supports users in producing structurally sound designs without prior engineering or design expertise. It addresses gaps in understanding how people interpret and interact with machine-learning tools during guided design, using a fully co-creative environment and conditional cGAN-based representations.","Copyright by  \nChristopher Seong-Hwa Hong 2024  \nThe Thesis Committee for Christopher Seong-Hwa Hong Certifies that this is the approved version of the following Thesis:  \nInvestigating the Effects of a Machine-Learning-Powered Framework  \non Interactive Engineering Design  \nAPPROVED BY  \nSUPERVISING COMMITTEE:  \nCarolyn C. Seepersad, Supervisor  \nRichard H. Crawford  \nInvestigating the Effects of a Machine-Learning Powered Framework  \non Interactive Engineering Design  \nby  \nChristopher Seong-Hwa Hong  \nThesis  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nMaster of Science in Engineering  \nThe University of Texas at Austin  \nMay 2024  \nAcknowledgements  \nThis thesis would not have been possible without the guidance, feedback, and assistance of many. First and foremost, I am incredibly grateful to my advisor Professor Carolyn Seepersad for her mentorship and guidance. Professor Seepersad has been a supportive and creative mentor throughout this project and my time at the University of Texas at Austin.  \nI would also like to thank Sofia Valdez for being an incredibly open-minded and supportive partner throughout this project. Sofia played an important role in the success of this project, and collaborating with her has made it such an enjoyable experience.  \nI am grateful to my fellow lab mates and friends for their support and empathy as well. In particular, I would like to thank Francisco Gonzalez-Castillo and Riley Popp for transforming the cave into a fun study spot.  \nLastly, I would like to thank my parents for always showing their unconditional love and full support as I pursue studies in an unfamiliar field overseas.  \nAbstract  \nInvestigating the Effects of a Machine-Learning Powered Framework  \non Interactive Engineering Design  \nChristopher Seong-Hwa Hong, MSE  \nThe University of Texas at Austin, 2024  \nSupervisor: Carolyn C. Seepersad  \nIn a collaborative effort involving re:3D, Austin Habitat for Humanity (AHFH), The University of Texas at Austin, and other partners, our team is developing a system that provides an opportunity for non-professionals to create their own custom-made, 3D-printed furniture and home goods by upcycling plastic waste sourced from the local community. Within this process, our team is also exploring the integration of an AI-powered interactive design framework that allows customers to lead the design process of their own structurally sound home goods without any prior engineering or design expertise.  \nThe recent development of machine learning algorithms has led to the implementation of human-AI (artificial intelligence) co-creative systems in various sectors with the potential to transform the interactive design process. However, the way humans interpret and interact with machine learning tools and the effect of the tools on humanguided design processes are not fully understood yet. Recent research has attempted to quantify characteristics of generative AI models by conducting various user studies, but  \nthe focus has been on characterizing the model rather than analyzing the interaction patterns and behavior of users. Also, the user studies often employed simple GUIs that perform one task only, which is not sufficient to model realistic design processes.  \nIn this thesis, a preliminary user study is conducted with a fully co-creative environment in which all the searching and editing is driven by the user with the assistance of an automated system built on AI and specifically, design representations generated by conditional generative adversarial networks (cGANs) . As users design their own furniture and recreate a specific piece of furniture using the framework, we attempt to emulate an organic, iterative, and user-driven design process. Furthermore, we investigate user strategies and behavior when solving different design problems, changes in user preferences and tradeoffs associ","cbCaiso1etS0evXI","https://ap.wps.com/l/cbCaiso1etS0evXI","pdf",3902134,1,73,"English","en",105,"# List of Tables\n# List of Figures\n# Chapter 1 : Introduction\n# Chapter 2: Background & Related Work\n## 2.1 Valdez's Interactive Framework\n## 2.2 Interactive Design\n# Chapter 3: Interactive Design Framework\n## 3.1 Dataset Generation\n## 3.2 Passive Search Functions & Visualization\n## 3.3 Active Editing Functions","[{\"question\":\"What is the goal of the thesis project?\",\"answer\":\"The thesis develops and studies an AI-powered interactive design framework that lets non-professionals lead the design of structurally sound, custom 3D-printed furniture and home goods using upcycled plastic waste.\"},{\"question\":\"Why are existing user studies considered insufficient?\",\"answer\":\"Prior research often focuses on characterizing generative AI models rather than analyzing user interaction patterns, and it frequently uses simple single-task GUIs that do not reflect realistic design workflows.\"},{\"question\":\"How does the thesis conduct its user study?\",\"answer\":\"A preliminary user study is run in a fully co-creative environment where the user drives all searching and editing with an AI-assisted system using design representations generated by conditional generative adversarial networks (cGANs).\"},{\"question\":\"What aspects of user interaction does the thesis investigate?\",\"answer\":\"The thesis examines user strategies and behavior across different design problems, changes in user preferences and related tradeoffs, and how AI-powered searching and editing functions affect users’ convergence toward a final design.\"}]","Investigating the Effects of a Machine-Learning-Powered Framework on Interactive Engineering Design - 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