Research on design forms based on artificial intelligence collaboration model
With the advent of the era of great intersection and integration, the development of generative artificial intelligence has caused the renewal of design methods, promoting a new paradigm of research in design fundamentals. The study seeks to investigate the research method of design form in the coll...
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description | With the advent of the era of great intersection and integration, the development of generative artificial intelligence has caused the renewal of design methods, promoting a new paradigm of research in design fundamentals. The study seeks to investigate the research method of design form in the collaborative mode of artificial intelligence, to provide new ideas for design to conduct interdisciplinary research, and to promote design innovation under AI collaboration. This research begins with the design morphology theory, integrates interdisciplinary theories such as bionic design, and topology research, and collaborates with AIGC tools such as Midjourney, Stable Diffusion, and Chilloutmix to conduct case-specific research. To improve the accuracy of the morphological study, parametric design, bi-directional progressive topology optimization, genetic algorithm and simulation analysis, and other methods were also used in the research process to carry out a comprehensive design experiment exploration. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. AI synergy provides new ideas for complex morphology research, extends and complements design, and plays a crucial role in the phases of morphology exploration, concept generation, and solution implementation, thereby assisting in the exploration of the central content of design morphology. |
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This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. 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Published by Informa UK Limited, trading as Taylor & Francis Group 2024</rights><rights>2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). 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This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. 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Yin, Hu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c282t-d2104f7fd5995d90a065c9fec0141f3f5e620fe699ad8c375d949bf6e69b22953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>AIGC</topic><topic>Artificial Intelligence</topic><topic>Autonomous underwater vehicles</topic><topic>Bionics</topic><topic>CAD CAE CAM - Computing & Information Technology</topic><topic>co-design</topic><topic>Collaboration</topic><topic>Design</topic><topic>Design engineering</topic><topic>design morphology</topic><topic>Design optimization</topic><topic>Generative artificial intelligence</topic><topic>Genetic algorithms</topic><topic>Industrial design</topic><topic>Industrial Engineering & Manufacturing</topic><topic>Interdisciplinary aspects</topic><topic>Interdisciplinary studies</topic><topic>Jenhui Chen, Chang Gung University, Taiwan</topic><topic>Mechanical Engineering Design</topic><topic>Morphology</topic><topic>Research methodology</topic><topic>Topology optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Zipeng</creatorcontrib><creatorcontrib>Yin, Hu</creatorcontrib><collection>Access via Taylor & Francis (Open Access Collection)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Cogent engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Zipeng</au><au>Yin, Hu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Research on design forms based on artificial intelligence collaboration model</atitle><jtitle>Cogent engineering</jtitle><date>2024-12-31</date><risdate>2024</risdate><volume>11</volume><issue>1</issue><issn>2331-1916</issn><eissn>2331-1916</eissn><abstract>With the advent of the era of great intersection and integration, the development of generative artificial intelligence has caused the renewal of design methods, promoting a new paradigm of research in design fundamentals. 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This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. AI synergy provides new ideas for complex morphology research, extends and complements design, and plays a crucial role in the phases of morphology exploration, concept generation, and solution implementation, thereby assisting in the exploration of the central content of design morphology.</abstract><cop>Abingdon</cop><pub>Cogent</pub><doi>10.1080/23311916.2024.2364051</doi><oa>free_for_read</oa></addata></record> |
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subjects | AIGC Artificial Intelligence Autonomous underwater vehicles Bionics CAD CAE CAM - Computing & Information Technology co-design Collaboration Design Design engineering design morphology Design optimization Generative artificial intelligence Genetic algorithms Industrial design Industrial Engineering & Manufacturing Interdisciplinary aspects Interdisciplinary studies Jenhui Chen, Chang Gung University, Taiwan Mechanical Engineering Design Morphology Research methodology Topology optimization |
title | Research on design forms based on artificial intelligence collaboration model |
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