A Generative Comparative Study of Industrial Design Based on AIGC Tools and Deep Learning
With the rapid advancement of artificial intelligence-generated content (AIGC) technology, the field of industrial design is undergoing unprecedented transformations. By leveraging deep learning algorithms and generative models, AIGC tools can autonomously generate creative design solutions, offerin...
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| Vydáno v: | 2025 IEEE 3rd International Conference on Image Processing and Computer Applications (ICIPCA) s. 1619 - 1622 |
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IEEE
28.06.2025
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| Abstract | With the rapid advancement of artificial intelligence-generated content (AIGC) technology, the field of industrial design is undergoing unprecedented transformations. By leveraging deep learning algorithms and generative models, AIGC tools can autonomously generate creative design solutions, offering designers inspiration and accelerating product development. However, the practical impact of AIGC tools differs significantly from traditional design methods. This study aims to compare and analyze the performance of AIGC tools versus traditional industrial design techniques in the generative design process, with a focus on evaluating differences in creativity, efficiency, design quality, and adaptability. Currently, AIGC tools on the market exhibit notable variations in their generative characteristics, and they have been widely adopted in fields such as product design, architectural planning, packaging design, and graphic design. However, no AIGC tools are specifically tailored for industrial equipment styling. Each AIGC tool operates on distinct algorithms and generative features, resulting in different output when processing the same content. Therefore, selecting high-quality, accurate AIGC design tools for industrial equipment design remains a challenging problem that requires further exploration. |
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| AbstractList | With the rapid advancement of artificial intelligence-generated content (AIGC) technology, the field of industrial design is undergoing unprecedented transformations. By leveraging deep learning algorithms and generative models, AIGC tools can autonomously generate creative design solutions, offering designers inspiration and accelerating product development. However, the practical impact of AIGC tools differs significantly from traditional design methods. This study aims to compare and analyze the performance of AIGC tools versus traditional industrial design techniques in the generative design process, with a focus on evaluating differences in creativity, efficiency, design quality, and adaptability. Currently, AIGC tools on the market exhibit notable variations in their generative characteristics, and they have been widely adopted in fields such as product design, architectural planning, packaging design, and graphic design. However, no AIGC tools are specifically tailored for industrial equipment styling. Each AIGC tool operates on distinct algorithms and generative features, resulting in different output when processing the same content. Therefore, selecting high-quality, accurate AIGC design tools for industrial equipment design remains a challenging problem that requires further exploration. |
| Author | Yao, Xin Jiang, Yuhan |
| Author_xml | – sequence: 1 givenname: Yuhan surname: Jiang fullname: Jiang, Yuhan email: 1534721612@163.com organization: Universiti Sains Malaysia,Penang,Malaysia – sequence: 2 givenname: Xin surname: Yao fullname: Yao, Xin email: 474314594@qq.com organization: Guangxi Eco-engineering Vocational and Technical College,Liuzhou,China |
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| Snippet | With the rapid advancement of artificial intelligence-generated content (AIGC) technology, the field of industrial design is undergoing unprecedented... |
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| SubjectTerms | AIGC Artificial intelligence Deep learning Design tools generative contrast Graphics Image reconstruction Industrial design Packaging Planning Product design Product development Usability |
| Title | A Generative Comparative Study of Industrial Design Based on AIGC Tools and Deep Learning |
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