DesignFusion: Integrating Generative Models for Conceptual Design Enrichment

Author:

Chen Liuqing1,Jing Qianzhi1,Tsang Yixin11,Wang Qianyi22,Sun Lingyun3,Luo Jianxi4

Affiliation:

1. Zhejiang University College of Computer Science and Technology, , Hangzhou 310058 , China

2. Zhejiang University School of Software Technology, , Ningbo 315048 , China

3. Zhejiang University International Design Institute, , Hangzhou 310058 , China

4. City University of Hong Kong Data-Driven Innovation Laboratory, , Hong Kong 999077 , China

Abstract

Abstract Conceptual design is a pivotal phase of product design and development, encompassing user requirement exploration and informed solution generation. Recent generative models with their powerful content generation capabilities have been applied to conceptual design to support designers’ ideation. However, the lack of transparency in their generation process and the shallow nature of their generated solutions constrain their performance in complex conceptual design tasks. In this study, we first introduce a conceptual design generation approach that combines generative models with classic design theory. This approach decomposes the conceptual design task based on design process and design attributes, and uses the who, what, where, when, why, how (5W1H) method, function-behavior-structure model, and Kansei Engineering to guide generative models to generate conceptual design solutions through multi-step reasoning. Then we present an interactive system using a mind-map layout to visualize multi-step reasoning, called DesignFusion. This empowers designers to track the generation process and control inputs/outputs at each reasoning step. Two user studies show that our approach significantly enhances the quality of generated design solutions and enriches designer experience in human–artificial intelligence co-creation.

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Publisher

ASME International

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Empowering design innovation using AI-generated content;Journal of Engineering Design;2024-09-13

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