融合FBS的GenAI辅助产品创新设计框架研究

宗威; 白廷俊; 田晓夏; 姚君

doi:10.19554/j.cnki.1001-3563.2025.14.005

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包装工程（设计栏目） ›› 2025, Vol. 46 ›› Issue (14) : 46-57. DOI: 10.19554/j.cnki.1001-3563.2025.14.005

工业设计

融合FBS的GenAI辅助产品创新设计框架研究

宗威, 白廷俊^*, 田晓夏, 姚君

作者信息 +

Generative AI-assisted Product Innovation Design Framework Incorporating FBS

ZONG Wei, BAI Tingjun^*, TIAN Xiaoxia, YAO Jun

Author information +

文章历史 +

摘要

目的旨在深度整合生成式人工智能（Generative Artificial Intelligence,GenAI）和功能-行为-结构（Function-Behavior-Structure,FBS）模型来构建产品创新设计框架,填补GenAI辅助设计在功能-结构约束方面的空缺,强化设计师跨学科研究能力,提升创新设计的效率与合理性。方法采用理论驱动与案例分析相结合的方法,通过拓展FBS模型、剖析GenAI在设计任务中的效能,并引入相关基础研究结论,构建GenAI辅助设计框架并基于案例分析法证明该框架的可行性及有效性。结果案例分析表明,该框架能清晰地引导设计师使用GenAI完成创新设计,大幅缩短设计耗时并优化产品的功能及结构。结论该框架能够有效辅助产品创新设计,不仅能提升设计师的创新能力和设计效率;还显著优化了产品的功能结构合理性。

Abstract

The work aims to deeply integrate Generative Artificial Intelligence (GenAI) with the Function-Behavior- Structure (FBS) model to construct an product innovation design framework, so as to fill the gap in GenAI-assisted design in terms of function-structure constraints, enhance designers' interdisciplinary research capabilities, and improve the efficiency and rationality of innovation. Theory-driven analysis and case studies were combined. The FBS model was extended, GenAI's effectiveness in design tasks was analyzed, and relevant foundational research findings were incorporated to build the framework. Case analyses demonstrated that the framework could clearly guide designers in utilizing GenAI to achieve innovative designs, significantly reducing design time while optimizing both product functionality and structure. Ultimately, this framework not only boosts designers' innovation capacities and design efficiency but also markedly enhances the functional and structural rationality of products.

导出引用

宗威, 白廷俊, 田晓夏, 姚君. 融合FBS的GenAI辅助产品创新设计框架研究[J]. 包装工程（设计栏目）. 2025, 46(14): 46-57 https://doi.org/10.19554/j.cnki.1001-3563.2025.14.005

ZONG Wei, BAI Tingjun, TIAN Xiaoxia, YAO Jun. Generative AI-assisted Product Innovation Design Framework Incorporating FBS[J]. Packaging Engineering. 2025, 46(14): 46-57 https://doi.org/10.19554/j.cnki.1001-3563.2025.14.005

中图分类号： TB472

参考文献

[1] 李彦, 王杰, 李翔龙, 等. 创造性思维及计算机辅助产品创新设计研究[J]. 计算机集成制造系统-CIMS, 2003(12): 1092-1096.
LI Y, WANG J, LI X L, et al.Creative Thinking and Computer Aided Product Innovation[J]. Computer Integrated Manufacturing Systems, 2003(12): 1092-1096.
[2] KERSTEN W C, DIEHL J C, VAN ENGELEN J M L. Facing Complexity through Varying the Clarification of the Design Task[J]. FormAkademisk-Forskningstidsskrift for Design Og Designdidaktikk, 2018, 11(4): 1-28.
[3] SHI J Y, JAIN R, DOH H, et al. An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions[J]. ArXiv e-Prints, 2023: arXiv: 2310.07127.
[4] 白仲航, 张资恒, 李晨辉, 等. 功能-行为-结构(FBS)模型方法研究综述[J]. 图学学报, 2022, 43(5): 765-775.
BAI Z H, ZHANG Z H, LI C H, et al.Review on Research of FBS Model Method[J]. Journal of Graphics, 2022, 43(5): 765-775.
[5] WU J F, CAI Y T, SUN T Y, et al.Integrating AIGC with Design: Dependence, Application, and Evolution- a Systematic Literature Review[J]. Journal of Engineering Design, 2025, 36(5/6): 758-796.
[6] LEE S Y, LAW M, HOFFMAN G.When and how to Use AI in the Design Process? Implications for Human-AI Design Collaboration[J]. International Journal of Human-Computer Interaction, 2025, 41(2): 1569-1584.
[7] GOMEZ C, CHO S M, KE S C, et al.Human-AI Collaboration Is Not very Collaborative Yet: A Taxonomy of Interaction Patterns in AI-Assisted Decision Making from a Systematic Review[J]. Frontiers in Computer Science, 2025, 6: 1521066.
[8] ZHOU C Y, ZHANG X Y, YU C Y.How Does AI Promote Design Iteration? The Optimal Time to Integrate AI into the Design Process[J]. Journal of Engineering Design, 2023: 1-28.
[9] KIM J, MAHER M, SIDDIQUI S.Collaborative Ideation Partner: Design Ideation in Human-AI Co-Creativity[C]//Proceedings of the 5th International Conference on Computer-Human Interaction Research and Applications. Online Streaming, SCITEPRESS-Science and Technology Publications, 2021: 123-130.
[10] STIGE Å, ZAMANI E D, MIKALEF P, et al.Artificial Intelligence (AI) for User Experience (UX) Design: A Systematic Literature Review and Future Research Agenda[J]. Information Technology & People, 2024, 37(6): 2324-2352.
[11] GUO Z Y, ZHU Z Y, LI Y Z, et al.AI Assisted Fashion Design: A Review[J]. IEEE Access, 2023, 11: 88403-88415.
[12] 余隋怀, 王鹏超, 王磊, 等. 基于知识工程的产品设计研究综述[J]. 机械工程学报, 2024, 60(13): 216-234.
YU S H, WANG P C, WANG L, et al.Research Review of Product Design Based on Knowledge Engineering[J]. Journal of Mechanical Engineering, 2024, 60(13): 216-234.
[13] LI S P, TANG D B, WANG Q, et al.Optimal Scheme of Open Design Based on Co-Designer Involvement and the Dynamic FBSC Model[J]. Journal of Engineering Design, 2023, 34(3): 202-236.
[14] JIA J, ZHANG Y Z, et al.Knowledge Graph-Based Multi-Granularity Tacit Design Knowledge Reuse for Product Design[J]. Journal of Computational Design and Engineering, 2024, 12(1): 53-79.
[15] ZHANG P, JIANG N, LEE K Y, et al.RFWBS Model: Multilevel Hybrid Mapping Solution Framework for Designing Neurorehabilitation Physiotherapy Devices[J]. Machines, 2024, 12(1): 43.
[16] TARIQ S, BARUWAL CHHETRI M, NEPAL S, et al.A2C: A Modular Multi-Stage Collaborative Decision Framework for Human-AI Teams[J]. Expert Systems with Applications, 2025, 282: 127318.
[17] LI J R, CHANG T W, CHANG C C, et al.Cocobot: Developing a Consent Communication Tool Based on Co-Evolutionary Model[J]. Journal of Social Robotics, 2025, 17(3): 421-439.
[18] XU S Y, WEI Y, ZHENG P, et al.LLM Enabled Generative Collaborative Design in a Mixed Reality Environment[J]. Journal of Manufacturing Systems, 2024, 74: 703-715.
[19] ZHANG F.Design and Implementation of Industrial Design and Transformation System Based on Artificial Intelligence Technology[J]. Mathematical Problems in Engineering, 2022, 2022(1): 9342691.
[20] DE PEUTER S, OULASVIRTA A, KASKI S.Toward AI Assistants that Let Designers Design[J]. AI Magazine, 2023, 44(1): 85-96.
[21] 马进, 范明浩, 马良山, 等. 基于图文多模态融合推理的产品创新方案设计方法研究[J]. 包装工程, 2024, 45(8): 21-28.
MA J, FAN M H, MA L S, et al.Innovative Product Design Schemes Based on Image-Text Multi-Modal Fusion Reasoning[J]. Packaging Engineering, 2024, 45(8): 21-28.
[22] VERGANTI R, VENDRAMINELLI L, IANSITI M.Design in the Age of Artificial Intelligence[J]. Harvard Business Review, 2023, 101(4): 56-65.
[23] 刘建伟, 丁熙浩, 罗雄麟. 多模态深度学习综述[J]. 计算机应用研究, 2020, 37(6): 1601-1614.
LIU J W, DING X H, LUO X L.Survey of Multimodal Deep Learning[J]. Application Research of Computers, 2020, 37(6): 1601-1614.
[24] CROSS N.Design thinking: understanding how designers think and work[M]. Bloomsbury Publishing, 2023.
[25] ZHU Y, LI Z, WANG T, et al.Conditional Text Image Generation With Diffusion Models[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver: IEEE, 2023: 15894-15904.
[26] CHANDRASEGARAN S K, RAMANI K, SRIRAM R D, et al.The Evolution, Challenges, and Future of Knowledge Representation in Product Design Systems[J]. Computer-Aided Design, 2013, 45(2): 204-228.
[27] CHANG Y P, WANG X, WANG J D, et al.A Survey on Evaluation of Large Language Models[J]. ACM Transactions on Intelligent Systems and Technology, 2024, 15(3): 1-45.
[28] MIRZADEH I, ALIZADEH K, SHAHROKHI H, et al.GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models[A]. arXiv, 2024.
[29] LAWSON B.How Designers Think: The Design Process Demystified[M]. 4th ed. Oxford: Routledge, 2005.
[30] XU W. User-Centered Design (IX): A "User Experience 3.0" Paradigm Framework in the Intelligence Era[J]. CoRR, 2023: 06681.
[31] 杨大松, 闫朝华. 工业设计应用计算机辅助系统的探讨[J]. 中国制造业信息化, 2004, 33(6): 77-80.
YANG D S, YAN C H.Research on the Application of Computer-Aided Industry Design[J]. Mie of China, 2004, 33(6): 77-80.
[32] MA K, GRANDI D, MCCOMB C, et al. Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions[J]. arXiv preprint arXiv:2405. 02345, 2024
[33] 孔文强. 基于LLM Prompt技术的领域知识图谱构建框架研究[D]. 成都: 电子科技大学, 2024.
KONG W Q.Research on the Construction Framework of Domain Knowledge Map Based on LLM Prompt Technology[D]. Chengdu: University of Electronic Science and Technology of China, 2024.
[34] 王愫, 刘月林, 孙利. 面向AI生成的产品概念设计方案智能评估方法[J]. 计算机集成制造系统, 2025, 31(1): 20-34.
WANG S, LIU Y L, SUN L.Intelligent Evaluation Method of Product Concept Design Scheme for AI Generation[J]. Computer Integrated Manufacturing Systems, 2025, 31(1): 20-34.
[35] HU J E, SHEN Y L, WALLIS P, et al.LoRA: Low-Rank Adaptation of Large Language Models[J]. ICLR, 2022, 1(2): 3.
[36] CHAN S.Tutorial on Diffusion Models for Imaging and Vision[J]. Foundations and Trends® in Computer Graphics and Vision, 2024, 16(4): 322-471.
[37] ZHANG L M, RAO A Y, AGRAWALA M.Adding Conditional Control to Text-to-Image Diffusion Models[C]// 2023 IEEE/CVF International Conference on Computer Vision (ICCV). Paris, France. IEEE, 2023: 3813-3824.
[38] STRACKE N, BAUMANN S A, SUSSKIND J, et al.CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control and Altering of T2I Models[M]//Computer Vision-ECCV 2024. Cham: Springer Nature Switzerland, 2024: 87-103.