Closed-loop Optimization of AIGC-driven Product Design Based on Dynamic Preference Learning and Uncertainty Modeling

WANG Shence; WU Jing; NIU Hongsu

doi:10.19554/j.cnki.1001-3563.2026.14.015

PDF(5838 KB)

Packaging Engineering ›› 2026, Vol. 47 ›› Issue (14) : 162-180. DOI: 10.19554/j.cnki.1001-3563.2026.14.015

Industrial Design

Closed-loop Optimization of AIGC-driven Product Design Based on Dynamic Preference Learning and Uncertainty Modeling

WANG Shence, WU Jing^*, NIU Hongsu

Author information +

History +

Abstract

To address the issues of unstable evaluation, difficulty in dynamically adapting user preferences, and limited capability for continuous optimization in AIGC-driven product concept generation, the work aims to propose a preference-convergent closed-loop optimization framework for AIGC-driven product design. The Model for Uncertainty-aware Preference-driven AIGC Closed-loop Optimization (MUPA) was constructed, forming an end-to-end process of "generation-evaluation-preference learning-regeneration". In the evaluation stage, q-rung Orthopair Fuzzy Sets (q-ROF) were employed to characterize uncertainty in multi-source data, while Fuzzy-Weighted Zero-Inconsistency (FWZIC), Beta distribution, and Stochastic Multicriteria Acceptability Analysis (SMAA) were integrated to achieve robust weighting and probabilistic ranking. In the optimization stage, Bayesian dynamic preference learning and a difference-dimension prompt injection mechanism were introduced to enable iterative regeneration driven by user feedback. A case study on the appearance design of a handheld trenching machine demonstrated that the proposed method achieved rapid preference convergence within limited iterations, reliably identified superior solutions, expanded the design space, and improved ranking consistency. By integrating uncertainty modeling with dynamic preference learning, MUPA provides an interpretable and generalizable framework for continuous optimization in AIGC-assisted product design.

Key words

product design optimization / dynamic preference learning / uncertainty modeling / AIGC / closed-loop optimization

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

WANG Shence, WU Jing, NIU Hongsu. Closed-loop Optimization of AIGC-driven Product Design Based on Dynamic Preference Learning and Uncertainty Modeling[J]. Packaging Engineering. 2026, 47(14): 162-180 https://doi.org/10.19554/j.cnki.1001-3563.2026.14.015

References

[1] XIE B S, LI J X.Research on the Workability of AIGC Generated Pictures[J]. Progress in Social Sciences, 2024, 6(2): 424-441.
[2] HUANG K L, LIU Y C, DONG M Q, et al.Integrating AIGC into Product Design Ideation Teaching: An Empirical Study on Self-Efficacy and Learning Outcomes[J]. Learning and Instruction, 2024, 92: 101929.
[3] ZHU Z J, YU T, WANG Y J, et al.Revolutionizing Imaging Design Content with AIGC: User-Centered Challenges, Opportunities, and Workflow Evolution[J]. IEEE Access, 2025, 13: 87600-87620.
[4] SHEN X T, ZHANG R, ZHAO X Y, et al.PMG: Personalized Multimodal Generation with Large Language Models[C]// Proceedings of the ACM Web Conference 2024. Singapore: ACM, 2024: 3833-3843.
[5] ZHANG P, WANG H X, LI X D, et al.Research on Digital Characterization and Identification Process Model of Functional Genes for Intelligent Innovative Design[J]. Advanced Engineering Informatics, 2023, 56: 101983.
[6] LEE C H, CHEN C H, LI F, et al.Customized and Knowledge-Centric Service Design Model Integrating Case-Based Reasoning and TRIZ[J]. Expert Systems with Applications, 2020, 143: 113062.
[7] LU M L, SUN Y J, MA Z J.Multi-Objective Design Optimization of Multiple Energy Systems in Net/nearly Zero Energy Buildings under Uncertainty Correlations[J]. Applied Energy, 2024, 370: 123620.
[8] XIAO L M, HUANG G Q, ZHANG G B.Improved Assessment Model for Candidate Design Schemes with an Interval Rough Integrated Cloud Model under Uncertain Group Environment[J]. Engineering Applications of Artificial Intelligence, 2021, 104: 104352.
[9] ZHOU T T, CHEN Z H, MING X G.Multi-Criteria Evaluation of Smart Product-Service Design Concept under Hesitant Fuzzy Linguistic Environment: A Novel Cloud Envelopment Analysis Approach[J]. Engineering Applications of Artificial Intelligence, 2022, 115: 105228.
[10] TIWARI V, JAIN P K, TANDON P.An Integrated Shannon Entropy and TOPSIS for Product Design Concept Evaluation Based on Bijective Soft Set[J]. Journal of Intelligent Manufacturing, 2019, 30(4): 1645-1658.
[11] LAINWRIGHT N, PEMBERTON M. Assessing the Response Strategies of Large Language Models Under Uncertainty: A Comparative Study Using Prompt Engineering[EB/OL]. [2024-08-01](2025-12-11). https://www. researchgate.net/publication/382804673_Assessing_the_ Response_Strategies_of_Large_Language_Models_Under_ Uncertainty_A_Comparative_Study_Using_Prompt_Engineering.
[12] LAN L T H, HIEN D T T, THONG N T, et al. An ANP-TOPSIS Model for Tourist Destination Choice Problems under Temporal Neutrosophic Environment[J]. Applied Soft Computing, 2023, 136: 110146.
[13] SARWAR M, BASHIR F.Design Concept Evaluation Based on Cloud Rough Model and Modified AHP-VIKOR: An Application to Lithography Tool Manufacturing Process[J]. Advanced Engineering Informatics, 2024, 60: 102369.
[14] LIU P D, ZHU B Y, WANG P.A Weighting Model Based on Best-Worst Method and Its Application for Environmental Performance Evaluation[J]. Applied Soft Computing, 2021, 103: 107168.
[15] ALSALEM M A, ALAMOODI A H, ALBAHRI O S, et al.Evaluation of Trustworthy Artificial Intelligent Healthcare Applications Using Multi-Criteria Decision-Making Approach[J]. Expert Systems with Applications, 2024, 246: 123066.
[16] MOHAMMED R T, ZAIDAN A A, YAAKOB R, et al.Determining Importance of Many-Objective Optimisation Competitive Algorithms Evaluation Criteria Based on a Novel Fuzzy-Weighted Zero-Inconsistency Method[J]. International Journal of Information Technology & Decision Making, 2022, 21(1): 195-241.
[17] QAHTAN S, SHARIF K Y, ZAIDAN A A, et al.Novel Multi Security and Privacy Benchmarking Framework for Blockchain-Based IoT Healthcare Industry 4.0 Systems[J]. IEEE Transactions on Industrial Informatics, 2022, 18(9): 6415-6423.
[18] DEMIREL H O, GOLDSTEIN M H, LI X, et al.Human- Centered Generative Design Framework: An Early Design Framework to Support Concept Creation and Evaluation[J]. International Journal of Human-Computer Interaction, 2024, 40: 933-944.
[19] TSANG Y P, WU C H, LIN K Y, et al.Unlocking the Power of Big Data Analytics in New Product Development: An Intelligent Product Design Framework in the Furniture Industry[J]. Journal of Manufacturing Systems, 2022, 62: 777-791.
[20] CONG J C, CHEN C H, MENG X, et al.Conceptual Design of a User-Centric Smart Product-Service System Using Self-Organizing Map[J]. Advanced Engineering Informatics, 2023, 55: 101857.
[21] WANG Z X, LI X Y, ZHENG P, et al.Smart Product- Service System Configuration: A Novel Hypergraph Model-Based Approach[C]//2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). Hong Kong: IEEE, 2020: 323-328.
[22] XIE X, CAI X W, ZHOU J P, et al.A Semantic-Based Method for Visualizing Large Image Collections[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(7): 2362-2377.
[23] ROCHLIN D.Hope and Grit: How Human-Centered Product Design Enhanced Student Mental Health[J]. California Management Review, 2024, 66(2): 108-120.
[24] 尹虎, 殷莹熙彤, 陈殿生, 等. 基于AIGC协同工业设计流程的气道廓清辅具设计研究与实践[J]. 包装工程, 2024, 45(16): 51-65.
YIN H, YIN Y, CHEN D S, et al.Research and Practice of Airway Clearance Assistive Device Design Based on AIGC Collaborative Industrial Design Process[J]. Packaging Engineering, 2024, 45(16): 51-65.
[25] WANG Y, ZHONG W, LI L, et al. Aligning Large Language Models with Human: A Survey[EB/OL]. [2023- 07-24] (2025-12-11). https://arxiv.org/abs/2307.12966.
[26] LIN L, ZHANG S H, FU S, et al.FD-LLM: Large Language Model for Fault Diagnosis of Complex Equipment[J]. Advanced Engineering Informatics, 2025, 65: 103208.
[27] ZHANG K, LIN K Y, WANG J F, et al.UNISON Framework for User Requirement Elicitation and Classification of Smart Product-Service System[J]. Advanced Engineering Informatics, 2023, 57: 101996.
[28] LI Q X, YANG Y, LI C J, et al.Energy Vehicle User Demand Mining Method Based on Fusion of Online Reviews and Complaint Information[J]. Energy Reports, 2023, 9: 3120-3130.
[29] 卢裕弘, 封颖超杰, 朱琳, 等. PromptVis: 面向文本生成图片的提示词的交互式可视分析方法[J]. 计算机辅助设计与图形学学报, 2025, 37(4): 688-696.
LU Y H, FENG Y, ZHU L, et al.PromptVis: Prompt-Based Interactive Visual Analysis Method for Text-to-Image Creation[J]. Journal of Computer-Aided Design & Computer Graphics, 2025, 37(4): 688-696.
[30] 路鹏, 吴凡, 唐建. 基于人工智能生成内容的产品造型设计与评价方法[J]. 图学学报, 2024, 45(6): 1277-1288.
LU P, WU F, TANG J.Product Design and Evaluation Methods Based on AI-Generated Content[J]. Journal of Graphics, 2024, 45(6): 1277-1288.
[31] ABDEEN W, UNTERKALMSTEINER M, WNUK K. Auxiliary Artifacts in Requirements Traceability: A Systematic Mapping Study[EB/OL].2025: arXiv: 2504.19658. https://arxiv.org/abs/2504.19658
[32] SANCHEZ-LOZANO J M, MARTINEZ M F, LARRAZ M A. A Comparison Between Fuzzy Topsis and Vikor for the Selection of Aircraft for Airspace Defense[J]. International Journal of Information Technology & Decision Making, 2025, 25(2): 121-133.
[33] SHIDPOUR H, DA CUNHA C, BERNARD A.Group Multi-Criteria Design Concept Evaluation Using Combined Rough Set Theory and Fuzzy Set Theory[J]. Expert Systems with Applications, 2016, 64: 633-644.
[34] QI J, HU J, PENG Y H.Integrated Rough VIKOR for Customer-Involved Design Concept Evaluation Combining with Customers' Preferences and Designers' Perceptions[J]. Advanced Engineering Informatics, 2020, 46: 101138.
[35] AYDOĞAN S, GÜNAY E E, AKAY D, et al. Concept Design Evaluation by Using Z-Axiomatic Design[J]. Computers in Industry, 2020, 122: 103278.
[36] KUKREJA V.Hybrid Fuzzy AHP-TOPSIS Approach to Prioritizing Solutions for Inverse Reinforcement Learning[J]. Complex & Intelligent Systems, 2023, 9(1): 493-513.
[37] SARWAR M, BASHIR F.Design Concept Evaluation Based on Cloud Rough Model and Modified AHP-VIKOR: An Application to Lithography Tool Manufacturing Process[J]. Advanced Engineering Informatics, 2024, 60: 102369.
[38] SAHA A, MISHRA A R, RANI P, et al.A Q-Rung Orthopair Fuzzy FUCOM Double Normalization-Based Multi-Aggregation Method for Healthcare Waste Treatment Method Selection[J]. Sustainability, 2022, 14(7): 4171.
[39] DEVECI M, GOKASAR I, BRITO-PARADA P R. A Comprehensive Model for Socially Responsible Rehabilitation of Mining Sites Using Q-Rung Orthopair Fuzzy Sets and Combinative Distance-Based Assessment[J]. Expert Systems with Applications, 2022, 200: 117155.
[40] LIU V, QIAO H, CHILTON L.Opal: Multimodal Image Generation for News Illustration[C]//Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. Bend: ACM, 2022: 1-17.
[41] ZENG H P, WANG X B, WANG Y, et al.GestureLens: Visual Analysis of Gestures in Presentation Videos[J]. IEEE Transactions on Visualization and Computer Graphics, 2023, 29(8): 3685-3697.
[42] 牛晓霆, 刘祥源. 基于AIGC技术下圈椅数字化修复应用研究[J]. 家具与室内装饰, 2025, 32(3): 9-15.
NIU X T, LIU X Y.Research on the Digital Restoration and Application of Circle Chairs Based on AIGC Technology[J]. Furniture & Interior Design, 2025, 32(3): 9-15.
[43] YANG J, FAN J P, HUBBALL D, et al.Semantic Image Browser: Bridging Information Visualization with Automated Intelligent Image Analysis[C]//2006 IEEE Symposium on Visual Analytics Science and Technology. Baltimore: IEEE, 2006: 191-198.
[44] LI Z J, ZHANG Y, ZHOU S Z, et al.RealtimeGen: An Intervenable AI Image Generation System for Commercial Digital Art Asset Creators[J]. International Journal of Human-Computer Interaction, 2025, 41(11): 6613-6636.
[45] 缪翌, 张卫锋, 徐领. 基于CLIP的视频时刻检索预训练模型[J]. 计算机应用研究, 2024, 41(12): 3866-3872.
MIAO Y, ZHANG W F, XU L.Pre-Training Model for Video Moment Retrieval Based on CLIP[J]. Application Research of Computers, 2024, 41(12): 3866-3872.
[46] WANG Z, LI J S, PAN H R, et al.Research on Multimodal Generative Design of Product Appearance Based on Emotional and Functional Constraints[J]. Advanced Engineering Informatics, 2025, 65: 103106.
[47] HORVAT N, ŠKLEBAR J, ŠTORGA M, et al.Create or Revise? A Comparative Study on CAD Rework after Team-Based Engineering Design Review in Virtual Reality and Desktop Interface[J]. Advanced Engineering Informatics, 2025, 65: 103177.
[48] MARUKATAT S.Tutorial on PCA and Approximate PCA and Approximate Kernel PCA[J]. Artificial Intelligence Review, 2023, 56(6): 5445-5477.
[49] KURITA T. Principal Component Analysis (PCA)[M]// Computer Vision. Cham: Springer International Publishing, 2021: 1013-1016.
[50] YAGER R R.Generalized Orthopair Fuzzy Sets[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(5): 1222-1230.
[51] PELISSARI R, OLIVEIRA M C, BEN AMOR S, et al.SMAA Methods and Their Applications: A Literature Review and Future Research Directions[J]. Annals of Operations Research, 2020, 293(2): 433-493.
[52] LI D Y, ZHANG H X, DING H, et al.User- Preference-Learning-Based Proactive Edge Caching for D2D- Assisted Wireless Networks[J]. IEEE Internet of Things Journal, 2023, 10(13): 11922-11937.
[53] ZHANG T, CHEN C, WANG D, et al.A VAE-Based User Preference Learning and Transfer Framework for Cross- Domain Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(10): 10383-10396.
[54] WOIKE J K, HERTWIG R, GIGERENZER G.Heterogeneity of Rules in Bayesian Reasoning: A Toolbox Analysis[J]. Cognitive Psychology, 2023, 143: 101564.