The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning

被引:1
作者
Mo, Zhenchong [1 ]
Gong, Lin [1 ,2 ]
Zhu, Mingren [1 ]
Lan, Junde [1 ]
机构
[1] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Yangtze Delta Reg Acad, Jiaxing 314000, Peoples R China
基金
中国国家自然科学基金;
关键词
design cognition; engineering design; generative design; generic-field design; knowledge-based design; product design; TOPIC ATTENTION; NEURAL-NETWORKS; GO; SHOGI; CHESS; GAME;
D O I
10.3390/su16229841
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Large language model (LLM) and Crowd Intelligent Innovation (CII) are reshaping the field of engineering design and becoming a new design context. Generative generic-field design can solve more general design problems innovatively by integrating multi-domain design knowledge. However, there is a lack of knowledge representation and design process model in line with the design cognition of the new context. It is urgent to develop generative generic-field design methods to improve the feasibility, innovation, and empathy of design results. This study proposes a method based on design cognition and knowledge reasoning. Firstly, through the problem formulation, a generative universal domain design framework and knowledge base are constructed. Secondly, the knowledge-based discrete physical structure set generation method and system architecture generation method are proposed. Finally, the application tool Intelligent Design Assistant (IDA) is developed, verified, and discussed through an engineering design case. According to the design results and discussion, the design scheme is feasible and reflects empathy for the fuzzy original design requirements. Therefore, the method proposed in this paper is an effective technical scheme of generative generic-field engineering design in line with the design cognition in the new context.
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收藏
页数:34
相关论文
共 122 条
  • [1] Discovering a Failure Taxonomy for Early Design of Complex Engineered Systems Using Natural Language Processing
    Andrade, Sequoia R.
    Walsh, Hannah S.
    [J]. JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2023, 23 (03)
  • [2] Cross-domain representation learning by domain-migration generative adversarial network for sketch based image retrieval
    Bai, Cong
    Chen, Jian
    Ma, Qing
    Hao, Pengyi
    Chen, Shengyong
    [J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2020, 71 (71)
  • [3] Probabilistic Topic Models
    Blei, David M.
    [J]. COMMUNICATIONS OF THE ACM, 2012, 55 (04) : 77 - 84
  • [4] Latent Dirichlet allocation
    Blei, DM
    Ng, AY
    Jordan, MI
    [J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) : 993 - 1022
  • [5] Blei DM., 2005, Advances in Neural Information Processing Systems, P147
  • [6] Blei DM., 2006, ACM International Conference Proceeding Series, P113, DOI 10.1145/1143844.1143859
  • [7] Fast unfolding of communities in large networks
    Blondel, Vincent D.
    Guillaume, Jean-Loup
    Lambiotte, Renaud
    Lefebvre, Etienne
    [J]. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2008,
  • [8] Boyd-Graber J., 2010, P 2010 C EMP METH NA
  • [9] Improved prediction of protein-protein interactions using AlphaFold2
    Bryant, P.
    Pozzati, G.
    Elofsson, A.
    [J]. NATURE COMMUNICATIONS, 2022, 13 (01)
  • [10] A function-oriented biologically analogical approach for constructing the design concept of smart product in Industry 4.0
    Cao, Guozhong
    Sun, Yindi
    Tan, Runhua
    Zhang, Jinpu
    Liu, Wei
    [J]. ADVANCED ENGINEERING INFORMATICS, 2021, 49