A computational approach to biologically inspired design

被引:29
|
作者
Nagel, Jacquelyn K. S. [1 ]
Stone, Robert B. [2 ]
机构
[1] James Madison Univ, Sch Engn, Harrisonburg, VA 22807 USA
[2] Oregon State Univ, Design Engn Lab, Dept Mech Ind & Mfg Engn, Sch Mech Ind & Mfg Engn, Corvallis, OR 97331 USA
基金
美国国家科学基金会;
关键词
Biomimicry; Concept Generation; Design; Function; FUNCTIONAL BASIS; REPRESENTATION; BIOMIMETICS; LANGUAGE;
D O I
10.1017/S0890060412000054
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The natural world provides numerous cases for analogy and inspiration in engineering design. During the early stages of design, particularly during concept generation when several variants are created, biological systems can be used to inspire innovative solutions to a design problem. However, identifying and presenting the valuable knowledge from the biological domain to an engineering designer during concept generation is currently a somewhat disorganized process or requires extensive knowledge of the biological system. To circumvent the knowledge requirement problem, we developed a computational approach for discovering biological inspiration during the early stages of design that integrates with established function-based design methods. This research defines and formalizes the information identification and knowledge transfer processes that enable systematic development of biologically inspired designs. The framework that supports our computational design approach is provided along with an example of a smart flooring device to demonstrate the approach. Biologically inspired conceptual designs are presented and validated through a literature search and comparison to existing products.
引用
收藏
页码:161 / 176
页数:16
相关论文
共 50 条
  • [41] A biologically inspired cognitive skills measurement approach
    Ahmad, Sadique
    Li, Kan
    Eddine, Hosni Adil Imad
    Khan, Muhammad Imran
    BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES, 2018, 24 : 35 - 46
  • [42] A Biologically Inspired Approach for Interactive Learning of Categories
    Kirstein, Stephan
    Wersing, Heiko
    2011 IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING (ICDL), 2011,
  • [43] FUEL OPTIMIZATION USING BIOLOGICALLY-INSPIRED COMPUTATIONAL MODELS
    Mora, Thamar E.
    Sesay, Abu B.
    Denzinger, Joerg
    Golshan, H.
    Poissant, G.
    Konecnik, C.
    IPC2008: PROCEEDINGS OF THE ASME INTERNATIONAL PIPELINE CONFERENCE - 2008, VOL 4, 2009, : 167 - 173
  • [44] A Biologically Inspired Approach for Robot Depth Estimation
    Martinez-Martin, Ester
    del Pobil, Angel P.
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2018, 2018
  • [45] A Biologically Inspired Computational Model of Basal Ganglia in Action Selection
    Baston, Chiara
    Ursino, Mauro
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2015, 2015
  • [46] A novel biologically inspired computational framework for visual tracking task
    Sokhandan, Alireza
    Monadjemi, Amirhassan
    BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES, 2016, 18 : 68 - 79
  • [47] A Biologically Inspired Computational Trust Model based on the Perspective of the Trustee
    Lygizou, Zoi
    Kalles, Dimitris
    PROCEEDINGS OF THE 12TH HELLENIC CONFERENCE ON ARTIFICIAL INTELLIGENCE, SETN 2022, 2022,
  • [48] Biologically-inspired image processing in computational retina models
    Melanitis, Nikos
    Nikita, Konstantina S.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2019, 113
  • [49] A SCALABLE APPROACH FOR THE INTEGRATION OF LARGE KNOWLEDGE REPOSITORIES IN THE BIOLOGICALLY-INSPIRED DESIGN PROCESS
    Vandevenne, D.
    Verhaegen, P. -A.
    Dewulf, S.
    Duflou, J. R.
    PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN (ICED 11): IMPACTING SOCIETY THROUGH ENGINEERING DESIGN, VOL 6: DESIGN INFORMATION AND KNOWLEDGE, 2011, 6 : 210 - 219
  • [50] Biologically-Inspired Iterative Learning Control Design: A Modular-Based Approach
    Hobson, Daniel
    Chu, Bing
    Cai, Xiaohao
    2024 UKACC 14TH INTERNATIONAL CONFERENCE ON CONTROL, CONTROL, 2024, : 175 - 176