Hostname: page-component-89b8bd64d-4ws75 Total loading time: 0 Render date: 2026-05-07T11:21:38.216Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling detailed design knowledge with the extended structure–behavior–function model

Published online by Cambridge University Press:  24 April 2013

Yong Chen ,
Jian Huang ,
Youbai Xie  and
Zhinan Zhang
Yong Chen*
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Jian Huang
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Youbai Xie
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Zhinan Zhang
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
*
Reprint requests to: Yong Chen, Room 838, School of Mechanical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai 200240, China. E-mail: mechenyong@sjtu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Detailed design is often a time-consuming and experience-dependent engineering process, where various detailed design knowledge can be reused. This paper proposes a formal approach for modeling detailed design knowledge for effective reuse. An extended structure–behavior–function model is developed for representing the structural, behavioral, and functional information in various life cycle periods of a detailed design. Based on the extended structure–behavior–function model, an issue- and solution-based approach is then developed to model the detailed knowledge of a mechanical design. The proposed approach is implemented in a detailed design knowledge modeling system, with a fixture design knowledge modeling as a brief example.

Keywords

Design RationaleDetailed Design KnowledgeMechanical DesignStructure–Behavior–FunctionTacit Knowledge

Information

Type
Technical Brief
Information
AI EDAM , Volume 27 , Issue 4 , November 2013 , pp. 415 - 420
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Baxter, D., Gao, J., Case, K., Harding, J., Young, B., & Cochrane, S. (2007). An engineering design knowledge reuse methodology using process modeling. Research in Engineering Design 18, 37–48.CrossRefGoogle Scholar
Bracewell, R., Wallace, K., Moss, M., & Knott, D. (2009). Capturing design rationale. Computer-Aided Design 41(3), 173–186.CrossRefGoogle Scholar
Chen, Y., Huang, J., & Xie, Y. (2012). Using part functions to capture various lifecycle requirements in detailed design. Proc. Int. Conf. Design Computing and Cognition, College Station, Texas, June 2012.Google Scholar
Chen, Y., Liu, Z., & Xie, Y. (2012). A knowledge-based framework for creative conceptual design of multi-disciplinary systems. Computer-Aided Design 44(2), 146–153.CrossRefGoogle Scholar
Fenves, S.J., Foufou, S., Bock, C., & Sriram, S.D. (2008). CPM2: a core model for product data. Journal of Computing and Information Sciences in Engineering 8, 014501.Google Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Goel, A.K., Rugaber, S., & Vattam, S. (2009). Structure, behavior and function of complex systems: the structure, behavior and function modeling language. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23, 23–35.CrossRefGoogle Scholar
Haug, A. (2012). The illusion of tacit knowledge as the great problem in the development of product configurators. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(1), 25–37.CrossRefGoogle Scholar
Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.Google Scholar
Maier, J.R.A., & Fadel, G.M. (2009). Affordance-based design: a relational theory for design. Research in Engineering Design 20, 13–27.CrossRefGoogle Scholar
Pahl, G., & Beitz, W. (2007). Engineering Design: A Systematic Approach. London: Springer-Verlag.CrossRefGoogle Scholar
Szykman, S., Racz, J., Bochenek, C., & Sriram, R.D. (2000). A web-based system for design artifact modeling. Design Studies 21, 145–165.CrossRefGoogle Scholar
Witherell, P., Krishnamurty, S., Grosse, I.R., & Wileden, J.C. (2010). Improved knowledge management through first-order logic in engineering design ontologies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, 245–257.CrossRefGoogle Scholar