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A reuse oriented representation model for capturing and formalizing the evolving design rationale

Published online by Cambridge University Press:  18 October 2013

Jihong Liu  and
Xujie Hu
Jihong Liu*
Affiliation:
School of Mechanical Engineering and Automation, Beihang University, Beijing, China
Xujie Hu
Affiliation:
School of Mechanical Engineering and Automation, Beihang University, Beijing, China
*
Reprint requests to: Jihong Liu, School of Mechanical Engineering and Automation, Beihang University, No. 37, Xueyuan Road, Haidian District, 100191, Beijing, China. E-mail: ryukeiko@buaa.edu.cn
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Abstract

Design rationale (DR) explains why an artifact is designed the way it is. An explicit representation of DR is helpful to designers, allowing them to understand, improve, and reuse previous designs. The argumentation-based representation is the mainstream approach to DR representation. It has a semiformal graphical format to depict the structure of arguments for solving a design problem. This paper argues that because the design is not just a problem-solving process but also a cognitive activity that is continuously iterative and evolving, the conventional argumentation-based representation of DR has some inherent limitations. An improved, intent-driven representation model is proposed to capture and formalize the DR and its evolving history to support DR reuse. The model's knowledge structure, consisting of DR elements and their relationships, is detailed. A preliminary knowledge representation of the model based on Web Ontology Language is introduced. Furthermore, the context of DR is defined to document the complete DR and support effective traceability of design thinking. A graphical DR modeling system is developed, and an example is demonstrated to verify the system's application and the effectiveness of the proposed representation model. The paper provides an effective method to retain and manage a designer's implicit design knowledge, which has the potential to significantly improve the integrated management of product development knowledge.

Keywords

Design RationaleDesign ReuseKnowledge ManagementKnowledge Representation Model

Information

Type
Regular Articles
Information
AI EDAM , Volume 27 , Issue 4 , November 2013 , pp. 401 - 413
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Arai, E., Okada, K., & Iwata, K. (1992). Intention modeling with product model and knowledge in design process. Proc. IFIP TC5/WG5.3 8th Int. PROLAMAT Conf. Human Aspects in Computer Integrated Manufacturing, pp. 271–281. Amsterdam: North-Holland.Google Scholar
Arai, E., Shirase, K., & Wakamatsu, H. (1998). CAD with use of designer's intention. Proc. 3rd IFIP Working Group 5.2 Workshop on Knowledge Intensive CAD, pp. 21–30.Google Scholar
Baxter, D., & Gao, J. (2007). An engineering design knowledge reuse methodology using process modelling. Research in Engineering Design 18(1), 37–48.CrossRefGoogle Scholar
Bracewell, R., Ahmed, S., & Wallace, K. (2004). DRed and design folders, a way of capturing, storing, and passing on knowledge generated during design projects. Proc. ASME Design Automation Conf., September 28–October 2.CrossRefGoogle Scholar
Bracewell, R., Wallace, K., Moss, M., & Knott, D. (2009). Capturing design rationale. Computer-Aided Design 41(3), 173–186.CrossRefGoogle Scholar
Buckingham-Shum, S.J., & Hammond, N. (1994). Argumentation-based design rationale: what use at what cost? Human–Computer Interaction 40(4), 603–652.Google Scholar
Burge, J.E., & Brown, D.C. (2000). Reasoning with design rationale. Artificial Intelligence in Design ’00 (Gero, J., Ed.), pp. 611–629. Amsterdam: Kluwer Academic.CrossRefGoogle Scholar
Burge, J.E., & Brown, D.C. (2008). Software engineering using RATionale. Journal of Systems and Software 81(3), 395–413.CrossRefGoogle Scholar
Carcia, A.C.B., & Howard, C.H. (1992). Acquiring design knowledge through design decision justification. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 6(1), 59–71.Google Scholar
Conklin, J. (2005). Dialogue Mapping: Building Shared Understanding of Wicked Problems. New York: Wiley.Google Scholar
De Medeiros, A.P., & Schwabe, D. (2008). Kuaba approach: integrating formal semantics and design rationale representation to support design reuse. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22(4), 399–419.CrossRefGoogle Scholar
Friedman-Hill, E. (2003). Jess in Action: Rule-Based Systems in Java. New York: Manning.Google Scholar
Ganeshan, R., Garrett, J., & Finger, S. (1994). A framework for representing design intent. Design Studies 15(1), 59–84.CrossRefGoogle Scholar
Gero, J.S., & Kannengiesser, U. (2004). The situated function–behaviour–structure framework. Design Studies 25(4), 373–391.CrossRefGoogle Scholar
Hatchuel, A., & Weil, B. (2009). C-K design theory: an advanced formulation. Research in Engineering Design 19(4), 181–192.CrossRefGoogle Scholar
Hooey, B.L., & Foyle, D.C. (2007). Requirements for a design rationale capture tool to support NASA's complex systems. Proc. Int. Workshop on Managing Knowledge for Space Missions, Pasadena, CA, July 17–19.Google Scholar
Kim, J., Pratt, M.J., Iyer, R.G., & Sriram, R.D. (2008). Standardized data exchange of CAD models with design intent. Computer-Aided Design 40(7), 760–777.CrossRefGoogle Scholar
Kunz, W., & Rittel, H.W.J. (1970). Issues as elements of information systems, Working Paper 131. Stuttgart: University of Stuttgart, Institute of Urban and Regional Development.Google Scholar
Lee, J. (1989). Decision representation language (DRL) and its support environment. Boston: MIT AI Lab.Google Scholar
Lee, J. (1997). Design rationale systems: understanding the issues. IEEE Expert 12(3), 78–85.CrossRefGoogle Scholar
Leveson, N. (2001). Systemic factors in software-related spacecraft accidents. Proc. AIAA Space 2001 Conf. and Exposition, Paper AIAA 2001-4763, Reston, VA.CrossRefGoogle Scholar
Li, M., Langbein, F.C., & Martin, R.R. (2010). Detecting design intent in approximate CAD models using symmetry. Computer-Aided Design 42(3), 183–201.CrossRefGoogle Scholar
Liu, J.H., & Sun, Z.Y. (2008). Representing design intents for design thinking process modeling. Proc. Int. Conf. Advanced Design and Manufacture. Sanya, China: Springer.Google Scholar
Liu, Y., Liang, Y., Kwong, C.K., & Lee, W.B. (2010). A new design rationale representation model for rationale mining. Journal of Computing and Information Science in Engineering 10(3), 031009.CrossRefGoogle Scholar
MacLean, A., Young, R., Belloti, V., & Moran, T. (1991). Questions, options, and criteria: elements of design space analysis. Human–Computer Interaction 6(3), 201–250.CrossRefGoogle Scholar
McCall, R.J. (1991). PHI: a conceptual foundation for design hypermedia. Design Studies 12(1), 30–41.CrossRefGoogle Scholar
Nirenburg, S., & Raskin, V. (2003). Ontological Semantics. New York: MIT Press.Google Scholar
Pedgley, O. (2007). Capturing and analyzing own design activity. Design Studies 28(5), 468–483.CrossRefGoogle Scholar
Regli, W.C., Hu, X., Atwood, M., & Sun, W. (2000). A survey of design rationale systems: approaches, representation, capture and retrieval. Engineering With Computers 16(3), 209–235.CrossRefGoogle Scholar
Rittel, H.W.J., & Webber, M.M. (1973). Planning problems are wicked problems. Policy Science 4, 155–169.CrossRefGoogle Scholar
Rockwell, J.A., Grosse, I.R., & Krishmanurty, S. (2010). A semantic information model for capturing and communicating design decisions. Journal of Computing and Information Science in Engineering 10(3), 1–8.CrossRefGoogle Scholar
Shipman, F.M. III, & McCall, R.J. (1997). Integrating different perspectives on design rationale: supporting the emergence of design rationale from design communication. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11(2), 141–154.CrossRefGoogle Scholar
Shum, S., Selvin, A., Sierhuis, M., Conklin, J., Haley, C., & Nuseibeh, B. (2006). Hypermedia support for argumentation-based rationale. In Rationale Management in Software Engineering (Dutoit, A.H., McCall, R., Mistrik, I., & Paech, B., Eds.), pp. 111–132. Amsterdam: Springer.CrossRefGoogle Scholar
Sim, S.K., & Duffy, A.H.B. (2003). Towards an ontology of generic engineering design activities. Research in Engineering Design 14(4), 200–223.CrossRefGoogle Scholar
Tang, A., Jin, Y., & Han, J. (2007). A rationale-based architecture model for design traceability and reasoning. Journal of Systems and Software 80(6), 918–934.CrossRefGoogle Scholar
Tang, M.X., & Frazer, J. (2001). A representation of context for computer supported collaborative design. Automation in Construction 10(6), 715–729.CrossRefGoogle Scholar
Ullman, D.G. (2002). Toward the ideal mechanical engineering design support system. Research in Engineering Design 13, 55–64CrossRefGoogle Scholar
Ullman, D.G., Dietterich, P.G., & Stauffer, L.A. (1988). A model of the mechanical design process based on empirical data. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2(1), 33–52.CrossRefGoogle Scholar