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A framework for supporting multidisciplinary engineering design exploration and life-cycle design using underconstrained problem solving

Published online by Cambridge University Press:  09 November 2006

XIU-TIAN YAN  and
HIROYUKI SAWADA
XIU-TIAN YAN
Affiliation:
CAD Centre, Department of Design Manufacture & Engineering Management, University of Strathclyde, Glasgow, Scotland
HIROYUKI SAWADA
Affiliation:
Digital Manufacturing Research Centre, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan
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Abstract

The problem investigated in this research is that engineering design decision making can be complicated and made difficult by highly coupled design parameters and the vast number of design parameters. This complication often hinders the full exploration of a design solution space in order to generate optimal design solution. These hindrances result in inferior or unfit design solutions generated for a given design problem due to a lack of understanding of both the problem and the solution space. This research introduces a computational framework of a new algebraic constraint-based design approach aimed at providing a deeper understanding of the design problem and enabling the designers to gain insights to the dynamic solution space and the problem. This will enable designers to make informed decisions based on the insights derived from parameter relationships extracted. This paper also describes an enhanced understanding of an engineering design process as a constraint centered design. It argues that with more effort and appreciation of the benefits derived from this constraint-based design approach, engineering design can be advanced significantly by first generating a more quantitative product design specification and then using these quantitative statements as the basis for constraint-based rigorous design. The approach has been investigated in the context of whole product life-cycle design and multidisciplinary design, aiming to derive a generic constraint-based design approach that can cope with life-cycle design and different engineering disciplines. A prototype system has been implemented based on a constraint-based system architecture. The paper gives details of the constraint-based design process through illustrating a worked real design example. The successful application of the approach in two highly coupled engineering design problems and the evaluation undertaken by a group of experienced designers show that the approach does provide the designers with insights for better exploration, enabled by the algebraic constraint solver. The approach thus provides a significant step towards fuller scale constraint-based scientific design.

Keywords

Algebraic ConstraintsConstraint-Based DesignDesign InsightsGröbner BaseLife-Cycle Synthesis

Information

Type
Research Article
Information
AI EDAM , Volume 20 , Issue 4 , November 2006 , pp. 329 - 350
Copyright
© 2006 Cambridge University Press

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References

REFERENCES

Andreasen, M.M. (1991a). Design methodology, Journal of Engineering Design 2(4), 321–335.Google Scholar
Andreasen, M.M. (1991b). The theory of domains. Workshop on Understanding Function and Function to Form Evolution, University of Cambridge.
Andreasen, M.M. & Hein, L. (2000). Integrated Product Development; Institute for Product Development. Copenhagen: Technical University of Denmark.
Bahler, D., Dupont, C., & Bowen, J. (1994). An axiomatic approach that supports negotiated resolution of design conflicts in concurrent engineering. In Artificial Intelligence in Design '94 (Gero, J.G., Ed.), pp. 363–379. New York: Kluwer Academic.
Borg, J., Yan, X.T., & Juster, N.P. (2000). Exploring decision's influence on life-cycle performance to aid “design for multi-X.” Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14(2), 91–113.Google Scholar
Bowen, J. & Bahler, D. (1992). Frames, quantification, perspectives, and negotiation in constraint networks for life-cycle engineering. Artificial Intelligence in Engineering 7(2), 199–226.Google Scholar
Bowler, C., Hicks, B.J., Medland, A.J., & Mullineux, G. (2001). Assisted model development for kinematic analysis and optimisation of mechanical systems. Int. Conf. Engineering Design ICED01, Design Methods for Performance and Sustainability (Culley, S., Duffy, A., McMahon, C. & Wallace, K., Eds.), pp. 91–98, Glasgow.
Bracewell, R.H., Chaplin, R.V., Langdon, P., Li, M., Oh, V., Sharpe, J.E.E., & Yan, X.T. (1995). Integrated platform for AI support of complex design (Part 1) and (Part 2). In AI System Support for Conceptual Design, Int. Workshop on Engineering Design, Ambleside, UK, March 27–29.
Buchberger, B. (1985). Gröbner bases: an algorithmic method in polynomial ideal theory. In Multidimensional Systems Theory (Bose, N.K., Ed.), pp. 184–232. Dordrecht: Reidel.
Cox, D., Little, J., & O'Shea, D. (1997). Ideals, Varieties and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, 2nd ed. New York: Springer–Verlag.
Crow, K. (2002). Customer focused development with QFD. DRM Associates. Available on-line at http://www.npd-solutions.com/qfd.html
Deitel, H.M. & Deitel, P.J. (2005). C++ How to Program, 5th ed. Englewood Cliffs, NJ: Prentice–Hall.
Dym, C.L. & Little, P. (2000). Engineering Design: A Project-Based Introduction. New York: Wiley.
French, M. (1999). Conceptual Design for Engineers, 3rd ed. New York: Springer–Verlag.
Gao, X., Lin, Q., & Zhang, G. (2006). A C-tree decomposition algorithm for 2D and 3D geometric constraint solving. Computer-Aided Design 38(1), 1–13.Google Scholar
Imamura, S. (1992). FDL: a constraint based object oriented language for functional design. IFIP Transactions B (Applications in Technology) B-3, 227–236.
Imamura, S. (1995). Towards more flexible parametric design support by introducing transformable object oriented language. Computer Applications in Production and Engineering: Proc. CAPE '95 (Sun, Q.N., Tang, Z. & Zhang, Y., Eds.), pp. 548–55. London: Chapman & Hall.
Karnopp, D.C., Margolis, D.L., & Rosenberg, R.C. (1990). System Dynamics, A Unified Approach, 2nd ed. New York: Wiley.
Kopsch, J., Marx, P., & Lechner, G. (1997). Modelling engineering design knowledge with constraints. Proc. 3rd Int. Conf. Practical Application of Constraint Technology (PACT 97), pp. 213–224.
Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann.
Kuokka, D. & Livezey, B. (1994). A collaborative parametric design agent. Proc. 12th National Conf. Artificial Intelligence, Vol. 1, pp. 387–393.
Lottaz, C., Stalker, R., & Smith, I. (1998). Constraint solving and preference activation for interactive design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(1), 13–27.Google Scholar
Medland, A.J., Mullineux, G., Rentoul, A.H., & Twyman, B.R. (1996). A constraint-based approach to the design and optimisation of mechanism systems. IUTAM Symp. Optimisation of Mechanical Systems, pp. 205–212.
Mortensen, N.H. & Andreasen, M.M. (1996). Designing in an interplay with a product model—explained by design units. TMCE'96, Budapest, Hungary.
Mullins, S. & Anderson, D. (1998). Automatic identification of geometric constraints in mechanical assemblies. Computer-Aided Design 30, 715–726.
Navinchandra, D. (1991). Exploration and Innovation in Design: Towards a Computational Model. New York: Springer–Verlag.
Noro, M. & Takeshima, T. (1992). Risa/Asir—a computer algebra system. Proc. ISSAV'92, pp. 387–396.
O'Sullivan, B. (1999). Constraint-aided conceptual design. PhD Thesis, University College Cork.
O'Sullivan, B. (2002). Interactive constraint-aided conceptual design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16(4), 303–328.Google Scholar
Pahl, G. & Beitz, W. (1996). Engineering Design—A Systematic Approach. London: Springer–Verlag.
Petrie, C., Jeon, H., & Cutkosky, M.R. (1996). Combining Constraint Propagation and Backtracking for Distirbuted Engineering, Technical Report CDR-TR-19960813. Stanford, CA: Stanford University, Center for Design Research. Available on-line at http://cdr.stanford.edu/pub/CDR/Publications/Reports/constraint.html
Petrie, C., Webster, T.A., & Cutkosky, M.R. (1995). Using Pareto optimality to coordinate distributed agents. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9(4), 269–281.Google Scholar
Pugh, S. (1991). Total Design—Integrated Methods for Successful Product Engineering. Reading, MA: Addison–Wesley.
Rehman, F. & Yan, X.T. (2005). A prototype system to support conceptual design synthesis for Multi-X. Proc. 2005 14th Int. Conf. Engineering Design ICED05, Melbourne, Australia.
Roozenburg, N.F.M. & Eekels, J. (1995). Product Design: Fundamentals and Methods. West Sussex: Wiley.
Sawada, H. (1998). The algebraic under constrain solver as a design tool. Proc. 1998 IMACS Conf. Applications of Computer Algebra.
Sawada, H. (2001). Constraint-based computer support for insightful multidisciplinary engineering design. PhD Thesis, University of Strathclyde, Department of Design, Manufacture and Engineering Management.
Sawada, H. & Yan, X.T. (2000). Preliminary design support system based on a generic under-constraint solving technique. ASME 2000 Int. Design Engineering Technical Conf. and the Computers and Information in Engineering Conf. Baltimore, MD: ASME Design Automation.
Sawada, H. & Yan, X.T. (2004). Application of Gröbner bases and quantifier elimination for insightful engineering design. Mathematics and Computers in Simulation 67(1–2), 135–148.Google Scholar
Sekiya, T. & Tomiyama, T. (1997). Case studies of ontology for the knowledge intensive engineering framework. In Knowledge Intensive CAD (Mantyla, M., Finger, S. & Tomiyama, T., Eds.), Vol. 2, pp. 139–156. London: Chapman & Hall.
Sitharam, M., Oung, J., Zhou, Y., & Arbree, A. (2006). Geometric constraints within feature hierarchies. Computer-Aided Design 38(1), 22–38.Google Scholar
Stumptner, M., Friedrich, G., & Haselbock, A. (1998). Generative constraint-based configuration of large technical systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(4), 307–320.Google Scholar
Suh, N.P. (1990). The Principles of Design. New York: Oxford University Press.
Thornton, A.C. (1994). Genetic algorithms versus simulated annealing: satisfaction of large sets of algebraic mechanical constraints. Artificial Intelligence in Design '94 (Gero, J.G. & Sudweeks, F., Eds.), pp. 384–398. New York: Kluwer Academic.
Thornton, A.C. & Johnson, A. (1996). CADET: a software support tool for constraint processes in embodiment design. Research in Engineering Design 8(1), 1–13.Google Scholar
Weispfenning, V. (1997). Quantifier elimination for real algebra—the quadratic case and beyond. Applicable Algebra in Engineering, Communication and Computing 8(1), 85–101.Google Scholar
Yan, X.T. & Sharpe, J.E.E. (1996). Reasoning and truth maintenance of causal structures in interdisciplinary product modelling and simulation. AI System Support for Conceptual Design: Proc. 1995 Lancaster Int. Workshop on Engineering Design (Sharpe, J.E.E., Ed.), pp. 405–425. London: Springer–Verlag.