Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-24T23:31:31.454Z Has data issue: false hasContentIssue false
  • English
  • Français

Synergy and transitivity in constraint dominance methods: Demonstration with linear motor design problem

Published online by Cambridge University Press:  09 November 2006

ASHISH DESHPANDE  and
JAMES R. RINDERLE
ASHISH DESHPANDE
Affiliation:
Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, Massachusetts, USA
JAMES R. RINDERLE
Affiliation:
Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, Massachusetts, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Reasoning about relationships among design constraints can facilitate objective and effective decision making at various stages of engineering design. Exploiting dominance among constraints is one particularly strong approach to simplifying design problems and to focusing designers' attention on critical design issues. Three distinct approaches to constraint dominance identification have been reported in the literature. We lay down the basic principles of these approaches with simple examples, and we apply these methods to a practical linear electric actuator design problem. With the help of the design problem we demonstrate strategies to synergistically employ the dominance identification methods. Specifically, we present an approach that utilizes the transitive nature of the dominance relation. The identification of dominance provides insight into the design of linear actuators, which leads to effective decisions at the conceptual stage of the design. We show that the dominance determination methods can be synergistically employed with other constraint reasoning methods such as interval propagation methods and monotonicity analysis to achieve an optimal solution for a particular design configuration of the linear actuator. The dominance determination methods and strategies for their employment are amenable for automation and can be part of a suite of tools available to assist the designer in detailed as well as conceptual design.

Keywords

Conceptual DesignConstraint Dominance
Type
Research Article
Information
AI EDAM , Volume 20 , Issue 4 , November 2006 , pp. 383 - 397
Copyright
© 2006 Cambridge University Press

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.)

References

REFERENCES

Alfeld, G. & Herzberger, J. (1983). Introduction to Interval Computations. New York: Academic Press.
Basak, A. (1996). Permanent-Magnet DC Linear Motors. New York: Oxford Press.
Benhamou, F. & Granvilliers, L. (1996). Combining Local Consistency, Symbolic Rewriting and Interval Methods, Lecture Notes in Computer Science, Vol. 1138, p. 144. New York: Springer.
Benhamou, F., Goualard, F., Languenou, E., & Christie, M. (2000). An Algorithm To Compute Inner Approximations of Relations for Interval Constraints, Lecture Notes in Computer Science, Vol. 1755, pp. 416–423. New York: Springer.
Boldea, I. & Nasar, S.A. (1999). Linear electric actuators and generators. IEEE Transactions on Energy Conversion 14, 712–717.
Bordeaux, L., Monfroy, E., & Benhamou, F. (2003). Towards Automated Reasoning On The Properties Of Numerical Constraints. Constraints Solving and CLP, Lecture Notes in Artificial Intelligence, Vol. 2627, pp. 47–61. New York: Springer.
Bowen, J. (2003). Constraint Processing Offers Improved Expressiveness and Inference for Interactive Expert Systems. Constraints Solving and CLP, Lecture Notes in Artificial Intelligence, Vol. 2627, pp. 93–108. New York: Springer.
Bowen, J. & Bahler, D. (1992). Frames, quantification, perspectives, and negotiation in constraint networks in life-cycle engineering. International Journal of Artificial Intelligence in Engineering 7(2), 199–226.Google Scholar
Davis, E. (1987). Constraint propagation with interval labels. Artificial Intelligence 32(3), 281–331.Google Scholar
Deshpande, A.D. (2002). A study of methods to identify constraint dominance in engineering design problems. Master's thesis. University of Massachusetts, Amherst.
Deshpande, A.D. & Rinderle, J.R. (2001). Linear Electric Drive for UMM, Technical Report. Amherst, MA: University of Massachusetts.
Deshpande, A.D. & Rinderle, J.R. (2003). Constraint dominance methods applied to the design of a linear motor. ASME Int. Design Engineering Technical Conf.
Gieras, J.F. & Piek, Z.J. (1999). Linear Synchronous Motors: Transportation and Automation Systems. Boca Raton, FL: CRC Press.
Granvilliers, L., Goualard, F., & Benhamou, F. (1999). Box consistency through weak box consistency. Proc. Int. Conf. Tools With Artificial Intelligence, pp. 373–380.
Hansen, E. (2006). Sharpening interval computations. Reliable Computing 12(1), 21–34.Google Scholar
Moore, R.E. (1966). Interval Analysis. Englewood Cliffs, NJ: Prentice Hall.
O'Sullivan, B. (2002). Constraint-Aided Conceptual Design. London: Professional Engineering Publishing Limited.
Papalambros, P. & Wilde, D. (1979). Global non-iterative design optimization using monotonicity analysis. Journal of Mechanical Design 101, 645–649.
Papalambros, P. & Wilde, D. (1980). Regional monotonicity in optimal design. Journal of Mechanical Design 102, 497–500.
Papalambros, P. & Wilde, D. (1988). Principles of Optimal Design. New York: Cambridge University Press.
Rinderle, J.R. & Deshpande, A.D. (2003). Constraint dominance determination methods. ASME Design Theory and Methodology Conf.
Rinderle, J.R. & Deshpande, A.D. (in press). Constraint dominance identification methods. Journal of Mechanical Design.
Rinderle, J.R. & Krishnan, V. (1990). Constraint reasoning in concurrent design. ASME Design Theory and Methodology Conf., pp. 53–62.
Sarma, S.E. & Rinderle, J.R. (1991). Quiescence in interval propagation. ASME Design Theory and Methodology Conf., pp. 257–263.
Sarma, S.E. & Rinderle, J.R. (1992). Interval propagation: theory and methodology. ASME Design Theory and Methodology Conf., pp. 203–210.
Watton, J.D. & Rinderle, J.R. (1991a). Identifying reformulations of mechanical parametric design constraints. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 5(3), 173–188.Google Scholar
Watton, J.D. & Rinderle, J.R. (1991b). Improving mechanical design decisions with alternative formulations of constraints. Journal of Engineering Design 2(1), 55–68.Google Scholar
Wilde, D. (1975). Monotonicity and dominance in optimal hydraulic cylinder design. ASME Journal of Engineering for Industry 97(4), 1390–1394.Google Scholar