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Article type: Research Article
Authors: Chhabra, Robina | Reza Emami, M.b; c; *
Affiliations: [a] Department of Mathematics and Statistics, University of Calgary, AB, Canada | [b] Institute for Aerospace Studies, University of Toronto, Toronto, ON, Canada | [c] Division of Space Technology, Luleå University of Technology, Kiruna, Sweden
Correspondence: [*] Correspondence to: M. Reza Emami, Institute for Aerospace Studies, University of Toronto, 4925 Dufferin St., Toronto, ON, M3H 5T6, Canada. Tel.: +1 416 946 3357; Fax: +1 416 946 7109;E-mail: emami@utias.utoronto.ca, reza.emami@ltu.se
Abstract: This paper outlines a concurrent design methodology for multidisciplinary systems, which employs tools of fuzzy theory for the tradeoff in the design space. This methodology enhances communication between designers from various disciplines through introducing the universal notion of satisfaction and expressing the behaviour of multidisciplinary systems using the notion of energy. It employs fuzzy rule-bases, membership functions and parametric connectives in fuzzy logic to formalize subjective aspects of design, resulting in a two-phase simplification of the multi-objective constrained optimization of a design process. The methodology attempts to find a pareto-optimal solution for the design problem. In the primary phase of the methodology, a fuzzy-logic model is utilized to identify a region in the design space that contains the pareto-optimal design state, and a proper initial state is suggested for the optimization in the secondary phase, where the pareto-optimal solution is found. Finally, the impact of the designer’s subjective attitude on the design is adjusted based on a system performance by utilizing an energy-based model of multidisciplinary systems. As an application, it is shown that the design of a five-degree-of-freedom industrial robot manipulator can be enhanced by using the methodology.
Keywords: Fuzzy connectives, fuzzy logic, concurrent design, multidisciplinary systems
DOI: 10.3233/IFS-141321
Journal: Journal of Intelligent & Fuzzy Systems, vol. 28, no. 5, pp. 1985-2001, 2015
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