Abstract
In this study, an efficient methodology is proposed for robust design optimization by using preference function and fuzzy logic concepts. In this method, the experience of experts is used as an important source of information during the design optimization process. The case study in this research is wing design optimization of Boeing 747. Optimization problem has two objective functions (wing weight and wing drag) so that they are transformed into new forms of objective functions based on fuzzy preference functions. Design constraints include transformation of fuel tank volume and lift coefficient into new constraints based on fuzzy preference function. The considered uncertainties are cruise velocity and altitude, which Monte Carlo simulation method is used for modeling them. The non-dominated sorting genetic algorithm is used as the optimization algorithm that can generate set of solutions as Pareto frontier. Ultimate distance concept is used for selecting the best solution among Pareto frontier. The results of the probabilistic analysis show that the obtained configuration is less sensitive to uncertainties.
Highlights
Engineering design cycle is a process of the subsystems formulation to meet human needs
Two design optimizations have been done in this paper that includes: deterministic optimization, and robust optimization
The main purpose of this paper is to present an efficient method for robust design optimization in which wing design optimization is considered for implementation
Summary
Engineering design cycle is a process of the subsystems formulation to meet human needs. Taguchi introduces robust design at first to enhance product quality so that the quality is not sensitive to variations (Wan et al 2011). The purpose of this method is to minimize the deviation of system response due to uncertainties. In classical design methods for considering uncertainty in a system design, the designers apply drastic tolerances and large safety factors (Jun et al 2011). Assigning the values of these parameters is based on past experience of designer and has these drawbacks: a) specifying the values of safety factor for new systems and materials without any experience is difficult; b) in the design process, it is not easy to measure robustness and
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