Intelligent Design Methodology: A Fusion of Fuzzy Logic, Axiomatic Design, and Meta-Heuristic Optimization for Airplane Design

Abstract

This article presents a novel methodology for airplane design, integrating fuzzy logic, axiomatic design, and meta-heuristic optimization algorithms tailored for general aviation projects. The primary contribution of this study lies in the synthesis of conventional design practices with fuzzy and axiomatic decision-making and powerful meta-heuristic optimization algorithms. Five distinct phases are delineated in the intelligent design process of the airplane. Initially, airplane design parameters are established based on conventional methods. Subsequently, fuzzy logic is employed to make decisions based on these parameters in accordance with the conventional design criteria. Additionally, the axiomatic design method is utilized to identify values crucial to the design process. Furthermore, meta-heuristic optimization algorithms are deployed at various stages of the proposed novel algorithm to attain optimal design points. Notably, four robust optimization algorithms, Particle Swarm Optimization(PSO), Artificial Bee Colony(ABC), Firefly Algorithm(FA), and Gray Wolf Optimization(GWO), are utilized for verification purposes. The adoption of robust, precise, and intelligent systematic approaches in airplane design is deemed imperative to meet stringent requirements effectively. Ultimately, the design values obtained lead to the optimal configuration of a 19-seat airplane, which undergoes rigorous comparison, verification, and validation against existing airplane models. In summary, the fusion of fuzzy logic, axiomatic design, and potent meta-heuristic optimization algorithms presents a new and innovative methodology in airplane design, promising significant advancements in the field of airplane designing.