Light-Weight Design of Aerospace Components Using Genetic Algorithm and Dandelion Optimization Algorithm

Abstract

In recent years, weight reduction studies using optimization methods have been increasing, and they are widely used in sectors such as aerospace, automotive, and marine. While there are many research studies on weight reduction using these methods, weight reduction efforts in aircraft landing gear systems are inadequate. Therefore, to address this deficiency, in this study, it is proposed to design a lighter component of the aircraft's nose landing gear fork part that can withstand the same loading conditions with minimum material using optimization techniques. Genetic algorithm and dandelion optimization algorithm, which are algorithms created with a meta-heuristic approach, were used to obtain the optimum size in shape optimization. According to the results obtained, the initial mass of the nose landing gear fork was 14.25 kg, which decreased to 12.99 kg after topology optimization, resulting in approximately an 8.84% weight reduction. The part is 11.79% lighter compared to the initial model after shape optimization using genetic algorithm. With the dandelion optimization algorithm, a mass gain of 1.77 kg resulted in a 12.42% weight reduction, obtaining optimal dimensions. One of the aims of this study is to demonstrate the effectiveness of metaheuristic algorithms on optimal product development. This study is the first to perform weight reduction using artificial intelligence optimization algorithms in landing gear system components.