Application of Machine Learning and Grey Taguchi Technique for the Development and Optimization of a Natural Fiber Hybrid Reinforced Polymer Composite For Aircraft Body Manufacture

Abstract

Abstract The rapid expansion of the air transport industry raises significant sustainability concerns due to its substantial carbon emissions and contribution to global climate change. These emissions are closely linked to fuel consumption, which in turn is influenced by the weight of materials used in aircraft systems. This study extensively applied machine learning tools for the optimization of natural fiber-reinforced composite material production parameters for aircraft body application. The Taguchi optimization technique was used to study the effect of sisal fibers, glass fibers, fiber length, and NaOH treatment concentration on the performance of the materials. Multi-objective optimization methods like the grey relational system and genetic algorithm (using the MATLAB programming interface) were employed to obtain the best combination of the studied factors for low fuel consumption (low carbon emission) and high-reliability structural applications of aircraft. The models developed from regressional analysis had high accuracy of prediction, with R-Square values all > 80%. Optimization of the grey relational model of the developed composite using the genetic algorithm showed the best process parameter to achieve low weight material for aircraft application to be 40% sisal, 5% glass fiber at 35mm fiber length, and 5% NaOH concentration with grey relational analysis at the highest possible level, which is unity.