Jordan Neural Network-based Mechanical Properties Prediction of Ni Coated Duraliumin Composite Sheet for Aerospace Application

A high strength-to-weight ratio makes composite sheets perfect for airplane components, which is why the aerospace industry uses them extensively. Additionally, the nickel coating can improve the wear resistance of the duralumin sheet, thereby reducing the risk of surface damage that could lead to cracks. A blend of duralumin and nickel powder, along with additives and binders, is compacted to form a solid. This solid is then sintered in a vacuum or inert gas to fuse particles into a composite sheet. After generating the composite sheet, it undergoes fatigue failure analysis to validate the strength of the sheet and data on volume, temperature, stress, Young’s modulus, and pressure from the sheet is collected. These inputs are further processed for Jordan Neural Network (JNN) to determine critical mechanical properties such as strength, stiffness, elasticity, and plasticity, thus improving understanding of the material’s behaviour under various conditions. Coating thicknesses of 4, 6, 8, and 10 nm offer stress levels of 10, 110, 160, and 240 Mpa, respectively. This shows that a Ni 6 nm thick coating provides significantly lower stress compared to other coating thicknesses, verifying that the proposed model is well suited for real time aerospace industry.