An Investigation of the Mechanical Property Behaviour of Copper/AA7075 Nanocomposites Based on Machine Learning

Predicting the mechanical characteristics of Copper (Cu) -AA7075 nanocomposites produced by the powder metallurgy (PM) method was investigated using a machinelearning (ML) strategy to speed up production and characterization while offering physical insights into the properties of the materials.To build prediction models, six methods are used, including chemical arrangement and the porosity of the composites serving as the defining characteristics.The outcome indicates that the Sequential minimum optimization support vector regression Puk kernel (SMO reg/Puk) model produced the most precise predictions.Indeed, among the six models, its forecasts had the maximum correlation coefficient (CC) and the slightest inaccuracy.Using the SMO reg/Puk model, authors could make predictions about the tensile strength (TS) and hardness (HB) of Cu-AA7075, and this information guided the composition design efforts.The desired chemical composition, including a porosity of around 12.3%, a tensile strength of more than 360 MPa, and hardness of HB (140-145), was achieved in a Cu with 14 wt% Al and 7 wt% Ni nanocomposites.Cu-14Al-7Ni nanocomposites with the targeted tensile strength (394 MPa) and hardness (HB 143) was developed using the SMO reg/Puk model as a roadmap.