Identification of Material Models of Nanocoatings System Using the Metamodeling Approach

Hard systems of nanocoatings deposited using PVD (physical vapor deposition) are used in the artificial heart prosthesis. Correct determination of nanomaterial parameters is crucial for accuracy of simulation. The objective of this work is identification of material parameters of nanocoatings in hard system using the inverse analysis based on the artificial neural network metamodeling. The inverse analysis was preceded by the development of the Finite Element Method (FEM) model dedicated to the nanoindentation test of the hard nanocoatings system. The performed sensitivity analysis is focused on determination of parameters, having the highest influence on FEM model response. The obtained, reliable FEM model was used next in the inverse analysis. The objective of that analysis was evaluation of the parameters of the individual layers of the nanocoating system. In order to decrease the computation time connected with the inverse analysis, the metamodeling approach was proposed. The used metamodel was based on the artificial neural network technique. The obtained results confirm the usefulness of the presented method in the identification of the material properties of the complex, nanocoating systems.