Identification of Fe-Zn coating behaviors by a new reverse approach using artificial intelligence

Abstract

Abstract Nanoindentation is a technique commonly used to measure the mechanical properties of thin films even at depths less than 1 μm. In fact, the characterization is based on the study of the load/displacement curves resulting from the nanoindentation test. We aim to use the backtracking search optimization algorithm (BSA) to improve the extraction of P(h) curves performed by nanoindentation on galvannealed Fe-Zn coating in terms of precision and dispersion. Indeed, the originality of this study is not limited only to the P(h) curve extraction methods, but also to its application in modeling the reverse approach for the case of Fe-Zn coating deposited on a Dual-Phase DP600 steel substrate. Indeed, the BSA approach showed more precision (with respect to the determined mean value) and less dispersion (magnitude of error around the identified mean value) compared to the Least-squares approach. The average error with the BSA and LS methods is respectively 0.89% and 3.16% for the yield stress σ Y and 3.17% and 7.93% for the strain hardening exponent (n). This reduced the error variability in the prediction of the constitutive law to 72% and 60% for ( σ Y ) and n, respectively. Thus, we solved the problems of accessibility, uniqueness of the solution, precision (+10%) and dispersion (−85%) of the required prediction models for the Fe-Zn coating behavior.