Artificial intelligence based analysis of nanoindentation load-displacement data using a genetic algorithm

We developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load- displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases.

Automated summary

We developed an automated tool, Nanoindentation Neo package, using a Genetic Algorithm (GA) to analyze nanoindentation load-displacement curves based on the Oliver-Pharr method. For specific materials like polycrystalline isotropic graphites, Least Squares Fitting (LSF) may not provide accurate fit parameters due to the unique shape of unloading curves. To address this issue, we extended our general materials characterization tool Neo to fit nanoindentation data, resulting in meaningful fit parameters obtained with minimal user input.