Artificial Intelligence Based Analysis of Nanoindentation Load–Displacement Data Using a Genetic Algorithm

Authors

Abraham Burleigh, Illinois Institute of Technology
Miu Lun Lau, Boise State University
Megan Burrill, Illinois Institute of Technology
Daniel T. Olive, Los Alamos National Laboratory
Jonathan G. Gigax, Los Alamos National Laboratory
Nan Li, Los Alamos National Laboratory
Tarik A. Saleh, Los Alamos National Laboratory
Frederique Pellemoine, Fermi National Accelerator Laboratory
Sujit Bidhar, Fermi National Accelerator Laboratory
Min Long, Boise State UniversityFollow
Kavin Ammigan, Fermi National Accelerator Laboratory
Jeff Terry, Illinois Institute of Technology

Document Type

Article

Publication Date

3-1-2023

Abstract

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.

Comments

Erratum in: Applied Surface Science, 30 May 2023, 620, 156840. The authors regret the omission of the following from the acknowledgements: Undergraduate student researcher (MB) was supported by the National Science Foundation – Research Experience for Undergraduates under award DMR 2050916. See erratum publication for details at https://doi.org/10.1016/j.apsusc.2023.156840

Publication Information

Burleigh, Abraham; Lau, Min Lun; Burrill, Megan; Olive, Daniel T.; Gigax, Jonathan G.; Li, Nan; . . . and Terry, Jeff. (2023). "Artificial Intelligence Based Analysis of Nanoindentation Load–Displacement Data Using a Genetic Algorithm". Applied Surface Science, 612, 155734. https://doi.org/10.1016/j.apsusc.2022.155734