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

Abraham Burleigh, Miu Lun Lau, Megan Burrill, Daniel T. Olive, Jonathan G. Gigax, Nan Li, Tarik A. Saleh, Frederique Pellemoine, Sujit Bidhar, Min Long, Kavin Ammigan, Jeff Terry

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish
Article number155734
JournalApplied Surface Science
Volume612
DOIs
StatePublished - 1 Mar 2023

Keywords

  • Artificial intelligence
  • Genetic algortithm
  • Nanoindentation

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