Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

I. M.R. Najjar, A. M. Sadoun, Ghazi S. Alsoruji, Mohamed Abd Elaziz, A. Wagih

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al2O3 nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al2O3 nanocomposites with different Al2O3 concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al2O3 nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.
Original languageEnglish (US)
Pages (from-to)7748-7758
Number of pages11
JournalCeramics International
Volume48
Issue number6
DOIs
StatePublished - Mar 15 2022
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-21

ASJC Scopus subject areas

  • Materials Chemistry
  • Surfaces, Coatings and Films
  • Ceramics and Composites
  • Process Chemistry and Technology
  • Electronic, Optical and Magnetic Materials

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