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Sidrah Sajjad1, Sebastian Knorr2, Dirk Schellenberg2

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Summary
This summary is machine-generated.

This study introduces a data-driven approach using artificial neural networks (ANNs) and evolutionary optimization for efficient material parameter identification from indentation data. The method significantly speeds up inverse analysis, enabling robust characterization of material properties.

Keywords:
inverse problemmicro-indentationnumerical databaseoptimizationsurrogate modellinguniqueness

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Area of Science:

  • Materials Science
  • Computational Mechanics
  • Data Science

Background:

  • Inverse analysis in indentation testing is complex due to nonlinear material response.
  • Accurate material parameter identification is crucial for understanding mechanical behavior.

Purpose of the Study:

  • To develop a reliable and efficient data-driven method for inverse parameter identification in indentation experiments.
  • To integrate artificial neural networks (ANNs) with evolutionary optimization for enhanced inverse analysis.

Main Methods:

  • Generated a comprehensive dataset via systematic simulation of indentation processes.
  • Trained ANN models to predict indentation responses (displacement-time, force, surface profile) from material parameters.
  • Employed evolutionary optimization (genetic algorithm) with ANN-based surrogate models for efficient loss function minimization.

Main Results:

  • Trained ANN surrogate models accurately predict indentation responses, replacing computationally intensive simulations.
  • The integrated approach enables orders-of-magnitude acceleration of inverse analysis.
  • Investigated various mathematical loss functions to ensure robust and unique material parameter determination.

Conclusions:

  • The proposed data-driven method successfully performs inverse analysis on simulated indentation data.
  • This approach offers a computationally efficient pathway for material characterization.
  • Future work will extend the method to experimental indentation data for micro- and nano-indentation testing.