Material model calibration through indentation test and stochastic inverse analysis

TL;DR

This paper presents a stochastic inverse analysis method for calibrating material models using indentation tests, combining experimental data with computational techniques to assess parameter uncertainty efficiently.

Contribution

It introduces a novel stochastic inverse analysis approach utilizing Monte Carlo and Extended Kalman filter for material calibration from indentation data.

Findings

The method improves accuracy of parameter estimation.

It reduces computational cost through model reduction.

Uncertainty quantification enhances material characterization.

Abstract

Indentation test is used with growing popularity for the characterization of various materials on different scales. Developed methods are combining the test with computer simulation and inverse analyses to assess material parameters entering into constitutive models. The outputs of such procedures are expressed as evaluation of sought parameters in deterministic sense, while for engineering practice it is desirable to assess also the uncertainty which affects the final estimates resulting from various sources of errors within the identification procedure. In this paper an experimental-numerical method is presented centered on inverse analysis build upon data collected from the indentation test in the form of force-penetration relationship (so-called indentation curve). Recursive simulations are made computationally economical by an a priori model reduction procedure. Resulting inverse problem is solved in a stochastic context using Monte Carlo simulations and non-sequential Extended Kalman filter. Obtained results are presented comparatively as for accuracy and computational efficiency.