Abstract

In this paper, we analyse various minimization algorithms applied to the problem of determining elasto-plastic material parameters using an inverse analysis and digital image correlation (DIC) system. As the DIC system, ARAMIS is used, while for the finite element solution of boundary value problems, Abaqus software is applied. Different minimization algorithms, implemented in the SciPy Python library, were initially juxtaposed, compared and evaluated based on benchmark functions. Next the proper evaluation of the algorithms was performed to determine the material parameters for isotropic metal plasticity with the Huber-Mises yield criterion and isotropic or combined kinematic-isotropic plastic hardening models. For all researchers utilizing back calculation methods based on a DIC measuring system, such analysis results may be interesting. It was concluded that among the local minimization methods, derivative free optimization algorithms, especially the Powell algorithm, perform the most efficiently.

Highlights

  • In recent decades, the application of measuring techniques that enable us to register whole displacement fields has spread dramatically, mainly owing to companies that produce such systems for commercial applications

  • For each node in the chosen node set of the FEM model, the closest measurement point was determined from the Digital image correlation (DIC) system and, on this basis, a reference displacement function was assigned to it

  • The DFO, gradient and trust-region algorithms are used for local minimization

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Summary

Introduction

The application of measuring techniques that enable us to register whole displacement fields has spread dramatically, mainly owing to companies that produce such systems for commercial applications (see [1,2,3,4]). Having such solutions and the results of the experimental tests, we can define the goal/cost function, with the material parameters being variables whose norm should be minimized.

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