POD-RBF Surrogate Model-based Inverse Analysis for Identifying Nonlinear Burgers Model Parameters from Nanoindentation Data

[+] Author and Article Information
Salah U. Hamim

Advanced Development Engineering, Fiat Chrysler Automobiles Auburn Hills, Michigan 48326

Raman P. Singh

School of Mechanical and Aerospace Engineering Oklahoma State University Stillwater, Oklahoma 74078

1Corresponding author.

ASME doi:10.1115/1.4037022 History: Received August 12, 2016; Revised May 10, 2017


This study explores the application of a Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF)- based surrogate model to identify parameters of a nonlinear viscoelastic material model using nanoindentation data. The inverse problem is solved by reducing the difference between finite element simulation-trained surrogate model approximation and experimental data through genetic algorithm- based optimization. The surrogate model, created using POD-RBF, is trained using FE data obtained by varying model parameters within a parametric space. Sensitivity of the model parameters towards the load-displacement output is utilized to reduce the number of training points required for surrogate model training. The effect of friction on simulated load-displacement data is also analyzed. For the obtained model parameter set, the simulated output matches well with experimental data for various experimental conditions.

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