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research-article

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
hamim@okstate.edu

Raman P. Singh

School of Mechanical and Aerospace Engineering Oklahoma State University Stillwater, Oklahoma 74078
raman.singh@okstate.edu

1Corresponding author.

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

Abstract

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.

Copyright (c) 2017 by ASME
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