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Bayesian Calibration and Uncertainty Quantication of a Physics-based Precipitation Model in Nickel-Titanium Shape-Memory Alloys

[+] Author and Article Information
Gustavo Tapia

Industrial and Systems Engineering Department Texas A&M University College Station, Texas 77843
gtapia@tamu.edu

Luke Johnson

Materials Science and Engineering Department Texas A&M University College Station, Texas 77843
lukejohnson@tamu.edu

Brian Franco

Materials Science and Engineering Department Texas A&M University College Station, Texas 77843
be.franco@tamu.edu

Kubra Karayagiz

Materials Science and Engineering Department Texas A&M University College Station, Texas 77843
kubra87@tamu.edu

Ji Ma

Materials Science and Engineering Department Texas A&M University College Station, Texas 77843
jm@tamu.edu

Raymundo Arroyave

Materials Science and Engineering Department Texas A&M University College Station, Texas 77843
rarroyave@tamu.edu

Ibrahim Karaman

Materials Science and Engineering Department Texas A&M University College Station, Texas 77843
ikaraman@tamu.edu

Alaa Elwany

Industrial and Systems Engineering Department Texas A&M University College Station, Texas 77843
elwany@tamu.edu

1Corresponding author.

ASME doi:10.1115/1.4035898 History: Received August 04, 2016; Revised January 17, 2017

Abstract

Abstract Uncertainty Quantification (UQ) is an emerging field that focuses on characterizing, quantifying, and potentially reducing, the uncertainties associated with computer simulation models used in a wide range of applications. Although it has been successfully applied to computer simulation models in areas such as structural engineering, climate forecasting and medical sciences, this powerful research area is still lagging behind in multi-scale materials simulation models. These are broadly defined as physics-based predictive models developed to predict material behavior - i.e., processing-structure-property relations -, and have recently received considerable interest with the advent of emerging concepts such as Integrated Computational Materials Engineering (ICME). The need of effective tools for quantifying the uncertainties associated with multi-scale materials simulation models has been identified as a high priority research area in most recent roadmapping efforts in the field. Abstract In this paper, we present one of the first efforts in conducting systematic UQ of a physics-based materials simulation model used for predicting the evolution of precipitates in advanced nickel-titanium Shape-Memory Alloys subject to heat treatment. Specifically, a Bayesian calibration approach is used to conduct calibration of the precipitation model using a synthesis of experimental and computer simulation data. We focus on constructing a Gaussian Process-based surrogate modeling approach for achieving this task, and then benchmark the predictive accuracy of the calibrated model with that of the model calibrated using traditional Markov Chain Monte Carlo (MCMC) methods.

Copyright (c) 2017 by ASME
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