Research Papers

Bayesian Calibration and Uncertainty Quantification for a Physics-Based Precipitation Model of Nickel–Titanium Shape-Memory Alloys

[+] Author and Article Information
Gustavo Tapia

Industrial and Systems Engineering Department,
Texas A&M University,
College Station, TX 77843
e-mail: gtapia@tamu.edu

Luke Johnson

Materials Science and Engineering Department,
Texas A&M University,
College Station, TX 77843
e-mail: lukejohnson@tamu.edu

Brian Franco, Kubra Karayagiz, Ji Ma, Raymundo Arroyave, Ibrahim Karaman

Materials Science and Engineering Department,
Texas A&M University,
College Station, TX 77843

Alaa Elwany

Industrial and Systems Engineering Department,
Texas A&M University,
College Station, TX 77843
e-mail: elwany@tamu.edu

1Corresponding author.

Manuscript received August 4, 2016; final manuscript received January 17, 2017; published online March 6, 2017. Assoc. Editor: Donggang Yao.

J. Manuf. Sci. Eng 139(7), 071002 (Mar 06, 2017) (13 pages) Paper No: MANU-16-1417; doi: 10.1115/1.4035898 History: Received August 04, 2016; Revised January 17, 2017

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 materials simulation models. These are broadly defined as physics-based predictive models developed to predict material behavior, i.e., processing-microstructure-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 materials simulation models has been identified as a high priority research area in most recent roadmapping efforts in the field. 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 (SMAs) 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.

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Grahic Jump Location
Fig. 1

Scatterplot matrix of the experimental dataset showing relative location of test data points to training data points

Grahic Jump Location
Fig. 2

Histograms and kernel density estimates ofthe posterior distribution for calibration parameters using direct calibration: (a) θ1, (b) θ2, and (c) θ3

Grahic Jump Location
Fig. 3

Histograms and kernel density estimates of the posterior distribution for calibration parameters using surrogate model: (a) θ1, (b) θ2, and (c) θ3

Grahic Jump Location
Fig. 4

Ten-fold cross-validation of the surrogate model η(·,·)

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Fig. 5

Predictive distributions calculated for the testing set: (a) direct model and (b) surrogate model

Grahic Jump Location
Fig. 6

Sensitivity analysis using Sobol indices approach: (a) indices for the computer model and (b) indices for the calibrated surrogate model



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