Title: Uncertainty Quantification of Material Models for Process Simulation
Authors: Oskar Fernlund, Alireza Forghani, Martin Roy, Trevor Campbell and Göran Fernlund
Abstract: In composite process simulation, the ability to accurately capture changes in material behavior, such as degree of cure/crystallinity, viscosity, modulus and thermal expansion during the process is of great importance for making accurate predictions about the outcome of the process. Material behavior is commonly described by a mathematical equation whose parameters have been determined by fitting the equation to experimental data. This paper demonstrates the use of contemporary statistical methods to fit both parametric and nonparametric material models, how to quantify and reduce prediction error and uncertainty and how to avoid overfitting. Methods highlighted include: least-squares regression, Gaussian process regression, empirical Bayes and k-fold cross-validation. By quantifying prediction error and uncertainty, the value of data quantity and experimental design can be quantified, which is very useful when designing test matrices for material characterization.
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Conference: SAMPE 2020 | Virtual Series
Publication Date: 2020/06/01
Price: FREEGet This Paper