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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Multiscale Computational Engineering
Импакт фактор: 1.016 5-летний Импакт фактор: 1.194 SJR: 0.554 SNIP: 0.68 CiteScore™: 1.18

ISSN Печать: 1543-1649
ISSN Онлайн: 1940-4352

Выпуски:
Том 17, 2019 Том 16, 2018 Том 15, 2017 Том 14, 2016 Том 13, 2015 Том 12, 2014 Том 11, 2013 Том 10, 2012 Том 9, 2011 Том 8, 2010 Том 7, 2009 Том 6, 2008 Том 5, 2007 Том 4, 2006 Том 3, 2005 Том 2, 2004 Том 1, 2003

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.2016015552
pages 191-213

UNCERTAINTY QUANTIFICATION OF MANUFACTURING PROCESS EFFECTS ON MACROSCALE MATERIAL PROPERTIES

Guowei Cai
Vanderbilt University, Nashville, TN
Sankaran Mahadevan
Civil and Environmental Engineering Department, Vanderbilt University, Nashville, Tennessee 37235, USA

Краткое описание

This paper presents a methodology to propagate the uncertainties in the manufacturing process parameters to bulk material properties through multiscale modeling. Randomness of material initial condition and uncertainties in the manufacturing process lead to variability in the microstructure, which in turn leads to variability in the macrolevel properties of the material. In this paper, 2D dual-phase polycrystalline microstructure is simulated based on the initial condition of the grain cores and the manufacturing environment, instead of Voronoi tessellation, which assumes equal grain growth velocities for different phases and therefore is unable to link variability in grain growth velocity to the manufacturing process variability. Then a homogenization method is applied to predict macrolevel properties. The cooling schedule of a dual-phase alloy is used to illustrate the methodology, and Young's modulus is the prediction quantity of interest. Even with a given cooling schedule, spatial variation of temperature affects the microstructure and properties; this variability is also incorporated in this paper through a random field representation. The uncertainty quantification methodology uses Gaussian process surrogate modeling for computational efficiency. The relative contributions of both aleatory and epistemic sources to the overall bulk property uncertainty are quantified using an innovative global sensitivity analysis approach; this provides guidance for manufacturing process control in order to meet the desired uncertainty bounds in the bulk property estimates.


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