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International Journal for Multiscale Computational Engineering

Publicado 6 números por año

ISSN Imprimir: 1543-1649

ISSN En Línea: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

UNCERTAINTY QUANTIFICATION OF MANUFACTURING PROCESS EFFECTS ON MACROSCALE MATERIAL PROPERTIES

Volumen 14, Edición 3, 2016, pp. 191-213
DOI: 10.1615/IntJMultCompEng.2016015552
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SINOPSIS

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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