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International Journal for Multiscale Computational Engineering

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ISSN Druckformat: 1543-1649

ISSN Online: 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

ANALYSIS OF PREDICTIVE CAPABILITIES OF MULTISCALE PHASE TRANSFORMATION MODELS BASED ON THE NUMERICAL SOLUTION OF HEAT TRANSFER AND DIFFUSION EQUATIONS

Volumen 15, Ausgabe 5, 2017, pp. 413-430
DOI: 10.1615/IntJMultCompEng.2017020554
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ABSTRAKT

The high quality of steel products used in the transport industry is strongly dependent on the parameters of microstructure created during the thermomechanical treatment. Numerical models of phase transformations based on the solution of diffusion equations presented in this work allow one to determine the correlation between parameters of the technological process, changes of microstructure, and product properties. Consequently, these models can be a useful support of the technology design for manufacturing processes. On the other hand, diffusion-based multiscale models are computationally very expensive. Therefore, two simple single-point models, the Johnson-Mehl-Avrami-Kolmogorov (JMAK) equation and an upgrade of the Leblond model, were used as an alternative for fast calculations. Two industrial processes were selected for testing and validation of the developed multiscale models. The first was manufacturing of dual-phase steel strips and the second was manufacturing of pearlitic steel rails. A finite-element (FE) model was used to simulate temperature changes in the macro scale. Single-point models were solved in each Gauss point of the FE mesh. These models were used to analyze a large number of technological variants and to select those giving the required phase composition in products. The developed diffusion-based models were solved in selected points of the product only. In the micro scale these models simulated the austenite decomposition into ferrite, bainite, martensite, and pearlite. The FE method was used to solve the diffusion equation in austenite grains. The initial and boundary conditions for the diffusion model were determined for local thermodynamic equilibrium using ThermoCalc software. Diffusion-based models were used to simulate the best technological variants selected by the single-point models and to predict advanced parameters of the microstructure (morphology, carbon distribution, distribution of properties). All models were compared with respect to their predictive capabilities and computation times.

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