Abonnement à la biblothèque: Guest
International Journal for Multiscale Computational Engineering

Publication de 6  numéros par an

ISSN Imprimer: 1543-1649

ISSN En ligne: 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

Microstructure Evolution Modeling during and after Deformation in 304 Austenitic Stainless Steel through Cellular Automaton Approach

Volume 7, Numéro 5, 2009, pp. 381-393
DOI: 10.1615/IntJMultCompEng.v7.i5.10
Get accessGet access

RÉSUMÉ

A 2D cellular automaton approach was used to simulate microstructure evolution during and after hot deformation. Initial properties of the microstructure and dislocation density were used as input data to the cellular automaton model. The flow curve and final grain size were the output data for the dynamic recrystallization simulation, and softening kinetics curves were the output data of static and metadynamic recrystallization simulations. The model proposed in this work considered the effect of thermomechanical parameters (e.g., temperature and strain rate) on the nucleation and growth kinetics during dynamic recrystallization. The dynamic recrystallized microstructures at different strains, temperatures, and strain rates were used as input data for static and metadynamic recrystallization simulations. It was shown that the cellular automaton approach can model the final microstructure and flow curve successfully in dynamic recrystallization conditions. The postdeformation simulation results showed that the time for 50% recrystallization decreases with increasing strain for a given initial grain size and that dynamic recrystallization slows the postdeformation recrystallization kinetics compared to a model without dynamic recrystallization.

RÉFÉRENCES
  1. Humphreys, F. J., and Hatherly, M., Recrystallization and Related Annealing Phenomena.

  2. Humphreys, F. J., A network model for recovery and recrystallisation.

  3. Raabe, D., Computational Materials Science.

  4. Chen, L. Q., and Wang, Y. Z., The continuum field approach to modeling microstructural evolution. DOI: 10.1007/BF03223259

  5. Janssens, K. G. F., Random grid, threedimensional, space-time coupled cellular automata for the simulation of recrystallization and grain growth. DOI: 10.1088/0965-0393/11/2/304

  6. Hesselbarth, H. W., Simulation of recrystallization by cellular automata. DOI: 10.1016/0956-7151(91)90183-2

  7. Davies, C. H. J., The effect of neighbourhood on the kinetics of a cellular automaton recrystallization model. DOI: 10.1016/0956-716X(95)00335-S

  8. Janssens, K. G. F., Continuum Scale Simulation of Engineering Materials: Fundamentals, Microstructures, Process Applications.

  9. Ding, R., and Guo, Z. X., Microstructural modeling of dynamic recrystallisation using an extended cellular automaton approach. DOI: 10.1016/S0927-0256(01)00211-7

  10. Najafizadeh, A., Jonas, J. J., Stewart, G. R., and Poliak, E. I., The strain dependence of postdynamic recrystallization in 304 H stainless steel. DOI: 10.1007/s11661-006-0132-9

  11. Jonas, J. J., Dynamic recrystallization — Scientific curiosity or industrial tool?. DOI: 10.1016/0921-5093(94)91028-6

  12. Takeuchi, S., and Argon, A. S., Review Steady state creep of single-phase crystalline matter at high temperature. DOI: 10.1007/BF00540888

  13. Dehghan-Manshadi, A., Barnett, M. R., and Hodgson, P. D., Recrystallization in AISI 304 austenitic stainless steel during and after hot deformation. DOI: 10.1016/j.msea.2007.08.026

  14. Hodgson, P. D., Collinson, D. C., and Perrett, B., The use of hot torsion to simulate the thermomechanical processing of steel.

  15. Mecking, H., and Kocks, U. F., Kinetics of flow and strain-hardening. DOI: 10.1016/0001-6160(81)90112-7

  16. Ryan, N. D., and McQueen, H. J., Dynamic softening mechanism in 304 austenitic stainless steel. DOI: 10.1179/000844390795576058

  17. McQueen, H. J., and Ryan, N. D., Constitutive analysis in hot working. DOI: 10.1016/S0921-5093(01)01117-0

  18. Poliak, E. I., and Jonas, J. J., A one-parameter approach to determining the critical conditions for the initiation of dynamic recrystallization. DOI: 10.1016/1359-6454(95)00146-7

  19. Yazdipour, N., Davies, C. H. J., and Hodgson, P. D., Microstructural modeling of dynamic recrystallization using irregular cellular automata. DOI: 10.1016/j.commatsci.2008.04.027

  20. Stuwe, H. P., and Ortner, B., Recrystallization in hot working and creep.

  21. Ding, R., and Guo, Z. X., Coupled quantitative simulation of microstructural evolution and plastic flow during dynamic recrystallization. DOI: 10.1016/S1359-6454(01)00233-6

  22. Fujita, N., Narushima, T., Iguchi, Y., and Ouchi, C., Grain refinement of as cast austenite by dynamic recrystallization in HSLA steels.

  23. Higginson, R. L., and Sellars, C. M., Worked Examples in Quantitative Metallography.

CITÉ PAR
  1. Yazdipour N., Hodgson P.D., Modelling post-deformation softening kinetics of 304 austenitic stainless steel using cellular automata, Computational Materials Science, 54, 2012. Crossref

596 Vues d'articles 42 Téléchargements d'articles Métrique
596 VUES 42 TÉLÉCHARGEMENTS 1 Crossref CITATIONS Google
Scholar
CITATIONS

Articles avec un contenu similaire:

RESIDUAL STRESS PREDICTION IN POROUS CFRP USING ARTIFICIAL NEURAL NETWORKS Composites: Mechanics, Computations, Applications: An International Journal, Vol.9, 2018, issue 1
Sebastião Simões da Cunha, Jr., Guilherme Ferreira Gomes, Antonio Carlos Ancelotti, Jr.
EXPERIMENTAL INVESTIGATION OF MULTIAXIAL FAILURE AND IDENTIFICATION OF FAILURE CRITERIA FOR A PBX SIMULANT MATERIAL International Journal of Energetic Materials and Chemical Propulsion, Vol.20, 2021, issue 2
Arnaud Frachon, Didier Picart, Florian Lacroix, Marwen Chatti, Michael Caliez, Michel Gratton, Nourredine Aït Hocine
STRUCTURAL JOINTS MADE BY FRP AND STEEL: A NEW PROPOSAL OF ANALYSIS BASED ON THE PROGRESSIVE DAMAGE APPROACH Composites: Mechanics, Computations, Applications: An International Journal, Vol.6, 2015, issue 2
Carlo Casalegno, Salvatore Russo
A MULTISCALE MESH-FREE APPROACH TO MODELING DAMAGE OF AN ULTRA-HIGH-PERFORMANCE CONCRETE International Journal for Multiscale Computational Engineering, Vol.16, 2018, issue 2
Jesse A. Sherburn, William F. Heard, Paul A. Sparks, Brett A. Williams
EXPERIMENTAL STUDY OF IMPACT COMPRESSION MECHANICAL PROPERTIES OF THE CELLULAR CONCRETE WITH CORAL SAND FINE AGGREGATE Composites: Mechanics, Computations, Applications: An International Journal, Vol.15, 2024, issue 1
Wenlei Li, Yuhao Zhu, Ling Zhou, Junru Ren, Zhiping Deng
Portail numérique Bibliothèque numérique eBooks Revues Références et comptes rendus Collections Prix et politiques d'abonnement Begell House Contactez-nous Language English 中文 Русский Português German French Spain