![]() Tan L, Zabaras N (2007) A level set simulation of dendritic solidification of multi-component alloys. In: NIST GCR-16-006, NIST, Gaithersburg, MD Scott TJ, Beaulieu TJ, Rothrock GD, O’Connor AC (2016) Economic analysis of technology infrastructure needs for advanced manufacturing: additive manufacturing. Rodgers TM, Madison JD, Tikare V (2017) Simulation of metal additive manufacturing microstructures using kinetic Monte Carlo. Rodgers TM, Madison JD, Tikare V, Maguire MC (2016) Predicting mesoscale microstructural evolution in electron beam welding. Rappaz M, Gandin C-A (1993) Probabilistic modelling of microstructure formation in solidification processes. Panwisawas C, Qiu C, Anderson MJ, Sovani Y, Turner RP, Attallah MM, Brooks JW, Basoalto HC (2017) Mesoscale modelling of selective laser melting: thermal fluid dynamics and microstructural evolution. ![]() (2015) MPI: a message-passing interface standard. Liu DR, Reinhart G, Mangelinck-Noel N, Gandin C-A, Nguyen-Thi H, Billia B (2014) Coupled cellular automaton (CA)–finite element (FE) modeling of directional solidification of Al-3.5 wt% Ni alloy: a comparison with X-ray synchrotron observations. Lipton J, Glicksman ME, Kurz W (1984) Dendritic growth into undercooled alloy melts. Kurz W, Giovanola B, Trivedi R (1986) Theory of microstructural development during rapid solidification. Kim Y-T, Goldenfeld N, Dantzig J (2000) Computation of dendritic microstructures using a level set method. Gandin C-A, Rappaz M (1997) A 3D cellular automaton algorithm for the prediction of dendritic grain growth. Gandin C-A, Desbiolles J-L, Rappaz M, Thevoz P (1999) A three-dimensional cellular automation-finite element model for the prediction of solidification grain structures. Gandin C-A, Rappaz M (1994) A coupled finite element cellular automaton model for the prediction of dentritic grain structures in solidification processes. Sci Rep 7(41527):1–11įerreira AF, da Silva AJ, de Castro JA (2006) Simulation of the solidification of pure nickel via the phase-field method. EPFL Press, Lausanneĭezfoli ARA, Hwang W-S, Huang W-C, Tsai T-W (2017) Determination and controlling of grain structure of metals after laser incidence: theoretical approach. Metall Mater Trans A Phys Metall Mater Sci 44(2):873–887ĭantzig JA, Rappaz M (2016) Solidification. Ann Rev Mater Res 32(1):163–194Ĭarozzani T, Gandin C-A, Digonnet H, Bellet M, Zaidat K, Fautrelle Y (2013) Direct simulation of a solidification benchmark experiment. Metall Mater Trans A 41(13):3422–3434īoettinger WJ, Warren JA, Beckermann C, Karma A (2002) Phase-field simulation of solidification. Mater Charact 84:153–168Īl-Bermani SS, Blackmore ML, Zhang W, Todd I (2010) The origin of microstructural diversity, texture, and mechanical properties in electron beam melted Ti–6Al–4V. Acta Mater 124:360–371Īntonysamy AA, Meyer J, Prangnell PB (2013) Effect of build geometry on the \(\beta \)-grain structure and texture in additive manufacture of Ti–6Al–4V by selective electron beam melting. The model is further demonstrated through an additive manufacturing simulation with resulting grain structures showing reasonable agreement with those observed in experiments.Īcharya R, Sharon JA, Staroselsky A (2017) Prediction of microstructure in laser powder bed fusion process. Scaling tests on a representative problem with about half a billion cells demonstrate parallel efficiency of more than 80% on 8 processors and around 50% on 64 loss of efficiency is attributable to load imbalance due to near-surface grain nucleation in this test problem. ![]() We give a detailed summary of the formulation of the model, as well as a description of the communication strategies implemented to ensure parallel efficiency. As a result, parallelization requires careful treatment of load balancing between processors as well as interprocess communication in order to maintain a high parallel efficiency. Solidification phenomena are characterized by highly localized events, such as the nucleation and growth of multiple grains. In this paper, a parallelized 3D cellular automaton computational model is developed to predict grain morphology for solidification of metal during the additive manufacturing process.
0 Comments
Leave a Reply. |