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用于介观模拟电子束选区熔化的数据挖掘技术
Data Mining for Mesoscopic Simulation of Electron Beam Selective Melting
在电子束选区熔化技术（EBSM）工艺中，制造部件的性质受到每一道熔道沉积质量的影响。然而，熔道的形成受到各种物理现象和工艺参数的支配，这些参数之间的相关性十分复杂，难以通过实验得出。近来，介观建模技术已成为模拟电子束（EB）熔化过程以及揭示特定熔道形貌的形成机制的手段。尽管如此，人们对工艺参数与熔道特征之间的相关性尚未有定量的理解。本文从介观模拟的结果出发，研究了熔道的形态特征，同时引入了熔道宽度和高度等关键性描述指标，以便从数值上评估沉积质量。本文还定量研究了各种工艺参数的影响，从而导出了工艺条件和熔道特征之间的相关性。最后，本文提出了一种由介观建模和数据挖掘技术组成的仿真驱动优化框架，并讨论了框架的潜力和局限性。
In the electron beam selective melting (EBSM) process, the quality of each deposited melt track has an effect on the properties of the manufactured component. However, the formation of the melt track is governed by various physical phenomena and influenced by various process parameters, and the correlation of these parameters is complicated and difficult to establish experimentally. The mesoscopic modeling technique was recently introduced as a means of simulating the electron beam (EB) melting process and revealing the formation mechanisms of specific melt track morphologies. However, the correlation between the process parameters and the melt track features has not yet been quantitatively understood. This paper investigates the morphological features of the melt track from the results of mesoscopic simulation, while introducing key descriptive indexes such as melt track width and height in order to numerically assess the deposition quality. The effects of various processing parameters are also quantitatively investigated, and the correlation between the processing conditions and the melt track features is thereby derived. Finally, a simulation-driven optimization framework consisting of mesoscopic modeling and data mining is proposed, and its potential and limitations are discussed.
Electron beam selective melting / Mesoscopic modeling / Data mining
| [1] |
Cansizoglu O, Harrysson O, Cormier D, West H, Mahale T. Properties of Ti–6Al– 4V non-stochastic lattice structures fabricated via electron beam melting. Mater Sci Eng A 2008;492(1–2):468–74.
|
| [2] |
Körner C, Helmer H, Bauereiß A, Singer RF. Tailoring the grain structure of IN718 during selective electron beam melting. In: Guedou JY, editor. Proceedings of the 2nd European Symposium on Superalloys and Their Applications; 2014 May 12–16; Giens, France. Les Ulis: EDP Sciences; 2014. p. 117–22.
|
| [3] |
Guo C, Ge W, Lin F. Effects of scanning parameters on material deposition during electron beam selective melting of Ti–6Al–4V powder. J Mater Process Technol 2015;217:148–57.
|
| [4] |
Bauereiß A, Scharowsky T, Körner C. Defect generation and propagation mechanism during additive manufacturing by selective beam melting. J Mater Process Technol 2014;214(11):2522–8.
|
| [5] |
Safdar A, He H, Wei LY, Snis A, Chavez de Paz LE. Effect of process parameters settings and thickness on surface roughness of EBM produced Ti–6Al–4V. Rapid Prototyping J 2012;18(5):401–8.
|
| [6] |
Scharowsky T, Juechter V, Singer RF, Körner C. Influence of the scanning strategy on the microstructure and mechanical properties in selective electron beam melting of Ti–6Al–4V. Adv Eng Mater 2015;17 (11):1573–8.
|
| [7] |
Rausch AM, Markl M, Körner C. Predictive simulation of process windows for powder bed fusion additive manufacturing: influence of the powder size distribution. Comput Math Appl. In press.
|
| [8] |
Jamshidinia M, Kong F, Kovacevic R. Numerical modeling of heat distribution in the electron beam melting of Ti–6Al–4V. J Manuf Sci Eng 2013;135 (6):061010.
|
| [9] |
Yan W, Ge W, Qian Y, Lin S, Zhou B, Liu WK, et al. Multi-physics modeling of single/multiple-track defect mechanisms in electron beam selective melting. Acta Mater 2017;134:324–33.
|
| [10] |
Körner C, Bauereiß A, Attar E. Fundamental consolidation mechanisms during selective beam melting of powders. Model Simul Mater Sci Eng 2013;21 (8):085011.
|
| [11] |
Khairallah SA, Anderson AT, Rubenchik A, King WE. Laser powder-bed fusion additivemanufacturing: physics of complexmelt flow and formationmechanisms of pores, spatter, and denudation zones. Acta Mater 2016;108:36–45.
|
| [12] |
Markl M, Körner C. Multiscale modeling of powder bed-based additive manufacturing. Annu Rev Mater Res 2016;46:93–123.
|
| [13] |
Körner C, Attar E, Heinl P. Mesoscopic simulation of selective beam melting processes. J Mater Process Technol 2011;211(6):978–87.
|
| [14] |
Leung CLA, Marussi S, Atwood RC, Towrie M, Withers PJ, Lee PD. In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing. Nat Commun 2018;9(1):1355.
|
| [15] |
Guo Q, Zhao C, Escano LI, Young Z, Xiong L, Fezzaa K, et al. Transient dynamics of powder spattering in laser powder bed fusion additive manufacturing process revealed by in-situ high-speed high-energy X-ray imaging. Acta Mater 2018;151:169–80.
|
| [16] |
Ly S, Rubenchik AM, Khairallah SA, Guss G, Matthews MJ. Metal vapor microjet controls material redistribution in laser powder bed fusion additive manufacturing. Sci Rep 2017;7(1):4085.
|
| [17] |
Yan W, Qian Y, Ma W, Zhou B, Shen Y, Lin F. Modeling and experimental validation of the electron beam selective melting process. Engineering 2017;3 (5):701–7.
|
| [18] |
Yan W, Qian Y, Ge W, Lin S, Liu WK, Lin F, et al. Meso-scale modeling of multiple-layer fabrication process in selective electron beam melting: interlayer/track voids formation. Mater Des 2018;141:210–9.
|
| [19] |
Hirt CW, Nichols BD. Volume of fluid (VOF) method for the dynamics of free boundaries. J Comput Phys 1981;39(1):201–25.
|
| [20] |
Czitrom V. One-factor-at-a-time versus designed experiments. Am Stat 1999;53(2):126–31.
|
| [21] |
Saltelli A, Chan K, Scott EM. Sensitivity analysis. New York: John Wiley & Sons, Inc.; 2000.
|
/
| 〈 |
|
〉 |
