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《工程(英文)》 >> 2019年 第5卷 第4期 doi: 10.1016/j.eng.2019.03.014

基于自组织映射的增材制造中数据驱动式微观组织和显微硬度设计

a Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA
b DMG MORI, Hoffman Estates, IL 60192, USA

收稿日期: 2018-08-13 修回日期: 2018-12-09 录用日期: 2019-03-01 发布日期: 2019-07-02

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摘要

为了在镍基高温合金的增材制造(AM)中设计微观组织和显微硬度,本研究提出了一种新的数据驱动方法,该方法结合了物理模型、实验测量和数据挖掘方法。该模拟基于计算热流体动力学(CtFD)模型,可以获得热行为、凝固参数(如冷却速度)和凝固层的稀释率。根据计算出的热信息, 可利用经过充分测试的力学模型估算枝晶臂间距和显微硬度。通过实验测定试样的微观结构和显微硬度,与模拟值进行比较验证。为了实现过程-组织-性能(PSP)关系的可视化,模拟及实验数据集被输入到数据挖掘模型——自组织映射(SOM)中。在多目标下,工艺参数的设计窗口可以从可视化映射中得到。这种被提出的方法可用于AM和其他数据密集型工艺过程。过程、组织和性能之间的数据驱动联系可能会有利于在线过程监控控制,从而获得理想的微观组织和力学性能。

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参考文献

[ 1 ] reneman CM, Brinson LC, Schadler LS, Natarajan B, Krein M, Wu K, et al. Stalking the materials genome: a data-driven approach to the virtual design of nanostructured polymers. Adv Funct Mater 2013;23(46):5746–52. 链接1

[ 2 ] cDowell DL, Kalidindi SR. The materials innovation ecosystem: a key enabler for the materials genome initiative. MRS Bull 2016;41(4):326–37. 链接1

[ 3 ] ain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 2013;1(1):011002. 链接1

[ 4 ] hompson SM, Bian L, Shamsaei N, Yadollahi A. An overview of direct laser deposition for additive manufacturing; part I: transport phenomena, modeling and diagnostics. Addit Manuf 2015;8:36–62. 链接1

[ 5 ] avid SA, DebRoy T. Current issues and problems in welding science. Science 1992;257(5069):497–502. 链接1

[ 6 ] mith J, Xiong W, Yan W, Lin S, Cheng P, Kafka OL, et al. Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support. Comput Mech 2016;57 (4):583–610. 链接1

[ 7 ] e X, Mazumder J. Transport phenomena during direct metal deposition. J Appl Phys 2007;101(5):053113. 链接1

[ 8 ] an Z, Yu G, He X, Li S. Numerical simulation of thermal behavior and multicomponent mass transfer in direct laser deposition of co-base alloy on steel. Int J Heat Mass Transfer 2017;104:28–38. 链接1

[ 9 ] an Z, Yu G, Li S, He X, Chen R, Zheng C, et al. A novel intelligent adaptive control of laser-based ground thermal test. Chin J Aeronaut 2016;29(4): 1018–26. 链接1

[10] Gan Z, Lian Y, Lin SE, Jones KK, Liu WK, Wagner GJ. Benchmark study of thermal behavior, surface topography, and dendritic microstructure in selective laser melting of inconel 625. Integr Mater Manuf Innovation 2019;8(2):178–93. 链接1

[11] Wolff SJ, Lin S, Faierson EJ, Liu WK, Wagner GJ, Cao J. A framework to link localized cooling and properties of directed energy deposition (DED)- processed Ti–6Al–4V. Acta Mater 2017;132:106–17. 链接1

[12] Wolff S, Lee T, Faierson E, Ehmann K, Cao J. Anisotropic properties of directed energy deposition (DED)-processed Ti–6Al–4V. J Manuf Process 2016;24: 397–405. 链接1

[13] Popova E, Rodgers TM, Gong X, Cecen A, Madison JD, Kalidindi SR. Process-structure linkages using a data science approach: application to simulated additive manufacturing data. Integr Mater Manuf Innov 2017; 6(1):54–68. 链接1

[14] Li J, Jin R, Yu HZ. Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing. Mater Des 2018; 139:473–85. 链接1

[15] Hu Z, Wang H, Thouless MD, Lu W. An approach of adaptive effective cycles to couple fretting wear and creep in finite-element modeling. Int J Solids Struct 2018;139–140:302–11. 链接1

[16] Salloum M, Johnson KL, Bishop JE, Aytac JM, Dagel D, Van Bloemen Waanders BG. Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets. Comput Mech 2019;63(3):491–510. 链接1

[17] Kohonen T. The self-organizing map. Proc IEEE 1990;78(9):1464–80. 链接1

[18] Kohonen T. The self-organizing map. Neurocomputing 1998;21(1–3):1–6. 链接1

[19] Rauber A, Merkl D, Dittenbach M. The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Trans Neural Netw 2002;13(6):1331–41. 链接1

[20] Koishi M, Kowatari N, Figliuzzi B, Faessel M, Willot F, Jeulin D. Computational material design of filled rubbers using multi-objective design exploration. In: Proceedings of the 10th European Conference on Constitutive Models for Rubbers (ECCMR); 2017 Aug 28–31; Munich, German; 2017.

[21] Vesanto J, Alhoniemi E. Clustering of the self-organizing map. IEEE Trans Neural Netw 2000;11(3):586–600. 链接1

[22] Wolff SJ, Gan Z, Lin S, Bennett JL, Yan W, Hyatt G, et al. Experimentally validated predictions of thermal history and mechanical properties in laser-deposited Inconel 718 on carbon steel. Addit Manuf 2019;27: 540–51. 链接1

[23] Gan Z, Liu H, Li S, He X, Yu G. Modeling of thermal behavior and mass transport in multi-layer laser additive manufacturing of Ni-based alloy on cast iron. Int J Heat Mass Transfer 2017;111:709–22. 链接1

[24] Gan Z, Yu G, He X, Li S. Surface-active element transport and its effect on liquid metal flow in laser-assisted additive manufacturing. Int Commun Heat Mass Transf 2017;86:206–14. 链接1

[25] Chew YX, Song J, Bi G, Chen HC, Yao X, Zhang B, et al. Thermal and fluid field modelling for laser aided additive manufacturing. In: Proceedings of Lasers in Manufacturing Conference 2017; Munich, German; 2017.

[26] Hunt JD, editor. Solidification and casting of metals. London: Metal Society; 1979. 链接1

[27] DebRoy T, Wei HL, Zuback JS, Mukherjee T, Elmer JW, Milewski JO, et al. Additive manufacturing of metallic components—process, structure and properties. Prog Mater Sci 2018;92:112–224. 链接1

[28] Vesanto J, Himberg J, Alhoniemi E, Parhankangas J. Self-organizing map in Matlab: the SOM toolbox. In: Proceedings of the Matlab DSP Conference; 1999 Nov 16–17. Espoo, Finland; 1999. p. 35–40.

[29] Mukherjee T, Zuback JS, De A, DebRoy T. Printability of alloys for additive manufacturing. Sci Rep 2016;6(1):19717. 链接1

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