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Research Article | Open Access

Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide

Qingfeng ZENGa,b( ), Yong GAOa, Kang GUANc( ), Jiantao LIUd, Zhiqiang FENGe,f
Science and Technology on Thermostructural Composite Materials Laboratory, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
MSEA International Institute for Materials Genome, Gu’an 065500, China
School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
School of Mechanics and Engineering, Southwest Jiaotong University, Chengdu 610031, China
LMEE-UEVE, Université Paris-Saclay, Evry 91020, France
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Abstract

Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from about 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl2 and BCl3 produce the most boron atoms, while C2H4 and CH4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron–carbon ratio and provide a new design solution for other multi-element systems.

Keywords

machine learning (ML)computational fluid dynamic (CFD)chemical vapor depositionboron carbideB/C ratiokinetic mechanisms

References

[1]
Ohnabe H, Masaki S, Onozuka M, et al. Potential application of ceramic matrix composites to aero-engine components. Compos Part A-Appl S 1999, 30: 489-496.
Google Scholar
[2]
Inghels E, Lamon J. An approach to the mechanical behaviour of SiC/SiC and C/SiC ceramic matrix composites. J Mater Sci 1991, 26: 5411-5419.
Google Scholar
[3]
Christin F. Design, fabrication, and application of thermostructural composites (TSC) like C/C, C/SiC, and SiC/SiC composites. Adv Eng Mater 2002, 4: 903-912.
Google Scholar
[4]
Katoh Y, Snead LL, Henager CH, et al. Current status and recent research achievements in SiC/SiC composites. J Nucl Mater 2014, 455: 387-397.
Google Scholar
[5]
Naslain R, Guette A, Rebillat F, et al. Boron-bearing species in ceramic matrix composites for long-term aerospace applications. J Solid State Chem 2004, 30: 489-496.
Google Scholar
[6]
Liu YS, Cheng LF, Zhang LT, et al. Oxidation protection of multilayer CVD SiC/B/SiC coatings for 3D C/SiC composite. Mat Sci Eng A-Struct 2007, 466: 172-177.
Google Scholar
[7]
Sezer AO, Brand JI. Chemical vapor deposition of boron carbide. Mater Sci Eng B-Adv 2001, 79: 191-202.
Google Scholar
[8]
Jacques S, Guette A, Langlais F, et al. C(B) materials as interphases in SiC/SiC model microcomposites. J Mater Sci 1997, 32: 983-988.
Google Scholar
[9]
Ruggles-Wrenn MB, Wallis TA. Creep in interlaminar shear of an Hi-Nicalon™/SiC–B4C composite at 1300 ℃ in air and in steam. J Compos Mater 2019, 54: 1819-1829.
Google Scholar
[10]
Deshpande SV, Gulari E, Harris SJ, et al. Filament activated chemical vapor deposition of boron carbide coatings. Appl Phys Lett 1994, 65: 1757-1759.
Google Scholar
[11]
Karaman M, Sezgi NA, Doğu T, et al. Kinetic investigation of chemical vapor deposition of B4C on tungsten substrate. AICHE J 2006, 52: 4161-4166.
Google Scholar
[12]
Karaman M, Sezgi NA, Doğu T, et al. Mechanism studies on CVD of boron carbide from a gas mixture of BCl3, CH4, and H2 in a dual impinging-jet reactor. AICHE J 2009, 55: 701-709.
Google Scholar
[13]
Berjonneau J, Langlais F, Chollon G, et al. Understanding the CVD process of (Si)–B–C ceramics through FTIR spectroscopy gas phase analysis. Surf Coat Tech 2017, 201: 7273-7285.
Google Scholar
[14]
Berjonneau J, Chollon G, Langlais F, et al. Deposition process of Si–B–C ceramics from CH3SiCl3/BCl3/H2 precursor. Thin Solid Films 2008, 516: 2848-2857.
Google Scholar
[15]
Liu YS, Zhang LT, Cheng LF, et al. Uniform design and regression analysis of LPCVD boron carbide from BCl3–CH4–H2 system. Appl Surf Sci 2009, 255: 5729-5735.
Google Scholar
[16]
Zeng B, Feng ZD, Li SW, et al. Microstructural study of oxidation of carbon-rich amorphous boron carbide coating. Front Mater Sci 2008, 2: 375-380.
Google Scholar
[17]
Mollick PK, Venugopalan R, Srivastava D. CFD coupled kinetic modeling and simulation of hot wall vertical tubular reactor for deposition of SiC crystal from MTS. J Cryst Growth 2017, 475: 97-109.
Google Scholar
[18]
Ni H, Lu S, Chen C. Modeling and simulation of silicon epitaxial growth in siemens CVD reactor. J Cryst Growth 2014, 404: 89-99.
Google Scholar
[19]
Deck CP, Khalifa HE, Sammuli B, et al. Fabrication of SiC–SiC composites for fuel cladding in advanced reactor designs. Prog Nucl Energy 2012, 57: 38-45.
Google Scholar
[20]
Reinisch G, Patel S, Chollon G, et al. Methyldichloroborane evidenced as an intermediate in the chemical vapour deposition synthesis of boron carbide. J Nanosci Nanotechnol 2011, 11: 8323-8327.
Google Scholar
[21]
Li J, Qin H, Liu Y, et al. Effect of the SiCl4 flow rate on SiBN deposition kinetics in SiCl4–BCl3–NH3–H2–Ar environment. Materials 2017, 10: 627-637.
Google Scholar
[22]
Kleijn CR. Modeling of Chemical Vapor Deposition of Tungsten Films. Boston, USA: Birkhäuser Basel, 1993.
[23]
Beek WJ, Muttzall KMK, van Heuven JW. Transport Phenomena, 2nd edn. New York, USA: John Wiley & Sons, 1999.
[24]
Cuadros F, Cachadiña I, Ahumada W. Determination of Lennard-Jones interaction parameters using a new procedure. Mol Eng 1996, 6: 319-325.
Google Scholar
[25]
Ge Y, Gordon M S, Battaglia F, et al. Theoretical study of the pyrolysis of methyltrichlorosilane in the gas phase. 1. Thermodynamics. J Phys Chem A 2007, 111: 1462-1474.
Google Scholar
[26]
Ge Y, Gordon M S, Battaglia F, et al. Theoretical study of the pyrolysis of methyltrichlorosilane in the gas phase. 2. Reaction paths and transition states. J Phys Chem A 2007, 111: 1475-1486.
Google Scholar
[27]
Ge Y, Gordon M S, Battaglia F, et al. Theoretical study of the pyrolysis of methyltrichlorosilane in the gas phase. 3. Reaction Rate Constant Calculations. J Phys Chem A 2010, 114: 2384-2392.
Google Scholar
[28]
Liu Y, Su KH, Zeng QF, et al. Reaction paths of BCl3 + CH4 + H2 in the chemical vapor deposition process. Struct Chem 2012, 23: 1677-1692.
Google Scholar
[29]
Liu Y, Su KH, Zeng QF, et al. Decomposition reaction rate of BCl3–CH4–H2 in the gas phase. Theor Chem Acc 2015, 134: 1-9.
Google Scholar
[30]
Reinisch G, Leyssale JM, Vignoles GL. Theoretical study of the decomposition of BCl3 induced by a H radical. J Phys Chem A 2011, 115: 4786-4797.
Google Scholar
[31]
Lee J H, Shin J, Realff M J. Machine learning: Overview of the recent progresses and implications for the process systems engineering field. Comput Chem Eng 2018, 114: 111-121.
Google Scholar
[32]
Moreno R, Corona F, Lendasse A, et al. Extreme learning machines for soybean classification in remote sensing hyperspectral images. Neurocomputing 2014, 128: 207-216.
Google Scholar
[33]
Basu A, Shuo S, Zhou H, et al. Silicon spiking neurons for hardware implementation of extreme learning machines. Neurocomputing 2013, 102: 125-134.
Google Scholar
[34]
Benoît F, Heeswijk M, Miche Y, et al. Feature selection for nonlinear models with extreme learning machines. Neurocomputing 2013, 102: 111-124.
Google Scholar
[35]
Feng S, Zhou H, Dong H, et al. Using deep neural network with small dataset to predict material defects. Mater Des 2019, 162: 300-310.
Google Scholar
[36]
Kushvaha V, Kumar S A, Madhushri P, et al. Artificial neural network technique to predict dynamic fracture of particulate composite. J Compos Mater 2021, 54: 3099-3108.
Google Scholar
[37]
Liu X, Gao C, Li P, et al. A comparative analysis of support vector machines and extreme learning machines. Neural Netw 2012, 33: 58-66.
Google Scholar
[38]
Berjonneau J, Chollon G, Langlais F, et al. Deposition process of amorphous boron carbide from CH4/BCl3/H2 precursor. Proc Electrochem Soc 2006, 153: C795-C800.
Google Scholar
[39]
Vandenbulcke L G. Theoretical and experimental studies on the chemical vapor deposition of boron carbide. Ind Eng Chem Res 2002, 24: 568-575.
Google Scholar
[40]
Lartigue S, Cazajous D, Nadal M, et al. Study of boron carbides vapor-deposited under low pressure. In Proceedings of the Fifth European Conference on Chemical Vapor Deposition, 1985: 413-419.
Journal of Advanced Ceramics
Volume 10 Issue 3,
June 2021
Pages 537-550
DOI: 10.1007/s40145-021-0456-3
Cite this article:
ZENG Q, GAO Y, GUAN K, et al. Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide. Journal of Advanced Ceramics, 2021, 10(3): 537-550. https://doi.org/10.1007/s40145-021-0456-3

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Received: 10 August 2020
Revised: 24 December 2020
Accepted: 06 January 2021
Published: 26 April 2021
© The Author(s) 2021

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