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

Journal of Advanced Ceramics

Kang Guan¹  Jiantao Liu²  Zhiqiang Feng³  Yong Gao⁴  Qingfeng Zeng⁵ 

[1] School of Materials Science and Engineering, South China University of Technology, 510640, Guangzhou, China;School of Mechanical Engineering, Southwest Jiaotong University, 610031, Chengdu, China;School of Mechanics and Engineering, Southwest Jiaotong University, 610031, Chengdu, China;LMEE-UEVE, Université Paris-Saclay, 91020, Evry, France;Science and Technology on Thermostructural Composite Materials Laboratory, School of Materials Science and Engineering, Northwestern Polytechnical University, 710072, Xi’an, China;Science and Technology on Thermostructural Composite Materials Laboratory, School of Materials Science and Engineering, Northwestern Polytechnical University, 710072, Xi’an, China;MSEA International Institute for Materials Genome, 065500, Gu’an, China;

关键词: machine learning (ML);    computational fluid dynamic (CFD);    chemical vapor deposition;    boron carbide;    B/C ratio;    kinetic mechanisms;   

DOI  :  10.1007/s40145-021-0456-3

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY