CN117275614A - 一种cvd/cvi数字化模型和预测方法 - Google Patents
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Abstract
本发明公开了一种CVD/CVI数字化模型和预测方法,包括第一H2桶、Ar桶、第二H2桶、化学气相沉积反应器、气体混合器、带加热功能的电子天秤;第一H2桶的输出口、Ar桶的输出口、带加热功能的电子天秤均连接气体混合器,第二H2桶的输出口均连接气体混合器,气体混合器连接化学气相沉积在反应器的进气口:本发明实现了在较宽CVD/CVI工艺参数范围内对SiC的沉积率进行快速、准确的预测,为未来的CVD/CVI数字孪生和智能制造奠定理论和方法基础。
Description
技术领域
本发明涉及化学气相领域,特别是一种CVD/CVI数字化模型和预测方法。
背景技术
陶瓷材料具有耐高温、高比强、高比模、耐腐蚀、耐磨损和低密度的优良性能,使其具有接替金属作为新一代高温结构材料的潜力。但脆性是陶瓷材料的致命弱点,其临界裂纹长度仅为几十微米,因此它没有象金属那样的塑性变形能力。
如何改善陶瓷材料的脆性,成为陶瓷研究者一直关注的命题。在发展的多种增韧途径中,连续纤维增韧陶瓷基复合材料(Continuous Fiber Reinforced Ceramic MatrixComposites,CFR–CMC,简称CMC)可以从根本上克服陶瓷的脆性,是航空航天等高科技领域发展不可缺少的材料,已成为陶瓷基复合材料发展的主流方向。
其中,连续纤维增韧碳化硅陶瓷基复合材料(Silicon Carbide Ceramic MatrixComposites,CMC–SiC)是研究最多和应用最成功的新型热结构陶瓷基复合材料,具有耐高温、低密度、高比强、高比模、抗烧蚀、抗氧化和可避免灾难性损毁等一系列突出优点。
CMC–SiC主要包括碳纤维增韧碳化硅C/SiC和碳化硅纤维增韧碳化硅SiC/SiC两种。由于碳纤维价格便宜且容易获得,因而C/SiC成为SiC陶瓷基复合材料研究、考核与应用的首选,已成功用于第三代航空发动机、冲压发动机、固体和液体发动机的热结构,以及空天飞行器防热结构等方面,在航空和航天领域具有广阔应用前景。
化学气相沉积法CVD和化学气相渗透法CVI是目前制备连续纤维增韧碳化硅陶瓷基复合材料CMC-SiC广泛采用并且已经商业化的方法。然而,由于沉积反应和气相传质之间的复杂关系,CVD工艺控制难度大和加工周期长。计算机数值模拟有助于理解CVI工艺机理,预测致密化过程,有效分析实验数据,缩短工艺参数优化的周期,因而具有十分积极的意义,已被普遍认为是对CVD/CVI实验过程的重要补充。
随着计算机水平和多学科多领域交叉研究方式的快速发展,基于数字孪生概念,建立与实际生产对应的物理模型孪生体来实现智能制造和生产,这种数字化制造的新模式给CVD/CVI的研发带来新的思路。
而数字孪生中比较关键的一个环节就是数值仿真模拟,这是建立与实际反应设备和条件对应的重要一步;与以前的传统仿真不同,数字孪生技术在传统数值仿真的基础上增加了数字化概念,本发明以CVD-SiC为例,介绍如何通过收集一组实验数据,建立对应的物理仿真模型,然后,结合新的离线或在线实验数据建立工艺数据库和机器模型,优化工艺参数,从而提高研发和生产效率,为未来的CVD/CVI数字孪生和智能制造奠定理论和方法基础。
现有技术一的技术方案
近年来,研究人员运用化学工程理论和计算流体力学模拟技术针对CVD/CVI进行了很多研究。根据实验反应器几何特征创立几何实体模型,建立流体流动、传热传质、化学反应等模型,定义模型参数,设置模型的边界条件和区域条件,对模型划分网格单元,基于自编程序或采用流体力学商业软件求解方程组,通过预实验结果对比和参数化分析,估算模型参数,最后用建立的模型对工艺进行优化设计。
现有技术一的缺点
经验参数过多、普适性差。
现有技术二的技术方案
机器学习(ML)在CVD/CVI中的应用越来越广泛,但主要应用于材料形貌设计和工艺参数优化方面。
现有技术二的缺点
尚未有学者将ML应用于预测SiC-CVD沉积速率,也未关联工艺参数。而且训练数据需求大,数据成本高。非常依赖数据的分布,针对数据稀疏区的预测能力差。
发明内容
本发明的目的在于解决现有技术中存在的问题,提供一种CVD/CVI数字化模型和预测方法,本发明实现了在较宽CVD/CVI工艺参数范围内对SiC的沉积率进行快速、准确的预测,为未来的CVD/CVI数字孪生和智能制造奠定理论和方法基础。
具体技术方案如下:
一种CVD/CVI数字化模型,包括第一H2桶、Ar桶、第二H2桶、化学气相沉积反应器、气体混合器和带加热功能的电子天秤;第一H2桶的输出口、Ar桶的输出口、带加热功能的电子天秤均连接气体混合器,第二H2桶的输出口连接气体混合器,气体混合器连接化学气相沉积在反应器的进气口。
优选地,化学气相沉积在反应器内部的一侧设置石墨基板,化学气相沉积反应器内部的另一侧设置石墨加热器,化学气相沉积反应器的内部的横截面前端处设置多孔盘,化学气相沉积反应器的底部横截面的位置处设置排气口。
优选地,化学气相沉积反应器进气口直径都为16mm,化学气相沉积反应器的反应区的长度为200mm或400mm,化学气相沉积反应器直径为25mm,厚度为6.5mm;
化学气相沉积反应器进气口直径都为16mm,化学气相沉积反应器的反应区的长度包括:200mm或400mm;化学气相沉积反应器的表面直径为25mm,厚度为6.5mm。
一种CVD/CVI数字化建模的预测方法,包括以下步骤:
步骤S1:采用MTS(CH3SiCl3)-Ar-H2体系通过化学气相渗透法,在石墨衬底上制备SiC涂层或者SiC基体,生成SiC;
步骤S2:对CVD/CVI化学气相沉积反应器建立二维轴对称几何模型;
步骤S3:应用有限元软件对CVD/CVI化学气相沉积反应器的二维轴对称几何模型进行网格划分,构建了反应区直径为100mm反应器的网格模型以及化学反应及其动力学参数;
步骤S4:对化学反应及其动力学参数中A参数进行已有实验数据进行算法处理,最终得到A参数值;
步骤S5:训练数据集内A参数值以及数据的分布,形成混合数据集;
步骤S6:根据混合数据集,训练ML模型;
步骤S7:对于训练后的混合数据集,采用XG Boost算法和Random forest算法进行训练,再结合机器学习算法,预测CVD/CVI沉积速率。
优选地,步骤S1包括以下子步骤:
子步骤S11:H2和Ar的流量由质量流量控制器控制;
子步骤S12:通过加热MTS溶液,由H2在MTS溶液中起泡带出MTS;
子步骤S13:所有气体混合器中充分混合后通过管道输入到化学气相沉积反应器中;
子步骤S14:反应气体通过多孔盘布气板,逐渐加热,在输运途中发生气相反应并在石墨基板上吸附生成SiC,尾气从出口排出。
优选地,步骤S4的算法处理包括以下子步骤:
子步骤S41:A参数需根据已有实验数据进行拟合,结合模型将“寻找最优模型参数值”转变为“函数极值寻优问题”;
子步骤S42:输入遗传算法,遗传算法优化BP神经网络的要素,最终得到A参数值。
遗传算法优化BP神经网络的要素包括种群初始化、造应度函数、选择操作、交叉操作和变异操作。
步骤S5的混合数据集为:将模拟数据、自研实验数据以及文献数据融合而成。
本发明CVD/CVI数字化建模和预测方法的有益效果如下:
1.本发明通过集成CVD/CVI数值模拟、数据库开发和机器学习辅助预测建模,将收集的实验数据处理后和数值模拟衍生的数据相融合组成数据库,使ML模型学习到了文献实验数据稀疏区的“知识”,实现了在数据稀疏区更高精度的预测。
2.本发明由于模拟数据相对于自研实验数据,数据量较大,ML模型在训练过程中可以充分学习到CVD/CVI在这个工艺参数区域的反应机理,所以预测性能提升效果显著。提出的模型为未来的CVD/CVI数字孪生和智能制造奠定理论和方法基础。
附图说明
图1为本发明的SiC-CVD/CVI系统图。
图2为本发明的反应器的几何模型图。
图3为本发明的反应器的网格模型图。
图4为本发明的模拟值与真实值的对比图。
图5为本发明的基于模拟数据、自研实验数据以及文献数据融合而成的混合数据集ML在数据稀疏区的预测性能图。
图6为本发明的自研实验数据以及文献数据融合的数据集ML在数据稀疏区的预测性能图。
具体实施方式
下面对本发明的具体实施方式进行描述,以便于本技术领域的技术人员理解本发明,但应该清楚,本发明不限于具体实施方式的范围,对本技术领域的普通技术人员来讲,只要各种变化在所附的权利要求限定和确定的本发明的精神和范围内,这些变化是显而易见的,一切利用本发明构思的发明创造均在保护之列。
实施例一
本发明一种CVDCVI数字化建模和预测方法,如图1、图2、图3、图4、图5、图6所示:
步骤S1:采用MTS(CH3SiCl3)-Ar-H2体系通过化学气相沉积法/化学气相渗透法CVD/CVI在石墨衬底上制备SiC涂层或者SiC基体,所使用的CVD/CVI系统如图1所示,H2和Ar的流量由质量流量控制器Mass Flow Controller,MFC控制。
通过加热MTS溶液,由H2在MTS溶液中起泡带出MTS。所有气体在混合罐中充分混合后通过管道输入到化学气相沉积反应器中。反应气体通过多孔圆盘布气板,逐渐加热,在输运途中发生气相反应并在石墨衬底上吸附生成SiC,尾气从出口排出。
步骤S2:由于化学气相沉积反应器的对称性,为减少计算量,对CVD/CVI反应器建立二维轴对称几何模型,图2为反应区直径100mm反应器的几何模型。反应器进气口直径都为16mm,反应器反应区的长度分别为200mm或400mm。前端的布气板被分别简化为均匀排布的8个3×3mm的圆环。
反应器内的圆柱体被定义为石墨基体,作为化学气相沉积反应的表面,其直径为25mm,厚度为6.5mm。MTS-H2-Ar混合气体由左边的进气口输入,通过布气板均匀分流,在反应器内部反应最终沉积SiC在衬底表面,尾气则由反应器右侧的排气口排出。
步骤S3:应用有限元软件对化学气相沉积反应器的二维轴对称几何模型进行网格划分,构建了为反应区直径为100mm反应器的网格模型。
步骤S3:为了构建SiC生长过程的仿真模型,做出以下假设:
(1)气体是连续体;
(2)气体符合理想气体定律:
pV=nRT (1)
p为理想气体的压强,其单位为Pa;V为理想气体气体体积,其单位为m3;T为热力学温度,其单位为K;n为理想气体的物质的量,其单位为mol;R为理想气体常数,8.314J/mol·K;
(3)气体在反应器内为层流流动;
(4)采用菲克扩散模型和附加对流传递机理;
(5)忽略重力影响;
(6)混合物动力粘度和热导率忽略MTS的影响,只考虑H2和Ar;
(7)各组分扩散系数为其在Ar中的扩散系数;
(8)忽略辐射热和反应热;
(9)沉积反应只发生在衬底表面上。
在此基础上,给出了气相热和质量输运的质量、动量、种类和能量守恒方程。质量守恒定律:
这里,ρ是理想气体状态方程中的气体混合物的质量密度,u是平均质量速度的向量。
动量守恒:
这里,ρ是流体粒子上的压力,μ是动态粘度,I是单位张量。
能量守恒定律:
这里,T是温度,Cp是热容,k是导热系数。
物质守恒:
这里,ci是气体成分的摩尔浓度,j是扩散通量,Ri是反应速率,Di是气体成分的扩散系数。
MTS(CH3SiCl3)-H2气相反应主要为:
1)CH3SiCl3的Si-C键断裂引发分解反应,生成CH3和SiCl3自由基;
2)这些自由基及自由基与原反应气体(H2、CH3SiCl3)反应生成中间物质(如CH4、C2H2、C2H6等烷烃和SiCl4、SiCl2、Si2Cl6等硅基化合物)和副产物(HCl)等;
3)其中具有较高表面活性的中间物质更容易吸附到基底表面,随后吸附物质发生表面反应生成SiC。
因此,本专利构建的化学气相沉积仿真模型主要是应用于数据衍生,因此,以C2H4、SiCl2为主要活性组分,拟采用三步反应简洁描述CVD/CVI-SiC的气相-表面反应。反应的动力学常数可由理论计算得到。本专利采用的化学反应及其动力学常数如表1;
表1化学反应及其动力学参数
No | Reaction | A(s-1 or m3mol-1s-1) | n | E(J/mol-1) |
G1 | CH3SiCl3→CH3+SiCl3 | 7.63E14 | 0 | 2.9E5 |
G2 | 2CH3→C2H4+H2 | 8E11 | 0 | 8.5E4 |
G3 | SiCl3→SiCl2+H2 | 3E9 | 0 | 2E5 |
G4 | SiCl2(s)+CH2(s)→SiC(s)+2HCl | 1E13 | 0 | 0 |
No | Reaction | τ | va(1/s) | Ea(eV) |
G5 | SiCl2→SiCl2(s) | 0.01 | 10^13 | 4 |
G6 | C2H4→2CH2(s) | 实验拟合 | 10^13 | 3.5 |
步骤S4:在表1中,G6式A参数需根据已有实验数据进行拟合,结合模型将“寻找最优模型参数值”转变为“函数极值寻优问题”。然后输入遗传算法,遗传算法优化BP神经网络的要素包括种群初始化、造应度函数、选择操作、交叉操作和变异操作,最终得到A参数值。由此得到模型,对比模拟结果和真实值,模拟的平均误差为12.8%。
步骤S5:影响机器学习模型性能的不仅有训练数据集内的数据量,其数据的分布也可以对其综合预测水平有显著影响。
数据的不均衡分布,例如过于集中于某个区域,会导致模型向某个类别具有偏向性,从而导致模型只能在一定范围内具有较好的预测精度,而在整体的预测性能不佳。为了保证后续的数字化模型的准确性和数据库的分布均匀性,通过上述模型在数据稀疏区进行模拟仿真,实现数据衍生以扩充数据库。
表2展示了数据衍生需要进行模拟的工艺参数,d=100mm反应器进行了45次模拟,总共产生了45组模拟数据。
表2数据衍生的工艺参数
步骤S6:建立混合数据集,就是将模拟数据、自研实验数据以及文献数据融合而成,用于训练ML模型。
其中自研实验数据可以是离线新测数据,也可以是通过在线传感器新测的实验数据。
在线传感可以通过连接微量天平获得:材料挂在天平的一臂上,连续记录沉积过程基体的重量变化。
步骤S7:对于混合数据集,可以采用XG Boost算法和Random forest算法进行训练,其结果如图5,如单纯的自研实验数据以及文献数据融合的数据集相比(如图6所示),准确性更高,结果说明建立CVD沉积数值模型进行数据扩展,再结合机器学习算法,可准确的预测CVD沉积速率。
本发明用于其他多元体系的CVD过程也是一种有效的方法。
实施例二
本发明一种CVD/CVI数字化建模和预测方法,如图1、图2、图3、图4、图5、图6所示:
本发明针对CVD/CVI-SiC,但提出的方法也可以用于CVD/CVI的材料体系。
实施例三
本发明一种CVD/CVI数字化建模和预测方法,如图1、图2、图3、图4、图5、图6所示:
由于CVD/CVI自研实验数据存在工艺参数范围广、数据量少的缺点,使ML模型无法充分学习到CVD在这个工艺参数区域的反应机理,导致预测性能的提升不佳。
本专利通过集成CVD/CVI数值模拟、数据库开发和机器学习辅助预测建模,将收集的实验数据处理后和数值模拟衍生的数据相融合组成数据库,使ML模型学习到了文献实验数据稀疏区的“知识”,实现了在数据稀疏区更高精度的预测。
同时,由于模拟数据相对于自研实验数据,数据量较大,ML模型在训练过程中可以充分学习到CVD/CVI在这个工艺参数区域的反应机理,所以预测性能提升效果显著。提出的模型为未来的CVD/CVI数字孪生和智能制造奠定理论和方法基础。
本发明保护点为:
1、建立CVD/CVI仿真模型,以少量实验数据进行校对,利用遗传算法获得最合理的模型参数后,利用CVD/CVI仿真模型生成足量工艺仿真数据库,极大扩展了数据库中的数据分布和数据量;
2、集成CVD/CVI数值模拟、数据库开发和机器学习辅助预测建模,将收集的实验数据处理后和数值模拟衍生的数据相融合组成数据库,使ML模型在实验数据稀疏区有更高精度的预测。
Claims (8)
1.一种CVD/CVI数字化模型,其特征在于,包括第一H2桶、Ar桶、第二H2桶、化学气相沉积反应器、气体混合器和带加热功能的电子天秤;所述第一H2桶的输出口、Ar桶的输出口、带加热功能的电子天秤均连接气体混合器,所述第二H2桶的输出口连接气体混合器,所述气体混合器连接化学气相沉积在反应器的进气口。
2.根据权利要求1所述CVD/CVI数字化模型,其特征在于,所述化学气相沉积在反应器内部的一侧设置石墨基板,所述化学气相沉积反应器内部的另一侧设置石墨加热器,所述化学气相沉积反应器的内部的横截面处且在化学气相沉积反应器的输入口后方设置多孔盘,所述化学气相沉积反应器的底部横截面的位置处设置排气口。
3.根据权利要求2所述CVD/CVI数字化模型,其特征在于,所述化学气相沉积反应器进气口直径都为16mm,所述化学气相沉积反应器的反应区的长度为200mm或400mm,所述化学气相沉积反应器直径为25mm,厚度为6.5mm;
所述化学气相沉积反应器进气口直径都为16mm,所述化学气相沉积反应器的反应区的长度包括:200mm或400mm;化学气相沉积反应器的表面直径为25mm,厚度为6.5mm。
4.一种CVD/CVI数字化建模的预测方法,其特征在于,包括以下步骤:
步骤S1:采用MTS(CH3SiCl3)-Ar-H2体系通过化学气相渗透法,在石墨衬底上制备SiC涂层或者SiC基体,生成SiC;
步骤S2:对CVD/CVI化学气相沉积反应器建立二维轴对称几何模型;
步骤S3:应用有限元软件对CVD/CVI化学气相沉积反应器的二维轴对称几何模型进行网格划分,构建反应区直径为100mm反应器的网格模型以及化学反应及其动力学参数;
步骤S4:对化学反应及其动力学参数中A参数进行已有实验数据进行算法处理,最终得到A参数值;
步骤S5:训练数据集内A参数值以及数据的分布,形成混合数据集;
步骤S6:根据混合数据集,训练ML模型;
步骤S7:对于训练后的混合数据集,采用XG Boost算法和Random forest算法进行训练,再结合机器学习算法,预测CVD/CVI沉积速率。
5.根据权利要求4所述的CVD/CVI数字化建模的预测方法,其特征在于,所述步骤S1包括以下子步骤:
子步骤S11:H2和Ar的流量由质量流量控制器控制;
子步骤S12:通过加热MTS溶液,由H2在MTS溶液中起泡带出MTS;
子步骤S13:所有气体混合器中充分混合后再通过管道输入到化学气相沉积反应器中;
子步骤S14:反应气体通过多孔盘布气板,逐渐加热,在输运途中发生气相反应并在石墨基板上吸附生成SiC,尾气从出口排出。
6.根据权利要求4所述的CVD/CVI数字化模型的预测方法,其特征在于,所述步骤S4的算法处理包括以下子步骤:
子步骤S41:A参数需根据已有实验数据进行拟合,结合模型将“寻找最优模型参数值”转变为“函数极值寻优问题”;
子步骤S42:输入遗传算法,遗传算法优化BP神经网络的要素,最终得到A参数值。
7.根据权利要求6所述的CVD/CVI数字化模型的预测方法,其特征在于,所述遗传算法优化BP神经网络的要素包括种群初始化、造应度函数、选择操作、交叉操作和变异操作。
8.根据权利要求4所述的CVD/CVI数字化模型的预测方法,其特征在于,所述步骤S5的混合数据集为:将模拟数据、自研实验数据以及文献数据融合而成。
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