基于数据挖掘技术的燃机透平叶片热障涂层孔隙率预测及力学性能研究  

Prediction of Porosity of Thermal Barrier Coatings for Gas Turbines Blades Based on the Data-mining Technology

作　　者：王伟 袭晟堃[3,4] 巩秀芳 聂丽萍 丁娟强[1,2] 王翠萍 刘兴军[4,5,6] WANG Wei;XI Shengkun;GONG Xiufang;NIE Liping;DING Juanqiang;WANG Cuiping;LIU Xingjun(State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment,Sichuan Deyang 618000,China;Dongfang Turbine Co.,Ltd.of Dongfang Electric Corporation,Sichuan Deyang 618000,China;Shenzhen Hongjing Technology Co.,Ltd.,Guangdong Shenzhen 518055,China;School of Materials Science and Engineering,Harbin Institute of Technology,Guangdong Shenzhen 518055,China;State Key Laboratory of Advanced Welding and Joining,Harbin Institute of Technology,Guangdong Shenzhen 518055,China;College of Materials and Fujian Provincial Key Laboratory of Materials Genome,Xiamen University,Fujian Xiamen 361005,China)

机构地区：[1]清洁高效透平动力装备全国重点实验室,四川德阳618000 [2]东方电气集团东方汽轮机有限公司,四川德阳618000 [3]深圳市鸿晶科技有限公司,广东深圳518055 [4]哈尔滨工业大学(深圳)材料科学与工程学院,广东深圳518055 [5]哈尔滨工业大学(深圳)先进焊接与连接国家重点实验室,广东深圳518055 [6]厦门大学材料学院,福建厦门361005

出　　处：《表面技术》2024年第17期208-217,共10页Surface Technology

摘　　要：目的预测燃机透平叶片热障涂层孔隙率,加速热障涂层的研发及工艺优化,解决传统实验方法效率低、成本高的问题,为重型燃气轮机热障涂层研发及工业实际生产中的具体工艺参数调控提供一定指导。方法采用MATLAB图像二值化处理技术计算陶瓷层的孔隙率数据,训练机器学习模型,预测不同工艺参数下热障涂层陶瓷层的孔隙率,并通过实验验证测试涂层的硬度和孔隙率。结果GradientBoosting Regression模型能够实现对热障涂层孔隙率的准确预测,喷涂功率、送粉率和喷涂距离对孔隙率的影响较大。机器学习具有一定的外延性,模型的R值(Related Coefficient,R)由0.8344提高到0.9430,R2值(Square of Related Coefficient,R^(2))从0.6962提高到0.8892,而MAE的值(Mean Absolute Error,MAE)从1.3440降低到1.0394,RMSE值(Root Mean Squared Error,RMSE)由1.8810减少到1.7128。随孔隙率的降低,等离子喷涂8YSZ陶瓷涂层的硬度由3.98 GPa增加到5.54 GPa,弹性模量由62.36 GPa提高到84.30 GPa。该模型准确预测了不同工艺下的涂层孔隙率。结论喷涂功率、送粉率和喷涂距离决定了热障涂层的孔隙率,热障涂层的孔隙率与其硬度和弹性模量息息相关。本工作利用机器学习准确预测了不同工艺下的涂层孔隙率,证明机器学习算法在重型燃气轮机透平叶片热障涂层研发、工艺优化及生产中具有一定的应用前景。Thermal barrier coating has been widely used in gas turbines,aircraft engines and other advanced power equipment due to its excellent performance in high temperature oxidation resistance,corrosion resistance and surface temperature reducing.The ceramic layer contains defects such as pores,microcracks,unmelted particles,microcracks,and interlayer interfaces.After long-term service at high temperature,the microstructure of the coating changes,which directly affects the insulation ability and mechanical properties of the coating,i.e.,the porosity of the thermal barrier coating is directly related to its properties.However,the traditional experimental method has low research efficiency,there are many factors influencing the porosity of thermal barrier coatings,and the preparation process of thermal barrier coatings is very complex,with dozens of factors determining their performance.In order to further shorten the development and process optimization time of thermal barrier coatings,mathematical methods need to be used to establish models and analyze data to accelerate the development process.In this context,the branch of artificial intelligence data mining technology based on machine learning algorithms has gradually been introduced into the research and development of materials.Different from traditional mathematical fitting,data mining methods can establish nonlinear models and support simultaneous consideration of hundreds or even thousands of variables,allowing for extrapolation predictions without overfitting.In this work,the data mining technology was used to analyze the spraying data of thermal barrier coatings on heavy-duty gas turbine blades,establish multiple machine learning models that described the quantitative relationship between thermal barrier coating porosity and process parameters,compare the predictive effects of each machine learning,and test the predictive effects of the model using data from actual research and development processes.The MATLAB image binarization processing technology was used

关 键 词：燃气轮机 热障涂层 孔隙率 数据挖掘 机器学习 

分 类 号：TG174.4[金属学及工艺—金属表面处理]