| WANG Wei,XI Shengkun,GONG Xiufang,NIELiping,DINGJuanqiang,WANG Cuiping,LIU Xingjun.Prediction of Porosity of Thermal Barrier Coatings for Gas Turbines Blades Based on the Data-mining Technology[J],53(17):208-217 |
| Prediction of Porosity of Thermal Barrier Coatings for Gas Turbines Blades Based on the Data-mining Technology |
| Received:October 23, 2023 Revised:April 17, 2024 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.17.020 |
| KeyWord:gas turbine thermal barrier coatings porosity data-mining machine learning |
| Author | Institution |
| WANG Wei |
State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Sichuan Deyang , China;Dongfang Turbine Co., Ltd.of Dongfang Electric Corporation, Sichuan Deyang , China |
| XI Shengkun |
Shenzhen Hongjing Technology Co., Ltd., Guangdong Shenzhen , China;a.School of Materials Science and Engineering, b.State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Guangdong Shenzhen , China |
| GONG Xiufang |
State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Sichuan Deyang , China;Dongfang Turbine Co., Ltd.of Dongfang Electric Corporation, Sichuan Deyang , China |
| NIELiping |
State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Sichuan Deyang , China;Dongfang Turbine Co., Ltd.of Dongfang Electric Corporation, Sichuan Deyang , China |
| DINGJuanqiang |
State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Sichuan Deyang , China;Dongfang Turbine Co., Ltd.of Dongfang Electric Corporation, Sichuan Deyang , China |
| WANG Cuiping |
College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Fujian Xiamen , China |
| LIU Xingjun |
a.School of Materials Science and Engineering, b.State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Guangdong Shenzhen , China;a.School of Materials Science and Engineering, b.State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Guangdong Shenzhen , China;College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Fujian Xiamen , China |
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| Abstract: |
| 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 to calculate the porosity data of the ceramic layer, train a machine learning model to predict the porosity of the ceramic layer of the thermal barrier coating under different process parameters, and verify and test the hardness and porosity of the coating through experiments. According to the "No Free Lunch" theorem, no algorithm could be universally applicable to all situations. After trying with multiple machine learning algorithms, it was found that the Gradient Boosting Regression model was able to accurately predict the porosity of thermal barrier coatings, with spray power, powder feeding rate, and spray distance having the greatest impact on porosity. Machine science had a certain degree of extensibility, with the R value of the model increasing from 0.834 4 to 0.943 0, the R2 value increasing from 0.696 2 to 0.889 2, the MAE value decreasing from 1.344 0 to 1.039 4, and the RMSE value decreasing from 1.881 0 to 1.712 8. As the porosity decreased, the hardness value of plasma sprayed 8YSZ ceramic coating increased from 3.98 GPa to 5.54 GPa, and the Young's modulus increased from 62.36 GPa increased to 84.30 GPa. This model accurately predicts the porosity of coatings under different processes. Machine learning is used to predict the coating porosity under different processes accurately, it is proved that machine learning algorithms have certain application prospects in the research and development, process optimization, and production of thermal barrier coatings for heavy-duty gas turbine blades. |
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