Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach
Thermal barrier coatings (TBCs) play a crucial role in safeguarding aero-engine blades from high-temperature environments and enhancing their performance and durability. Accurate evaluation of TBCs’ porosity is of paramount importance for aerospace material research. However, existing evaluation met...
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/15/8988 |
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| author | Rui Li Dongdong Ye Qiukun Zhang Jianfei Xu Jiabao Pan |
| author_facet | Rui Li Dongdong Ye Qiukun Zhang Jianfei Xu Jiabao Pan |
| author_sort | Rui Li |
| collection | DOAJ |
| description | Thermal barrier coatings (TBCs) play a crucial role in safeguarding aero-engine blades from high-temperature environments and enhancing their performance and durability. Accurate evaluation of TBCs’ porosity is of paramount importance for aerospace material research. However, existing evaluation methods often involve destructive testing or lack precision. In this study, we proposed a novel nondestructive evaluation method for TBCs’ porosity, utilizing terahertz time-domain spectroscopy (THz-TDS) and a machine learning approach. The primary objective was to achieve reliable and precise porosity evaluation without causing damage to the coatings. Multiple feature parameters were extracted from THz-TDS data to characterize porosity variations. Additionally, correlation analysis and <i>p</i>-value testing were employed to assess the significance and correlations among the feature parameters. Subsequently, the dung-beetle-optimizer-algorithm-optimized random forest (DBO-RF) regression model was applied to accurately predict the porosity. Model performance was evaluated using K-fold cross-validation. Experimental results demonstrated the effectiveness of our proposed method, with the DBO-RF model achieving high precision and robustness in porosity prediction. The model evaluation revealed a root-mean-square error of 1.802, mean absolute error of 1.549, mean absolute percentage error of 8.362, and average regression coefficient of 0.912. This study introduces a novel technique that presents a dependable nondestructive testing solution for the evaluation and prediction of TBCs’ porosity, effectively monitoring the service life of TBCs and determining their effectiveness. With its practical applicability in the aerospace industry, this method plays a vital role in the assessment and analysis of TBCs’ performance, driving progress in aerospace material research. |
| first_indexed | 2024-03-11T00:31:40Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:31:40Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-c8b710ef47ce456cb9150004e5509ab12023-11-18T22:40:12ZengMDPI AGApplied Sciences2076-34172023-08-011315898810.3390/app13158988Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning ApproachRui Li0Dongdong Ye1Qiukun Zhang2Jianfei Xu3Jiabao Pan4School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaSchool of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaFujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaDepartment of Automotive Engineering and Intelligent Manufacturing, Wanjiang College of Anhui Normal University, Wuhu 241008, ChinaSchool of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaThermal barrier coatings (TBCs) play a crucial role in safeguarding aero-engine blades from high-temperature environments and enhancing their performance and durability. Accurate evaluation of TBCs’ porosity is of paramount importance for aerospace material research. However, existing evaluation methods often involve destructive testing or lack precision. In this study, we proposed a novel nondestructive evaluation method for TBCs’ porosity, utilizing terahertz time-domain spectroscopy (THz-TDS) and a machine learning approach. The primary objective was to achieve reliable and precise porosity evaluation without causing damage to the coatings. Multiple feature parameters were extracted from THz-TDS data to characterize porosity variations. Additionally, correlation analysis and <i>p</i>-value testing were employed to assess the significance and correlations among the feature parameters. Subsequently, the dung-beetle-optimizer-algorithm-optimized random forest (DBO-RF) regression model was applied to accurately predict the porosity. Model performance was evaluated using K-fold cross-validation. Experimental results demonstrated the effectiveness of our proposed method, with the DBO-RF model achieving high precision and robustness in porosity prediction. The model evaluation revealed a root-mean-square error of 1.802, mean absolute error of 1.549, mean absolute percentage error of 8.362, and average regression coefficient of 0.912. This study introduces a novel technique that presents a dependable nondestructive testing solution for the evaluation and prediction of TBCs’ porosity, effectively monitoring the service life of TBCs and determining their effectiveness. With its practical applicability in the aerospace industry, this method plays a vital role in the assessment and analysis of TBCs’ performance, driving progress in aerospace material research.https://www.mdpi.com/2076-3417/13/15/8988thermal barrier coatingsporosity characterizationterahertz time-domain spectroscopynondestructive evaluationmulti-feature fusionmachine-learning-based prediction |
| spellingShingle | Rui Li Dongdong Ye Qiukun Zhang Jianfei Xu Jiabao Pan Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach Applied Sciences thermal barrier coatings porosity characterization terahertz time-domain spectroscopy nondestructive evaluation multi-feature fusion machine-learning-based prediction |
| title | Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach |
| title_full | Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach |
| title_fullStr | Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach |
| title_full_unstemmed | Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach |
| title_short | Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach |
| title_sort | nondestructive evaluation of thermal barrier coatings porosity based on terahertz multi feature fusion and a machine learning approach |
| topic | thermal barrier coatings porosity characterization terahertz time-domain spectroscopy nondestructive evaluation multi-feature fusion machine-learning-based prediction |
| url | https://www.mdpi.com/2076-3417/13/15/8988 |
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