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| An Organic Coating Performance Assessment Method Based on Machine Learning Classification Algorithms and EIS Data |
| Received:March 22, 2024 Revised:April 23, 2024 |
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| DOI：10.7643/issn.1672-9242.2024.05.016 |
| KeyWord:organic coating classification algorithm machine learning electrochemical impedance spectroscopy Support Vector Machine k-Nearest Neighbor Random Forest |
| Author | Institution |
| JI Haodi |
School of Reliability and Systems Engineering, Beihang University, Beijing , China |
| MA Xiaobing |
School of Reliability and Systems Engineering, Beihang University, Beijing , China |
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| Abstract: |
| The work aims to rapidly evaluate the corrosion resistance performance of organic coatings using machine learning classification algorithms. Laboratory accelerated tests were conducted to simulate the actual degradation process of coatings. The equivalent circuit parameters at different degradation stages were analyzed based on measured electrochemical data. Subsequently, a large amount of data were obtained for machine learning through random sampling. By comparing Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF) algorithms, as well as the accuracy of coating performance classifier models trained with various input feature sets, the most suitable machine learning algorithms and electrochemical features for rapid coating performance evaluation were analyzed. Classifier models trained with k-NN and RF models, demonstrated good predictive performance, while the SVM model showed relatively poorer predictive performance. Among the models trained with different frequency ranges, those trained with low-frequency data performed the best, whereas those trained with high-frequency data performed relatively worse. The RF classifier model trained with impedance imaginary part, imaginary part & real part, and impedance modulus as input features demonstrates the most accurate predictive performance. Within different frequency ranges, impedance features from the low-frequency range are more effective in accurately characterizing coating performance. |
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