High-temperature ablation resistance prediction of ceramic coatings using machine learning

Surface ablation temperature and linear ablation rate are two crucial indicators for ceramic coatings under ultrahigh temperatures service, yet the results collection of such two indicators in the process is difficult due to the long-period material preparation and the high-cost test. In this work, four kinds of machine learning models are applied to predict the above two indicators. The Random Forest (RF) model exhibits a high accuracy of 87% in predicting surface ablation temperature, while a low accuracy of 60% in linear ablation rate. To optimize the model, the novel features are constructed based on the original features by the sum of the importance weights in the model. Thereafter, the importance of the newly constructed features increases significantly, and the accuracy of the optimized RF model is improved by 11%, exceeding 70% in accuracy. By validation with available data and experiments, the optimized model demonstrates precise predictions of the target variables.