Predicting surface roughness of carbon/phenolic composites in extreme environments using machine learning

In thermal protection structures, controlling and optimizing the surface roughness of carbon/phenolic (C/Ph) composites can effectively improve thermal protection performance and ensure the safe operation of carriers in high-temperature environments. This paper introduces a machine learning (ML) framework to forecast the surface roughness of carbon-phenolic composites under various thermal conditions by employing an ML algorithm derived from historical experimental datasets. Firstly, ablation experiments and collection of surface roughness height data of C/Ph composites under different thermal environments were conducted in an electric arc wind tunnel. Then, an ML model based on Ridge regression is developed for surface roughness prediction. The model involves incorporating feature engineering to choose the most concise and pertinent features, as well as developing an ML model. The ML model considers thermal environment parameters and feature screened by feature engineering as inputs, and predicts the surface height as the output. The results demonstrate that the suggested ML framework effectively anticipates the surface shape and associated surface roughness parameters in various heat flow conditions. Compared with the conventional 3D confocal microscope scanning, the method can obtain the surface topography information of the same area in a much shorter time, thus significantly saving time and cost.