Machine-Learning-Assisted the Design of Resin Matrix Composites Coating with Ablation Resistance

Abstract

Abstract Traditional experimental methods always cost a lot but produce little when designing and developing new kinds of materials, especially for coating materials. However, with the assistance of machine learning, it is possible to predict the performance of a specific coating without preparation or simulation, which makes the design of material more efficient. In this study, machine learning was introduced to assist the design of resin matrix composites coating with ablation resistance. A structured method for engineering data in the material field was approved. Based on this method, the data from laboratory records and Lange’s Chemistry Handbook were collated into one operational database. All the 190 sets of data were used to train the artificial neural network (ANN) regression model to predict the back-surface temperature of the substrate for specific coating under given ablation condition. The mean absolutely percentage error (MAPE) of the model is 7%. Concerning the characteristics of the material database, a feature engineering method, which combines the Pearson correlation coefficient and random forest (RF) algorithm was performed to identify the main controlling factors of the service performance of the coatings.