Novel natural gradient boosting-based probabilistic prediction of physical properties for polypropylene-based composite data

Abstract

Accurately predicting the physical properties of polypropylene composites is challenging because they are highly complex due to the numerous combinations of materials used in their production. Therefore, several challenges must be addressed to develop a predictive model suitable for applied in commercial industry. This study proposes a probabilistic prediction model for the physical properties of polypropylene composites using NGBoost with categorization. The proposed model simultaneously addressed the data imbalance and uncertainty problems using the NGBoost with categorization method. In the data preprocessing step, categorization was used to classify data for each recipe type so that all types of data could be learned. In the model training step, the model performance was improved through hyperparameter optimization. Subsequently, the probabilistic possibility of the predicted value was presented using the probability density function of NGBoost. As a result, the performance of the model had a R2 of 0.9 or more and a normalized root mean square error of 0.1 or less in all four properties. Therefore, the proposed model simultaneously suggests a predicted physical property and its probability with a high performance using real commercial data. In addition, this model is useful for industrial workers because it saves time and cost for the manufacturing and property testing of PPCs.