A properties prediction strategy of aluminum anode foils through machine learning based on feature selection and stacking learning models

With the rapid development of new energy vehicles, the global demand for aluminum anode foils (AAF) increases continuously. In order to improve the stability and accuracy of properties prediction for AAF, a machine learning-based property prediction strategy, integrating feature selection and a stacking ensemble model, is proposed in this study. The Pearson correlation coefficient (PCC) and multicollinearity test (MT) are utilized to pinpoint the most relevant and representative features from the original datasets with 80 process parameters. The data are processed using Box-Cox transformation (BCT) to improve the normality. The stacking integrated learning model is constructed, and the Optuna algorithm is used to determine the hyperparameters of each model with the purpose of enhancing the prediction performance. The results demonstrate that the proposed model exhibits superior accuracy and stability in predicting the AAF properties in comparison to 7 baseline machine learning models. X61 (repair 2 anodizing voltage) is a key parameter of process regulation that has the strongest statistical correlation with AAF properties variation. The stepwise voltage strategy in the formation process can balance the trade-off between withstand voltage and specific capacitance. The proposed model can help to predict the two core properties of aluminum anode foils, thereby revealing the intrinsic correlations among process parameters, microstructures, and properties.