Prediction of Waviness Values in Skew Rolling Using Machine Learning Methods.

This paper relates to the selection of a machine learning model for predicting the surface waviness of rolled products. An experimental study was carried out using the skew rolling method with three tapered rolls. The products were shaped with variable process parameters. The obtained results were used as a training data set for selected regression models. The random forest model was determined to be the most effective due to the highest value of the coefficient of determination R2. The influence of individual process parameters on the waviness value was calculated using the SHAP library. [ABSTRACT FROM AUTHOR]