High-speed dry hobbing process is a highly nonlinear and time-varying complex dynamic process

so it is difficult to establish an accurate and effective dynamic model for hobbing force fluctuation characteristics. Therefore

a prediction model for rolling force fluctuation characteristics of SOA-XGBoost (Seagull optimization algorithm -extreme gradient boosting) based on machine learning is put forward. By using the seagull optimization algorithm to iteratively train the hyperparameters of XGBoost model

and assigning the optimized hyperparameters to XGBoost model to predict the rolling force fluctuation characteristics

and comparing with the existing GABP

PSO-SVR and XGBoost model

the prediction model is optimized. The experimental results show that the average absolute prediction errors via SOA-XGBoost in the rough machining and finishing test sets are 1.86% and 2.51% respectively

which improves the prediction accuracy of hobbing force fluctuation characteristics and can provide a guidance for optimizing the hobbing processing parameters.