Time-Generative Adversarial Networks Enabled Ensemble Prediction Method for Energy Consumption of Machine Tools

Abstract

The severe energy situation has become a key factor restricting sustainable development, and the contradiction between the processing cost of large-scale computer numerical control (CNC) production and a small number of low-quality experiments urgently needs to be resolved. Therefore, this article proposes a data augmentation–driven ensemble prediction method for the energy consumption of machine tools. First, machining experiments are designed based on a novel mechanism model of energy consumption considering material removal rate. By analyzing the experimental data and fitting the calibration coefficients in the mechanism model, the predictability of the initial cutting energy consumption model is demonstrated. Then, a time-series generative adversarial network is presented to extract the features of the entire operating process and enhance power samples. Meanwhile, extreme gradient boosting (XGBoost) is trained based on enhanced samples, and time series prediction is performed on the total process of machine tools. To verify the effectiveness of the generated data, the effects of various data augmentation methods on energy consumption prediction are compared. The experimental findings demonstrate that TG-XGBoost can better cover the original data distribution and generate high-quality samples, thereby effectively characterizing the cutting power model and predicting the error between cutting and overall energy consumption, ultimately improving the accuracy of energy efficiency prediction.