Data-driven methodology for energy consumption prediction of turning and drilling processes

To accurately and quickly predict the energy consumption of turning and drilling processes, a data-driven methodology to predict energy consumption of machining a part was proposed, which included four key technologies that were manufacturing data acquisition and preprocessing, preprocessing of feature attribute, algorithm for feature selection and energy consumption prediction. The feature selection was achieved by combining sample classification and RReliefF algorithm. The energy consumption was predicted using three algorithms that were neural network, support vector regression and random forest, and the prediction accuracy was improved by adjusting the parameters of the algorithms. Experiments were conducted to validate the proposed method. The energy consumption of cylindrical turning and drilling processes of parts was predicted using the proposed methodology and compared with the measured energy. Results showed that the proposed method could be used to identify the main factors influencing the machining energy consumption. The average prediction errors range from 4.94% to 9.94% and decreased as the number of training samples increasing. The neural network algorithm could achieve the lowest prediction error, which was lower than those obtained using existing method. The proposed methodology had a big potential for industrial applications. © 2020, Editorial Department of CIMS. All right reserved.