Data-driven method for predicting energy consumption of machine tool spindle acceleration

As an essential operation, spindle acceleration occurs frequently in the machining process, the energy consumption of which has an important impact on the energy efficiency of machine tools, cannot be ignored. However, due to its energy characteristics of short duration, high power peak and complex electromechanical operating of the spindle motor, the energy consumption of the spindle acceleration process is difficult to calculate accurately. To fill this gap, a data-driven method for machine tool spindle acceleration energy prediction is proposed in this paper. Firstly, the energy characteristics of spindle acceleration are studied, and a dataset for the energy prediction is determined. Secondly, an automatic extraction algorithm is developed to extract the time data of power peak, and then a framework for data collection and preprocessing is proposed. Thirdly, a spindle acceleration energy prediction model is established with Back-propagation Neural Network based on the Genetic Algorithm (GA-BP), and the network structure and the operation process are also studied. Finally, a case study of spindle acceleration is given to verify the validity of the proposed approach and model, and the accuracy is also verified with other algorithms.