Main content area

A method for predicting the energy consumption of the main driving system of a machine tool in a machining process

Liu, Fei, Xie, Jun, Liu, Shuang
Journal of cleaner production 2015 v.105 pp. 171-177
case studies, cutting, decision support systems, emissions, energy conservation, energy efficiency, equations, industry, models, prediction
The machining systems that mainly consist of machine tools are numerous and are used in a wide range of applications in industry, which usually exhibit very low energy efficiency; as a result, they have great potential for energy savings and environmental emissions reduction. To achieve such energy savings, the prediction of the energy consumption of the machining process has great significance. Also, it can provide a decision-support tool for the establishment of an energy consumption quota, the energy-saving optimization of cutting parameters, energy efficiency evaluation, and so on. Although existing researches on the energy consumption prediction of machine tools have been performed, a practical method is still lacking. Therefore, a new method for predicting the energy consumption of the main driving system of a machine tool in a machining process is proposed. First, a machining process is divided into three types of periods: start-up periods, idle periods and cutting periods. Second, the energy consumption prediction models for each type of period and the total prediction model for the machining process are established. Third, by measuring energy consumption data of the start-up and idle processes at discrete speeds, the functions of the fitted curves of the energy consumption of start-up periods and idle periods are obtained, which enables the energy consumption of the start-up period and the idle period at any different speed to be predicted. Fourth, using the cutting power calculated based on the machining parameters and the additional loss coefficients obtained based on the additional loss coefficients equation set, the energy consumption of the cutting periods can be predicted. Finally, the prediction error analysis model is constructed, and the reasons why the error is not big in the prediction are expounded. The results of a case study indicate that the method is practical and has good application prospect.