Prediction model of machine tool energy consumption in hard-to-process materials turning

Abstract

Accurate energy consumption prediction before actual turning is helpful for operators to select optimal processing parameters to improve energy efficiency. Tool wear is very fast in hard-to-process materials turning, which leads to the increase of cutting force, cutting temperature, and cutting power of machine tool. However, most existing prediction models do not consider the impact of tool wear on machine tool energy consumption. A new prediction model of machine tool energy consumption based on tool wear, spindle speed, and material removal rate in hard-to-process materials turning is developed, and verified with 06Cr19Ni10 stainless steel turning experiments. The experimental results show that the proposed model has higher prediction accuracy, and the maximum relative error between predicted value and true value is 2.9%. Furthermore, the influence of processing parameters and tool wear on machine tool energy consumption is studied. The machine tool energy consumption is proportional to the material removal volume, and linearly related to tool wear and spindle speed. The machine tool energy consumption decreases with the increase of material removal rate. The research results are helpful to formulate energy-saving turning scheme in hard-to-process materials turning.