An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

Abstract

Energy modeling and energy-saving have attracted extensive attention in the manufacturing industry. Energy monitoring, modeling, and management issues of grinding, milling, and turning processes have been widely studied. However, special research on drilling power and energy consumption needs to be strengthened. The existing drilling power and energy models have the problems of high computational complexity and low practicability. To address this issue, an improved rapid power and energy prediction method of drilling process is proposed in this study. The motivation of the proposed method is to reduce the computational complexity and improve the practicability without losing the predictive accuracy for drilling power and energy. To verify the effectiveness of the proposed method, experimental and case studies were carried out. The results show that the number of formulas, variables, coefficients of the proposed method are all decreased significantly, therefore, the computational complexity is greatly reduced. Meanwhile, power predictive accuracy is improved by 1.91% instead of decreasing compared with the traditional method. Consequently, the simpler model, lower computational complexity, and higher power accuracy make the proposed method more practical in manufacturing industry.INDEX TERMS drilling processes; power prediction; energy prediction; sustainable manufacturing.

Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing

“…To reduce drilling costs and achieve high-performance drilling, Kliuev et al ( 2019 ) studied the energy distribution of electro discharge machining process. Jia et al ( 2021c ) proposed an improved rapid power and energy prediction method for the drilling process to solve the problems of high computational complexity and low practicability of conventional drilling power and energy consumption models. In addition, Jia et al ( 2018a ) further established a novel energy model of machine-operator systems to assess the energy efficiency of machining processes and initially proposed energy-saving strategies.…”