Energy Consumption Modeling and Process Parameter Optimization of Internal Gear Power Honing Under Multi-Axis Coupling

The internal gear power honing process is widely used in gear machining for electric vehicles because of the advantages of high-precision and high-efficiency machining. The gear honing process involves six axes and high spindle speeds, this process contributes a substantial amount of energy consumption but has less attention on energy saving. To improve the energy efficiency of gear honing process, this paper proposes an energy consumption modeling and process parameter optimization method under multi-axis coupling. The multi-axis coupling motion and energy consumption characteristics of gear honing process are analyzed. The energy consumption model of gear honing process under multi-axis coupling is then established, and the influence law of process parameters on honing energy consumption is investigated. Furthermore, a multi-objective process parameter optimization model for minimizing energy consumption and machining time is constructed. An improved multi-objective atomic orbital search (IMOAOS) algorithm is developed to solve the multi-objective optimization problem. The gear honing experiment results demonstrate that the optimized scheme reduces energy consumption by 39.75% and machining time by 8.48% compared to the empirical scheme. The proposed multi-objective optimization scheme also significantly balances the energy consumption and machining time of gear honing process compared with single-objective optimization. Note to Practitioners—The internal gear power honing process is increasingly used in the electric vehicle and aerospace gear fields due to the high-precision machining. This process consumes a lot of energy but has fewer energy-saving concerns. This article analyzes the energy consumption characteristics of gear honing process, and establishes its energy consumption model for the first time. A multi-objective honing process parameter optimization model considering energy consumption and machining time is then constructed. The experimental results show that the energy consumption and time of the optimization scheme are significantly reduced compared with the empirical scheme. The proposed modeling and optimization method provides a honing parameter decision-making scheme for practical operators.