Data-driven modeling and integrated optimization of machining quality and energy consumption for internal gear power honing process

Abstract

The internal gear power honing process is increasingly used in the gear machining of electric vehicles due to superior tooth surface quality. Most of the existing work only investigates the quality improvement of gear machining processes, and focuses little attention on energy saving. However, the total rated power of multi-axis motion for gear honing process reaches 60 kW, which has great energy-saving potential. To this end, this article proposes a data-driven modeling and integrated optimization method of machining quality and energy consumption for internal gear power honing process. The machining quality formation mechanism and energy consumption characteristics of gear honing process are first analyzed. A gradient-enhanced Kriging (GEK) method is then used to establish data-driven tooth profile form deviation model and energy consumption model. Furthermore, an integrated honing process optimization model considering tooth profile form deviation and energy consumption is constructed. An improved multi-objective coati optimization algorithm (IMOCOA) is used to solve the optimization problem. The experimental results show that the R-square of the GEK model reaches 0.99, which has superior modeling accuracy compared with other methods. The optimization results demonstrate that compared with the empirical scheme, the proposed integrated optimization model reduces the tooth profile form deviation and energy consumption by 38.46 % and 10.26 %, respectively. Moreover, the developed IMOCOA also presents competitive algorithm performance. The proposed integrated optimization scheme significantly balances honing machining quality and energy consumption.