Data-driven based optimization for high-speed dry cutting gear hobbing processing parameters. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Wu, Dayuan; Yan, Ping; Pei, Jie; Zhou, Hui; Yi, Runzhong; Guo, You
• Is Part Of: Journal of physics. Volume 1827:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1827/1/012037
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The formulation of gear hobbing processing parameters relies on extensive professional knowledge and artificial experience, and inappropriate processing parameters will lead to low machining precision and high manufacturing costs. In order to obtain suitable processing parameters in high speed dry hobbing gear process, Gradient boosting regression (GBR) and Generalized Regression Neural Network (GRNN) are adopted to establish a process parameter optimization model targeting gear machining accuracy and machining energy consumption. And then a method based on Differential Evolution (DE) algorithm, parameter adaptive multi-object differential evolution (AMDE), for hobbing processing parameter optimization is proposed. Machining experiment verification shows that the established gear hobbing accuracy prediction model and energy consumption estimation model have high prediction accuracy, and AMDE has strong optimization capabilities and robustness.