高速滚齿加工工艺参数GABP预测模型NSGA-Ⅱ优化  

Optimization of NSGA-ⅡGABP Prediction Model for Machining Parameters of High Speed Gear Hobbing Machine

作　　者：王玉峰 于淼 李峰[3] WANG Yufeng;YU Miao;LI Feng(Puyang Technician College,Puyang 457099,China;Henan Economic and Trade Technician College,Xinxiang 453001,China;Henan University of Science and Technology,Zhengzhou 450064,China)

机构地区：[1]濮阳技师学院,河南濮阳457099 [2]河南经济贸易技师学院,河南新乡453001 [3]河南科技大学,河南郑州450064

出　　处：《机械制造与自动化》2025年第2期83-86,共4页Machine Building & Automation

基　　金：河南省科技创新人才计划项目(174100510007)。

摘　　要：为了进一步优化高速条件下滚齿工艺参数,以遗传反向传播算法(GABP)为目标设计预测模型,获得匹配滚齿工艺最优条件。采用新的非支配遗传算法NSGA-Ⅱ设计相应的优化数学模型,优化达到最低能耗以及最长的刀具使用期限。实验结果表明:该模型获得0.000425的最佳误差,达到出色的稳定性。GABP算法使刀具寿命误差减小16%,能量消耗减小36%,具备更优收敛性能。经过优化的Pareto解集在加工能耗和刀具寿命有显著下降,实现加工能耗和刀具寿命处于最佳平衡状态。该研究结果对优化滚齿加工工艺参数以及提高机加工效率具有很好的实际应用价值。To further optimize the gear hobbing process parameters under high speed conditions,the genetic back propagation algorithm(GABP)is used as the target to set a prediction model and obtain the optimal conditions for matching gear hobbing process.A new non-dominated genetic algorithm NSGA-Ⅱis applied to design the corresponding optimization mathematical model,which optimizes the lowest energy consumption and the longest tool life.The experimental results show that the optimal error of the model acquires 0.000425 with excellent stability.GABP algorithm can reduce the tool life error by 16%,energy consumption by 36%,with better convergence performance.The optimized Pareto solution set has a significant decrease in the machining energy consumption and tool life,achieveing an optimal balance between the machining energy consumption and tool life.The research has a very good practical value for optimizing hobbing process parameters and improving machining efficiency.

关 键 词：滚齿 工艺参数 BP神经网络 遗传算法 多目标优化 

分 类 号：TH161[机械工程—机械制造及自动化]