| 辛明泽,孙兴伟,张维锋,杨赫然,刘 寅.螺杆转子盘铣刀铣削表面粗糙度预测[J].电子测量与仪器学报,2023,37(12):204-212 |
| 螺杆转子盘铣刀铣削表面粗糙度预测 |
| Prediction of surface roughness of screw rotor disc milling cutter |
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| DOI： |
| 中文关键词: 铣削 螺旋曲面 表面粗糙度 北方苍鹰搜索算法 神经网络预测 |
| 英文关键词:milling spiral surface surface roughness northern goshawk search algorithm neural network prediction |
| 基金项目:辽宁省应用基础研究计划项目 (2022JH2 / 101300214)、辽宁省教育厅基本科研项目面上项目(LJKMZ20220459)、国家自然科学基金(52005346)项目资助 |
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| Author | Institution |
| Xin Mingze | 1. College of Mechanical Engineering, Shenyang University of Technology, 2. Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province |
| Sun Xingwei | 1. College of Mechanical Engineering, Shenyang University of Technology, 2. Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province |
| Zhang Weifeng | 1. College of Mechanical Engineering, Shenyang University of Technology, 2. Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province |
| Yang Heran | 1. College of Mechanical Engineering, Shenyang University of Technology, 2. Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province |
| Liu Yin | 1. College of Mechanical Engineering, Shenyang University of Technology, 2. Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province |
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| 中文摘要: |
| 螺杆转子主要应用在压缩机、螺杆泵等设备中,其表面质量对使用性能及工作寿命起到关键性作用。 工艺参数为影响
螺杆转子表面粗糙度的主要因素之一。 为了探究工艺参数对螺旋曲面铣削表面质量的影响规律,设计转子铣削实验,获取预测
及实验对比样本。 利用改进的北方苍鹰搜索算法(INGO)对 BP 神经网络的初始权值和阈值进行优化,以便提高铣削后的多头
螺杆转子表面粗糙度的预测精度。 通过实验结果验证所提出算法的预测精度。 结果表明,提出的预测模型在平均训练精度及
预测精度等方面均优于 GRU 神经网络及 CNN-GRU 神经网络模型,其中平均训练精度及预测精度分别约为 94. 502% 和
95. 523%。 故提出的算法具有较高的预测精度,可为合理选择螺杆转子铣削加工的工艺参数提供理论依据。 |
| 英文摘要: |
| Screw rotors are mainly used in compressors, screw pumps and other equipment, and their surface quality plays a key role in
service performance and service life. Process parameters are one of the main factors affecting the surface roughness of screw rotors. In
order to explore the influence of process parameters on the surface quality of helical surface milling, a rotor milling experiment was
designed to obtain prediction and experimental comparison samples. The improved northern goshawk search algorithm (INGO) is used to
optimize the initial weights and thresholds of the BP neural network, so as to improve the prediction accuracy of the surface roughness of
the milled multi-head screw rotor. Experimental results verify the prediction accuracy of the proposed algorithm. The results show that
the proposed prediction model outperforms GRU neural network and CNN-GRU neural network models in terms of average training
accuracy and prediction accuracy. The average training accuracy and prediction accuracy are about 94. 502% and 95. 523% respectively.
Therefore, the proposed algorithm has high prediction accuracy and can provide a theoretical basis for reasonable selection of processing
parameters of screw rotor milling. |
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