杨赫然,张培杰,孙兴伟,潘飞,董祉序,刘寅.基于麻雀算法优化神经网络的螺杆砂带磨削去除深度预测[J].表面技术,2025,54(2):182-190.
YANG Heran,ZHANG Peijie,SUN Xingwei,PAN Fei,DONG Zhixu,LIU Yin.Prediction of Removal Depth in Screw Belt Grinding Based on the Neural Network Optimized by Sparrow Algorithm[J].Surface Technology,2025,54(2):182-190 |
| 基于麻雀算法优化神经网络的螺杆砂带磨削去除深度预测 |
| Prediction of Removal Depth in Screw Belt Grinding Based on the Neural Network Optimized by Sparrow Algorithm |
| 投稿时间：2024-04-28 修订日期：2024-07-24 |
| DOI：10.16490/j.cnki.issn.1001-3660.2025.02.015 |
| 中文关键词: 材料去除深度 砂带磨削 预测 螺杆转子 |
| 英文关键词:MRD abrasive belt grinding prediction screw rotor |
| 基金项目:辽宁省教育厅高等学校基本科研项目面上项目(LJKMZ20220459，JYTMS20231190);辽宁省应用基础研究计划项目(2022JH2/ 101300214);“兴辽英才计划”青年拔尖人才项目(XLYC2203190) |
| 作者 | 单位 |
| 杨赫然 | 沈阳工业大学 机械工程学院 辽宁省复杂曲面数控制造技术重点实验室，沈阳 110870 |
| 张培杰 | 沈阳工业大学 机械工程学院 辽宁省复杂曲面数控制造技术重点实验室，沈阳 110870 |
| 孙兴伟 | 沈阳工业大学 机械工程学院 辽宁省复杂曲面数控制造技术重点实验室，沈阳 110870 |
| 潘飞 | 沈阳工业大学 机械工程学院 辽宁省复杂曲面数控制造技术重点实验室，沈阳 110870 |
| 董祉序 | 沈阳工业大学 机械工程学院 辽宁省复杂曲面数控制造技术重点实验室，沈阳 110870 |
| 刘寅 | 沈阳工业大学 机械工程学院 辽宁省复杂曲面数控制造技术重点实验室，沈阳 110870 |
|
| Author | Institution |
| YANG Heran | School of Mechanical Engineering,Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang 110870, China |
| ZHANG Peijie | School of Mechanical Engineering,Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang 110870, China |
| SUN Xingwei | School of Mechanical Engineering,Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang 110870, China |
| PAN Fei | School of Mechanical Engineering,Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang 110870, China |
| DONG Zhixu | School of Mechanical Engineering,Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang 110870, China |
| LIU Yin | School of Mechanical Engineering,Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang University of Technology, Shenyang 110870, China |
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| 中文摘要: |
| 目的 准确获得螺杆转子砂带磨削材料去除深度，探究工艺参数对材料去除深度的影响。方法 通过分析螺杆转子砂带磨削去除机理，确定材料去除深度影响因素。提出一种基于麻雀算法优化长短时记忆网络-卷积神经网络模型(SSA-CNN-LSTM)，对螺杆转子砂带磨削加工中的材料去除深度进行预测。以磨削中影响材料去除深度的因素为输入，磨削深度为输出，构建利用SSA对CNN-LSTM超参数进行寻优的预测模型，并且与CNN-LSTM、LSTM、PSO-BP、RBF以及随机森林预测方法进行对比。结果 所提预测方法平均绝对百分比误差MAPE可达0.046 1，均方根差RMSE为9.261，平均绝对误差MAE达7.836，确定系数R2为0.997 4，相比于未优化CNN-LSTM网络和其他经典网络模型，预测精度更高，能够有效预测螺杆转子磨削材料去除深度。利用提出的模型探究了磨削工艺参数对材料去除深度MRD和材料去除一致性的影响。由预测结果可知，螺杆转子砂带磨削材料去除深度随法向压力、砂带线速度增加而增大，随进给速度、砂带目数增加而减小，且受法向压力影响最明显。通过对磨削前后工件廓形进行分析可知，材料去除深度随磨削区域受到的法向压力呈现中间磨削深度大、两侧逐渐减小的趋势。结论 提出的预测模型预测效果显著，分析了工艺参数对磨削材料去除深度和去除一致性的影响规律，可为其他类型的加工轮廓预测提供参考。 |
| 英文摘要: |
| The work aims to accurately obtain the material removal depth of screw rotor belt grinding and explore the effect of process parameters on the material removal depth. The affecting factors of material removal depth were determined by analyzing the removal mechanism of screw rotor belt grinding. A long and short-term memory network-convolutional neural network model (SSA-CNN-LSTM) optimized based on the sparrow algorithm was proposed to predict the material removal depth in screw rotor belt grinding process. The grinding tools developed to accommodate concave and convex belt grinding of rotors were divided into two types, a contact wheel type belt grinding mechanism and a free-form belt grinding mechanism. With the factors affecting the depth of material removal in grinding as inputs and the grinding depth as outputs, the prediction model adopting SSA for optimization of CNN-LSTM hyperparameters was constructed and compared with CNN-LSTM, LSTM, PSO-BP, RBF, and random forest prediction methods. In the experiment, the normal pressure Fs and tension force Fm of the abrasive belt were controlled by the main cylinder and the tensioning cylinder respectively. In order to ensure that the abrasive belt and the grinding surface were in full contact during the grinding process, the pressure of the main cylinder was set to be larger than the pressure of the tensioning cylinder by 0.1-0.3 MPa. The quality of the grinding and the stability of the grinding device were fully considered and the linear velocity of the abrasive belt was set to 4.4-13.1 m/s. The feed speed was decided to determine the grinding time and was set to 100-300 mm/min in order to guarantee the appropriate grinding depth range. In order to ensure a suitable grinding depth range, it was set at 100~300 mm/min, the abrasive belt grit was zirconia grit belt with 80-240 mesh and the grinding time was set at 0-20 min considering the wear rate of different grit sizes. The proposed prediction method has an average absolute percentage error MAPE up to 0.046 1, a root mean square error RMSE of 9.261, an average absolute error MAE up to 7.836, and a coefficient of determination R2 of 0.997 4, which provides a higher prediction accuracy and is able to efficiently predict the depth of grinding material removal from screw rotors compared to the unoptimized CNN-LSTM network and other classical network models. The effects of grinding process parameters on material removal depth MRD and material removal consistency are explored by the proposed model. From the prediction results, it can be seen that the material removal depth of screw rotor belt grinding increases with the increase of normal pressure and belt linear speed, decreases with the increase of feed rate and belt grit size and is most affected by normal pressure. By analyzing the contour of the workpiece before and after grinding, it can be seen that the depth of material removal with the normal pressure on the grinding area shows a trend of large grinding depth in the middle and a gradual decrease on both sides. The prediction model proposed has a significant prediction effect and analyzes the effect of process parameters on the grinding material removal depth and removal consistency, which can provide reference for the prediction of other types of machining contours. |
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