张友栋,肖贵坚,柴东升,高慧,黄云.钛合金表面缓进给砂带磨削变参数优化方法及其试验研究[J].表面技术,2023,52(2):1-13.
ZHANG You-dong,XIAO Gui-jian,CAI Dong-sheng,GAO Hui,HUANG Yun.Experimental Study on Variable Parameter Optimization Method of Surface Slow Feed Abrasive Belt Grinding of Titanium Alloy[J].Surface Technology,2023,52(2):1-13 |
| 钛合金表面缓进给砂带磨削变参数优化方法及其试验研究 |
| Experimental Study on Variable Parameter Optimization Method of Surface Slow Feed Abrasive Belt Grinding of Titanium Alloy |
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| DOI：10.16490/j.cnki.issn.1001-3660.2023.02.001 |
| 中文关键词: 钛合金 参数优化 NSGA-Ⅱ 砂带磨损 变参数加工 |
| 英文关键词:titanium alloy parameter optimization NSGA-Ⅱ abrasive belt wear variable parameter machining |
| 基金项目:国家自然科学基金面上基金项目(52175377);国家科技重大专项(2017-VII-0002-0095) |
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| Author | Institution |
| ZHANG You-dong | College of Mechanical and Vehicle Engineering,Chongqing 400044, China |
| XIAO Gui-jian | College of Mechanical and Vehicle Engineering,Chongqing 400044, China ;State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China |
| CAI Dong-sheng | China Hangfa Shenyang Liming Aeroengine Co., Ltd., Shenyang 110043, China |
| GAO Hui | College of Mechanical and Vehicle Engineering,Chongqing 400044, China |
| HUANG Yun | College of Mechanical and Vehicle Engineering,Chongqing 400044, China |
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| 中文摘要: |
| 目的 改善钛合金砂带全生命周期中磨削的表面质量。方法 提出了钛合金缓进给砂带磨削变参数优化方法。首先，采集磨削过程中的加工参数、砂带磨损、表面粗糙度等数据。其次，采用SVM算法构建以磨削参数和磨损数据为输入、以表面粗糙度为输出的粗糙度预测模型，并且以预测的粗糙度和砂带磨损为约束应用NSGA-Ⅱ算法，针对缓进给砂带磨削过程中的全生命周期的加工参数进行优化。最后，通过对比分析变参数和固定参数磨削方法下的砂带磨损特点和钛合金表面粗糙度、形貌特征、微观特征、表面氧化的特点，对砂带全生命周期变参数磨削方法进行验证。结果 SVM预测的精度可达0.95以上，MAE低至0.064。采用NSGA-Ⅱ算法优化后的加工参数能够有效地改善表面质量，优化前的全生命周期中的粗糙度从0.787 μm逐渐降低至0.509 μm，优化后的粗糙度从0.934 μm降低至0.457 μm;并且优化后的钛合金形貌要优于传统的加工方式，变参数磨削的钛合金表面氧化程度明显小于固定参数磨削方法。此外，提出的变参数优化方法能够有效地改善砂带的磨损，降低缓进给磨削所带来的砂带快速磨损现象。结论 本文所提出的SVM-NSGA-Ⅱ磨削参数优化算法能够搜索到满意的解值，得到优化后的加工参数。磨削对比试验表明，本文提出的变参数砂带磨削方式相对于固定参数磨削，能够有效地提高磨削的表面质量，减缓砂带的磨损，延长砂带的使用寿命。 |
| 英文摘要: |
| In order to improve the rapid wear of abrasive belt and its influence on the surface of titanium alloy when grinding titanium alloy and other difficult-to-machine materials with abrasive belt in the whole life cycle. A variable parameter optimization method of grinding parameters based on slow feed abrasive belt grinding mode is proposed. Firstly, the pre-experiment of abrasive belt wear in the whole life cycle of titanium alloy processing is carried out, and the processing parameters, abrasive belt wear quality and surface roughness of the processing process are collected, so as to prepare for the training of the model. Secondly, SVM algorithm is used to build a roughness prediction model, and NSGA-Ⅱ algorithm is used to optimize the processing parameters in the whole life cycle of slow-feed abrasive belt grinding. Finally, by comparing and analyzing the abrasive belt wear characteristics and the characteristics of titanium alloy surface roughness, morphological characteristics, microscopic characteristics and surface oxidation under variable parameter and fixed parameter grinding methods, the variable parameter grinding method in the whole life cycle of abrasive belt is verified. The results show that the accuracy of roughness prediction based on SVM algorithm can reach above 0.95, and the mean absolute error (MAE) is as low as 0.064. By comparison, it can know that the prediction accuracy of the algorithm is higher at the end of abrasive belt wear, because the sampling frequency at the end of abrasive belt wear is relatively high. The processing parameters optimized by NSGA-Ⅱ algorithm can effectively improve the surface quality. The roughness of the whole life cycle before optimization gradually decreases from 2.049 μm to 0.184 μm, and the roughness after optimization decreases from 1.549 μm to 0.494 μm; Moreover, the surface morphology and oxidation degree were detected by SEM and EDS. During the whole abrasive belt wear process, the plastic flow of fixed parameter grinding method is more than that of variable parameter grinding method, and the oxidation reaction degree is also greater. In addition, using ultra-depth-of-field equipment to detect abrasive belts in different wear periods, the topography of abrasive belts is obtained, and it is found that the proposed variable parameter optimization method can effectively improve the abrasive belt wear and reduce the rapid abrasive belt wear caused by slow feed grinding. The SVM algorithm proposed in this paper can predict the roughness and the NSGA-Ⅱ algorithm can optimize the parameters of grinding line in abrasive belt. The optimal solution of machining can be found through this algorithm, and the optimal machining parameters can be obtained through calculation. Through the comparative experiment of fixed parameter grinding mode and variable parameter grinding mode, the grinding contrast experiment shows that the variable parameter abrasive belt grinding method proposed in this study can effectively improve the grinding surface quality (The roughness is relatively low, the plastic flow on the surface of titanium alloy is small, and the oxidation reaction on the surface can also be improved.), slow down the abrasive belt wear and prolong the service life of the abrasive belt compared with the fixed parameter grinding method. |
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