李颂华,李祥宇,孙健.Si3N4陶瓷轴承套圈端面磨削实验及表面质量分析[J].表面技术,2021,50(10):363-372.
LI Song-hua,LI Xiang-yu,SUN Jian.Surface Grinding Experiment and Surface Quality Analysis of Si3N4 Ceramic Bearing Ring[J].Surface Technology,2021,50(10):363-372 |
| Si3N4陶瓷轴承套圈端面磨削实验及表面质量分析 |
| Surface Grinding Experiment and Surface Quality Analysis of Si3N4 Ceramic Bearing Ring |
| 投稿时间：2020-12-24 修订日期：2021-03-01 |
| DOI：10.16490/j.cnki.issn.1001-3660.2021.10.038 |
| 中文关键词: 氮化硅陶瓷 陶瓷轴承 双端面磨削 单因素实验 表面粗糙度 预测模型 |
| 英文关键词:silicon nitride ceramic ceramic bearing double-face grinding single factor experiment surface roughness prediction model |
| 基金项目:国家自然科学基金资助项目(51975388);辽宁省自然科学基金(2019-MS-266, 2019-ZD-0666, 2020-BS-159);辽宁省百千万人才工程资助计划(2018921009) |
|
|
| Author | Institution |
| LI Song-hua | School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China, ;National-Local Joint Engineering Laboratory of NC Machining Equipment and Technology of High-Grade Stone, Shenyang 110168, China |
| LI Xiang-yu | School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China, |
| SUN Jian | School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China, |
|
| 摘要点击次数: |
| 全文下载次数: |
| 中文摘要: |
| 目的 确定加工氮化硅陶瓷轴承套圈端面的最优磨削加工参数，并构建表面粗糙度的预测模型。方法 首先，使用双端面磨床对氮化硅陶瓷轴承套圈进行多组单因素实验，实验设置的2个变量分别为砂轮转速和砂轮进给速度，并对两变量分别设置4个加工参数水平，以分析砂轮进给速度和砂轮转速对加工后表面质量的影响;再利用MATLAB中的工具箱，构建表面粗糙度预测模型。结果 通过实验得到最优的加工参数(砂轮转速为1400 r/min，砂轮进给速度为200 μm/min)，最优的表面粗糙度达到0.0827 μm，符合工程中对高精度全陶瓷轴承端面的质量要求。建立了预测模型，并对该预测模型进行了优化，优化后的预测模型较实际测量的表面粗糙度Ra绝对值最小的相对误差为–0.56%，预测值与实际测量的表面粗糙度值的最大误差为 0.0113 μm。结论 表面粗糙度与砂轮转速和砂轮进给速度呈负相关，从实验结果与预测模型中可以看出，随着砂轮转速和砂轮进给速度的提高，表面粗糙度呈下降趋势。磨削氮化硅陶瓷轴承套圈的端面时，适当提高砂轮转速和砂轮进给速度有助于降低表面粗糙度，提高表面质量。 |
| 英文摘要: |
| The purpose of this work is to determine the optimal grinding parameters for processing the end faces of silicon nitride ceramic bearing rings and to construct a predictive model of surface roughness. First, a double-face grinder is used to carry out multiple sets of single factor experiments on silicon nitride ceramic bearing rings. The two variables set in the experiment are the grinding wheel speed and the grinding wheel feed speed, and four processing parameter levels are set for the two variables respectively. Analyze the influence of the grinding wheel feed speed and the grinding wheel speed on the surface quality after processing, and then use the toolbox in MATLAB to construct the surface roughness prediction model. The optimal processing parameters obtained through experiments are:the grinding wheel speed is 1400 r/min, the grinding wheel feed speed is 200 μm/min, and the optimal surface roughness obtained is 0.0827 μm, which is in line with the quality of high-precision all-ceramic bearing end faces in engineering Claim. This paper established a surface roughness prediction model and optimized the prediction model. Compared with the actual measured surface roughness value, the absolute value of the optimized prediction model has the smallest relative error of –0.56%, and the maximum error of Ra is 0.0113 μm. According to the experimental results, the surface roughness is negatively correlated with the speed of the grinding wheel and the feed speed of the grinding wheel. From the experimental results and the prediction model, it can be seen that as the speed of the grinding wheel and the feed speed of the grinding wheel increase, the surface roughness value Ra shows a downward trend. It is concluded that when grinding the end face of the silicon nitride ceramic bearing ring, appropriately increasing the speed of the grinding wheel and the feeding speed of the grinding wheel can help reduce the surface roughness and improve the surface quality. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
渝公网安备 50010702501715号