轴承表面Al<sub>2</sub>O<sub>3</sub>基陶瓷绝缘涂层的粗糙度预测

徐钰淳; 朱建辉; 师超钰; 王宁昌; 赵延军; 张高亮; 乔帅; 谷春青

doi:10.13394/j.cnki.jgszz.2023-0118

轴承表面Al₂O₃基陶瓷绝缘涂层的粗糙度预测

doi: 10.13394/j.cnki.jgszz.2023-0118

徐钰淳^{1, 2,},
朱建辉^{1, 2},
师超钰^{1, 2},
王宁昌^{1, 2},
赵延军^{1, 2},
张高亮²,
乔帅²,
谷春青²

1.
高性能工具全国重点实验室, 郑州 450001
2.
郑州磨料磨具磨削研究所有限公司, 郑州 450001

基金项目: 国家重点研发计划（2020YFB2007900）

详细信息

作者简介:
徐钰淳，男，1995年生，硕士、工程师。主要研究方向：精密磨削加工及过程监测。E-mail：yuchun-xu@foxmail.com

中图分类号: TG58; TG74; TQ164
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- 文章访问数: 269
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- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2023-05-22
- 修回日期: 2023-08-01
- 录用日期: 2023-08-17
- 网络出版日期: 2024-06-28
- 刊出日期: 2024-06-28

Roughness prediction of Al₂O₃-based ceramic insulation coating on bearing surface

XU Yuchun^{1, 2
,},
ZHU Jianhui^{1, 2},
SHI Chaoyu^{1, 2},
WANG Ningchang^{1, 2},
ZHAO Yanjun^{1, 2},
ZHANG Gaoliang²,
QIAO Shuai²,
GU Chunqing²

1.
State Key Laboratory of High Performance Tools, Zhengzhou 450001, China
2.
Zhengzhou Research Institute for Abrasives & Grinding Co., Ltd., Zhengzhou 450001, China

摘要

摘要:
为了提升轴承表面Al₂O₃基陶瓷绝缘涂层的粗糙度预测精度，提出基于光谱共焦原理的砂轮表面测量及磨粒特征参数量化方法，以砂轮表面的磨粒特征参数K，砂轮线速度v_s，工件进给速度f，切削深度a_p及法向磨削力F为输入参数，建立能够直接反映砂轮表面时变状态的工件表面粗糙度BP神经网络预测模型，并通过已知磨削样本及砂轮磨损后的4组未知样本对网络预测模型性能进行验证。结果表明：已知样本的BP网络模型粗糙度预测结果与实际结果的规律及数值较为一致，其网络输出误差均 < ± 0.04 μm；4组未知样本的网络预测精度下降，但其相对误差最大值的绝对值不超过20.00%。建立的包含砂轮表面磨粒特征参数的神经网络预测模型，可以适应砂轮磨粒磨损时变状态下的轴承表面Al₂O₃基陶瓷绝缘涂层的粗糙度预测，且其对未知样本具有一定的泛化能力。
- Al₂O₃基陶瓷 /
- 绝缘涂层 /
- 粗糙度预测 /
- BP神经网络 /
- 磨粒磨损
Abstract:
To improve the roughness prediction accuracy of Al₂O₃-based ceramic insulation coating on bearing surfaces, a method based on the spectral confocal principle was proposed for measuring the surface of grinding wheels and quantifying the characteristic parameters of abrasive particles. The abrasive characteristic parameter K of the grinding wheel surface, the grinding wheel line speed v_s, the workpiece feed speed f, the cutting depth a_p, and the normal grinding force F were taken as input parameters. A BP neural network prediction model of workpiece surface roughness, which directly reflects the time-varying state of the grinding wheel surface, was established. The prediction performance of the network model was verified using known grinding samples and four groups of unknown samples after grinding wheel wear. The results show that the predicted roughness results of the BP network model with known samples are consistent with the actual roughness results in terms of regularity and numerical values, with network output errors are all less than ± 0.04 μm. The network prediction accuracy for the four unknown samples decreases, but the absolute value of the maximum relative error does not exceed 20.00%. The neural network prediction model, which includes the characteristic parameters of abrasive particles on the grinding wheel surface , can be used to predict the roughness of Al₂O₃-based ceramic insulation coating on the bearing surface under the time-varying state of abrasive wear on the grinding wheel. It also demonstrates a certain generalization ability for unknown samples.
- Al₂O₃-based ceramics /
- insulating coating /
- roughness prediction /
- BP neural network /
- abrasive wear

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图 1 砂轮表面的光谱共焦系统测试原理

Figure 1. Test principle of spectral confocal system of grinding wheel surface

下载: 全尺寸图片幻灯片

图 2 基于磨削参数和砂轮表面状态的BP神经网络结构

Figure 2. BP neural network structure based on grinding parameters and grinding wheel surface state

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图 3 磨削现场

Figure 3. Grinding site

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图 4 Al₂O₃基陶瓷涂层轴承钢样品

Figure 4. Al₂O₃-based ceramic coating bearing steel sample

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图 5 BP网络结构图

Figure 5. BP network structure diagram

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图 6 网络训练的收敛过程

Figure 6. Convergence process of network training

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图 7 预测值与实际值对比

Figure 7. Comparison between predicted and actual values

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图 8 粗糙度误差

Figure 8. Roughness error

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图 9 砂轮表面的磨粒磨损对比

Figure 9. Comparison of abrasive wear on grinding wheel surface

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图 10 磨损前后砂轮表面的形貌数据测量结果

Figure 10. Measurement results of surface morphology data of grinding wheels before and after wear

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图 11 Al₂O₃基陶瓷涂层工件的表面形貌及粗糙度测量结果

Figure 11. Surface morphology and roughness measurement results of Al₂O₃-based ceramic coating workpieces

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表 1 正交试验因素及水平

Table 1. Orthogonal test factors and levels

水平	因素
水平	砂轮线速度 v_s / (m·s⁻¹) A	切深 a_p / μm B	工件进给速度 f / (mm·min⁻¹) C
1	15	5	300
2	25	15	900
3	35	25	1 500
4	45	35	2 100

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表 2 正交试验表

Table 2. Orthogonal test table

试验序号	A	B	C
1	15	5	2 100
2	15	15	1 500
3	15	25	900
4	15	35	300
5	25	5	1 500
6	25	15	900
7	25	25	300
8	25	35	2 100
9	35	5	900
10	35	15	300
11	35	25	2 100
12	35	35	1 500
13	45	5	300
14	45	15	2 100
15	45	25	1 500
16	45	35	900

下载: 导出CSV

表 3 1#砂轮表面磨粒的特征参数与磨削试验结果

Table 3. Characteristic parameters of abrasive particles on the surface of grinding wheel 1# and grinding resuilts

序号	特征参数 K	法向磨削力 F / N	表面粗糙度 S_a / μm
1	30.3	26.87	0.319
2	30.4	29.73	0.373
3	30.3	66.73	0.531
4	30.1	87.53	0.599
5	30.2	25.94	0.239
6	29.9	21.88	0.361
7	30.4	73.87	0.528
8	29.8	88.79	0.593
9	30.3	13.96	0.225
10	29.8	16.20	0.312
11	30.0	88.40	0.455
12	30.3	90.41	0.500
13	30.4	13.38	0.202
14	29.7	84.48	0.332
15	30.6	72.02	0.365
16	30.4	88.51	0.545

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表 4 2#砂轮表面磨粒的特征参数与磨削试验结果

Table 4. Characteristic parameters of abrasive particles on the surface of grinding wheel 2# and grinding resuilts

序号	特征参数 K	法向磨削力 F / N	表面粗糙度 S_a / μm
1	15.8	40.73	0.254
2	15.8	34.05	0.312
3	15.4	25.55	0.453
4	15.5	73.24	0.507
5	15.7	33.69	0.204
6	15.4	39.63	0.309
7	16.1	90.16	0.430
8	15.4	86.58	0.486
9	15.4	27.80	0.178
10	15.4	32.49	0.268
11	15.4	89.57	0.387
12	15.6	103.64	0.423
13	15.5	38.41	0.151
14	16.1	38.37	0.287
15	15.6	103.69	0.315
16	15.4	104.42	0.467

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表 5 磨削试验参数

Table 5. Grinding test parameters

参数	规格或取值
砂轮编号	1#
砂轮线速度v_s / (m·s⁻¹)	20，32，37，50
工件进给速度 f / (mm·min⁻¹)	1400，900，600，200
切削深度 a_p / μm	24，27，11，5

下载: 导出CSV

表 6 磨削试验网络输入向量的试验值与预测值对比

Table 6. Comparison between experimental and predicted values of input vectors in grinding test networks

试验组	网络输入向量矩阵 q	表面粗糙度 S_a / μm		相对误差 δ / %
试验组	网络输入向量矩阵 q	实际	预测	相对误差 δ / %
1	[20,24,1400,20.1,73.64]	0.406	0.379	6.65
2	[32,27,900,20.5,59.19]	0.436	0.418	4.13
3	[37,11,600,20.3,17.66]	0.203	0.241	−18.72
4	[50,5,200,20.6,15.76]	0.170	0.198	−16.47

下载: 导出CSV

参考文献(14)

[1]	卜珍宇, 赵晓琴, 郭向东, 等. 电机轴承防护措施及Al₂O₃陶瓷绝缘涂层研究现状 [J]. 表面技术,2021,50(5):51-59. BU Zhenyu, ZHAO Xiaoqin, GUO Xiangdong, et al. Electromotor bearing protection measures and research status of Al₂O₃ ceramic coating [J]. Surface Technology,2021,50(5):51-59.
[2]	余剑武, 胡其丰, 文丞, 等. 基于支持向量机的电火花加工8418钢表面粗糙度预测模型 [J]. 中国机械工程,2018,29(7):771-774. YU Jianwu, HU Qifeng, WEN Chen, et al. Prediction model of surface roughness of 8418 steel by EDM based on SVM [J]. China Mechanical Engineering,2018,29(7):771-774.
[3]	PAN Y, WANG Y, ZhOU P, et al. Activation functions selection for BP neural network model of ground surface roughness [J]. Journal of Intelligent Manufacturing,2020(31):1825-1836. doi: 10.1007/s10845-020-01538-5
[4]	任敬心, 华定安. 磨削原理[M]. 西安: 西北工业大学出版社, 1988: 206-612. REN Jingxin, HUA Anding. Grinding principle [M]. Xi＇an: Northwestern Polytechnical University Press, 1988: 206-216.
[5]	NAMETALA C A L, SOUZA A M, JÚNIOR B R P, et al. A simulator based on artificial neural networks and NSGA-II for prediction and optimization of the grinding process of superalloys with high performance grinding wheels. [J]. CIRP Journal of Manufacturing Science and Technology,2020(30):157-173. doi: 10.1016/j.cirpj.2020.05.004
[6]	YANG J, ZhANG L, LIU G, et al. Sintered silicon carbide grinding surface roughness prediction based on deep learning and neural network [J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering,2022,44(7):1-13.
[7]	XIAO G. Prediction of surface roughness of abrasive belt grinding of superalloy material based on RLSOM-RBF [J]. Materials,2021,14(19):5701.
[8]	GUO W, WU C, DING Z, et al. Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding [J]. The International Journal of Advanced Manufacturing Technology,2021(112):2853-2871. doi: 10.1007/s00170-020-06523-z
[9]	LAI J Y, LIN P C. Grinded surface roughness prediction using data-driven models with contact force information: 2022 IEEE/ASME international conference on advanced intelligent mechatronics (AIM) [C]. Sapporo: IEEE, 2022: 983-989.
[10]	MIRIFAR S, KADIVAR M, AZARHOUSHANG B. First steps through intelligent grinding using machine learning via integrated acoustic emission sensors [J]. Journal of Manufacturing and Materials Processing, 2020, 4(2): 35.
[11]	董志刚, 马槐遥, 康仁科, 等. SiC_f/SiC复合材料超声辅助干式侧磨砂轮磨损研究 [J]. 机械工程学报,2022,58(15):134-143. doi: 10.3901/JME.2022.15.134 DONG Zhigang, MA Huaiyao, KANG Renke, et al. Study on wear of grinding wheel in ultrasonic assisted dry side grinding of SiC_f/SiC composites [J]. Journal of Mechanical Engineering,2022,58(15):134-143. doi: 10.3901/JME.2022.15.134
[12]	胡天荣, 郑红伟, 戴士杰. 基于异类信息融合的砂轮磨损状态监测 [J]. 工具技术,2023,57(2):126-131. HU Tianrong, ZHENG Hongwei, DAI Shijie. Grinding wheel wear condition monitoring based on heterogeneous information fusion [J]. Tool Engineering,2023,57(2):126-131.
[13]	王津楠, 陈凤东, 刘炳国, 等. 基于白光LED的光谱共焦位移传感器 [J]. 中国测试,2017,43(1):69-73. WANG Jinnan, CHEN Fengdong, LIU Bingguo, et al. White LED-based spectrum confocal displacement sensor [J]. China Measurment & Test,2017,43(1):69-73.
[14]	王嵘冰, 徐红艳, 李波, 等. BP神经网络隐含层节点数确定方法研究 [J]. 计算机技术与发展,2018,28(4):31-35. WANG Rongbing, XU Hongyan, LI Bo, et al. Research on method of determining hidden layer nodes in BP neural network [J]. Computer Technology and Development,2018,28(4):31-35.