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: 29
- HTML全文浏览量: 7
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2023-05-22
- 修回日期: 2023-08-01
- 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

HTML全文

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

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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

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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

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