基于GA-BP神经网络的微磨具磨损预测研究

田苗; 于康宁; 任莹晖; 佘程熙; 易峦

doi:10.13394/j.cnki.jgszz.2023.0074

基于GA-BP神经网络的微磨具磨损预测研究

doi: 10.13394/j.cnki.jgszz.2023.0074

田苗^1,,
于康宁^1,,
任莹晖^1, ,,
佘程熙¹,
易峦²

1.
湖南大学机械与运载工程学院，长沙 410082
2.
长沙矿冶研究院有限责任公司，长沙 410012

基金项目: 国家自然科学基金（52075161）

详细信息

通讯作者:
任莹晖，女，1979年出生，博士、副教授、博士研究生导师。主要研究方向为智能制造信息系统研究及开发、多能场复合微细加工技术及装备、难加工材料精密/超精密加工技术。E-mail：rebecca_ryh@163.com

中图分类号: TH162; TG58
计量
- 文章访问数: 21
- HTML全文浏览量: 8
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2023-03-25
- 修回日期: 2023-06-07
- 录用日期: 2023-06-07
- 网络出版日期: 2024-06-28
- 刊出日期: 2024-06-28

Wear prediction of micro-grinding tool based on GA-BP neural network

1.
School of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
2.
Changsha Research Institute of Mining and Metallurgy Co., Ltd., Changsha 410012, China

摘要

摘要: 为提高硬脆材料微结构的加工效率和精度，需要预测微磨具的不确定性磨损。基于微磨具在位视觉磨损检测和聚类分析，提出基于遗传算法的反向神经网络（genetic algorithm back propagation，GA-BP）模型。选取微磨具磨头截面面积损失量为指标，以表征微磨具不确定性磨损特征。利用K-均值聚类算法划分微磨具磨损状态阶段。最后构建以主轴转速、进给率、微槽深度、磨削长度和微磨具初始截面面积为输入层神经元，以磨头截面面积损失量预测值为输出层的GA-BP神经网络模型。设计不同工艺参数条件下的单晶硅微槽微细磨削实验，基于自搭建的机器视觉系统在位测量微磨具的磨头截面面积磨损量。将实验测得的微磨具磨损量作为训练数据，与传统高斯过程回归预测模型对比，验证GA-BP神经网络模型的有效性和准确性。结果表明，GA-BP神经网络模型能够实现不同工艺参数和不同磨削长度下的微磨具磨损预测，比传统高斯过程回归预测模型具有更高预测精度，平均误差精度达到5%，可以实现微磨具磨损阶段状态预测。
- 微磨具 /
- 磨损预测 /
- GA-BP神经网络 /
- 聚类分析
Abstract: An intelligent tool wear prediction model has been proposed for the micro-grinding tool, optimized using a genetic algorithm (GA) based BP neural network. The GA-BP prediction model is applied with in-situ tool wear detection to obtain training set data and combines cluster analysis to divide the tool wear stages. To represent the uncertainty in wear characteristics, the loss of cross-sectional area of the micro-grinding tool has been selected as an index to evaluate tool wear loss. The K-means clustering algorithm is used to cluster and analyze the tool wear stages under different process parameters. The GA-BP neural network includes five neurons in the input layer: rotating speed, feed rate, cutting depth, grinding length, and the initial cross-sectional area of the tool. The output layer neuron predicts the loss of the tool's cross-sectional area. To validate the method, a series of micro-grinding experiments were performed under different parameters for the micro-groove array of monocrystalline silicon. The loss of the tool's cross-sectional area was measured by a self-made visual inspection system, providing learning samples for the prediction model. The predicted results of the GA-BP neural network model were compared with the traditional Gaussian process regression method. The results show that the GA-BP neural network model can correctly predict tool wear loss and identify wear stages under different process parameters and grinding lengths. It has higher prediction accuracy during the self-learning process, with an average error of 5% .
- micro-grinding tools /
- wear prediction /
- GA-BP neural network /
- cluster analysis

HTML全文

图 1 微磨具不确定性磨损

Figure 1. Uncertain wear of micro-grinding tools

下载: 全尺寸图片幻灯片

图 2 微磨具磨头截面面积损失量计算示意图

Figure 2. Schematic diagram for calculating loss of cross-sectional area of grinding head

下载: 全尺寸图片幻灯片

图 3 基于GA-BP网络模型的微磨具磨损预测方法流程

Figure 3. Sketch of technological process for micro-grinding tool wear prediction method based on GA-BP neural network

下载: 全尺寸图片幻灯片

图 4 单晶硅微槽微细磨削实验平台

Figure 4. Experimental platform for micro-grinding of monocrystalline silicon microgrooves

下载: 全尺寸图片幻灯片

图 5 微磨具磨头直径损失量随磨削长度的变化趋势

Figure 5. Variation trend of diameter loss of grinding head with grinding length

下载: 全尺寸图片幻灯片

图 6 微磨具磨头截面面积损失量随磨削长度的变化趋势

Figure 6. Variation trend of section area loss of micro-grinding head with grinding length

下载: 全尺寸图片幻灯片

图 7 单因素实验测得磨头截面面积损失量与磨削长度关系

Figure 7. Relationship between loss of cross-sectional area of grinding head measured by single factor experiment

下载: 全尺寸图片幻灯片

图 8 磨头截面面积损失量聚类图

Figure 8. Cluster diagram of loss of cross-sectional area of tool

下载: 全尺寸图片幻灯片

图 9 磨头截面面积损失量实验值与GA-BP预测值

Figure 9. Experimental value and GA-BP model predicted value of cross-sectional area loss of tool

下载: 全尺寸图片幻灯片

图 10 相同工况下的磨头截面面积损失量

Figure 10. Cross-sectional area loss of tool under same working condition

下载: 全尺寸图片幻灯片

图 11 变工况下的磨头截面面积损失量

Figure 11. Cross-sectional area loss of tool under variable working conditions

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表 1 微磨具磨头直径

Table 1. Diameter of micro-grinders

微槽数	预实验A d_A / μm	预实验B d_B / μm	预实验C d_C / μm
0	414	423	413
1	411	421	410
2	407	416	404
3	409	414	403
4	405	411	402
5	406	412	404
6	404	412	404
7	－	411	402
8	－	411	402
9	－	－	403
10	－	－	401

下载: 导出CSV

表 2 微磨具磨头截面面积

Table 2. Cross-sectional area of micro-grinders

微槽数	预实验A S_A / μm²	预实验B S_B / μm²	预实验C S_C / μm²
0	35099.90	36334.72	35005.37
1	34911.55	36150.37	34790.47
2	34787.20	36019.83	34690.84
3	34760.29	35969.66	34646.12
4	34699.21	35934.75	34586.85
5	34642.48	35875.12	34538.49
6	34591.21	35832.94	34494.86
7	－	35780.94	34454.13
8	－	35720.22	34409.41
9	－	－	34275.97
10	－	－	34096.34

下载: 导出CSV

表 3 工艺实验参数表

Table 3. Processing parameters

组号	主轴转速n / (r·min⁻¹)	进给率v_f / (mm·min⁻¹)	微槽深度h / μm
1	20000	1.0	100
2	40000	1.0	100
3	60000	1.0	100
4	80000	1.0	100
5	100000	1.0	100
6	60000	0.4	100
7	60000	0.7	100
8	60000	1.3	100
9	60000	1.6	100
10	60000	1.0	50
11	60000	1.0	80
12	60000	1.0	150
13	60000	1.0	200
14	60000	1.0	250
15	60000	0.5	200

下载: 导出CSV

表 4 不同隐含层节点数的网络模型误差精度

Table 4. Error accuracy of network model with different number of hidden layer nodes

隐含层节点数 N	均方误差
4	0.0126
5	0.0111
6	0.0070
7	0.0045
8	0.0020
9	0.0061
10	0.0085
11	0.0062
12	0.0031

下载: 导出CSV

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