基于CNN的金刚石砂轮激光修锐参数优化

高孟阳; 陈根余; 李玮; 周伟; 李杰

doi:10.13394/j.cnki.jgszz.2022.0018

基于CNN的金刚石砂轮激光修锐参数优化

doi: 10.13394/j.cnki.jgszz.2022.0018

高孟阳^{1, 2},
陈根余^{1, 2,},
李玮^{1, 3},
周伟^{1, 2},
李杰¹

1.
湖南大学激光研究所, 长沙 410082
2.
湖南大学，国家高效磨削工程技术研究中心, 长沙 410082
3.
湖南大学信息科学与工程学院, 长沙 410082

基金项目: 广东省重点领域研发计划（2020B090924005）。

详细信息

作者简介:
陈根余，男，1965年生，博士、教授、博士生导师。主要研究方向：激光制造及激光微细加工技术。E-mail：hdgychen@163.com

中图分类号: TQ164；TG74
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- 文章访问数: 601
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- 被引次数: 0
出版历程
- 收稿日期: 2022-03-07
- 修回日期: 2022-04-21
- 录用日期: 2022-04-26
- 刊出日期: 2022-10-10

Optimization of laser sharpening parameters for diamond grinding wheel based on CNN

GAO Mengyang^{1, 2},
CHEN Genyu^{1, 2
,},
LI Wei^{1, 3},
ZHOU Wei^{1, 2},
LI Jie¹

1.
Institute of Laser Technology, Hunan University, Changsha 410082, China
2.
National High Efficiency Grinding Engineering Technology Research Center, Hunan University, Changsha 410082, China
3.
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China

摘要

摘要: 采用正交试验法对青铜金刚石砂轮进行激光修锐试验，并对其激光修锐参数进行优化。通过卷积神经网络（convolutional neural network，CNN）对砂轮表面图片进行像素级的金刚石磨粒识别，提取磨粒面积信息，求出磨粒突出高度，利用统计分布规律得到突出高度得分和最佳区间比率2个激光修锐质量评价指标。利用提出的评价指标对试验得到的砂轮激光修锐图片进行质量评价，并进行极差分析。结果表明：平均功率是影响修锐质量最大的因素。最优的修锐工艺参数为：平均功率，35 W；重复频率，100 kHz；转速，300 r/min；扫描速度，1.0 mm/min。
- 青铜金刚石砂轮 /
- 修锐质量评价 /
- 卷积神经网络 /
- 激光修锐
Abstract: To optimize laser sharpening parameters for bronze diamond grinding wheels, the laser sharpening test was carried out on the bronze diamond grinding wheel using the orthogonal test method.The convolutional neural network (CNN) is used to identify the diamond abrasive grains at the pixel level. The protruding height of abrasive grains is obtained by extracting the area information of abrasive grains. Two laser sharpening quality evaluation indicators, the protruding height score and the optimal interval ratio, are obtained by using the statistical distribution law. The quality of the grinding wheel laser sharpening effect obtained by the test is evaluated by the evaluation index proposed and the range method is performed. The results show that the average power is the biggest factor affecting the quality of trimming. The optimal trimming process parameters are as follows: the average power is 35 W; the repetition frequency is 100 kHz; the rotational speed is 300 r/min; the scanning speed is 1.0 mm/min.
- bronze-diamond grinding wheel /
- sharpening quality evaluation /
- convolutional neural network /
- laser sharpening

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图 1 不同磨粒分割算法的分割结果

Figure 1. Segmentation results of different abrasive particle segmentation algorithms

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图 2 改进U-Net神经网络结构

Figure 2. Improved U-Net neural network structure

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图 3 超景深显微镜

Figure 3. Super depth of field microscope

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图 4 激光砂轮修整机床

Figure 4. Machine of laser dressing wheels

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图 5 激光修锐金刚石砂轮原理图

Figure 5. Schematic diagram of laser sharpening diamond grinding wheel

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图 6 1～16组磨粒分割结果

Figure 6. Grains segmentation results in group 1 to group 16

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图 7 1～16组磨粒突出高度分布

Figure 7. Abrasive grain protrusion height distribution in group 1 to group 16

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图 8 17组试验结果对比

Figure 8. Comparison of test results for 17 groups

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表 1 神经网络评价结果

Table 1. Neural network evaluation results

模型	平均交并比 ζ / %	准确率 η / %
U-Net	82.97	90.29
MobileNet v2 SE Net + Focal Loss	87.53	93.05

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表 2 正交试验因素与水平

Table 2. Orthogonal test factors and levels

水平	因素
水平	A 平均功率 P_m / W	B 重复频率 f₀ / kHz	C 转速 v_s / (r∙min⁻¹)	D 扫描速度 v / (mm∙min⁻¹)
1	25	70	150	1.0
2	30	80	200	2.5
3	35	90	250	4.0
4	40	100	300	5.5

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表 3 正交试验表

Table 3. Orthogonal test table

组号	因素水平组合
组号	A	B	C	D
1	1	1	1	1
2	1	2	2	2
3	1	3	3	3
4	1	4	4	4
5	2	1	2	3
6	2	2	1	4
7	2	3	4	1
8	2	4	3	2
9	3	1	3	4
10	3	2	4	3
11	3	3	1	2
12	3	4	2	1
13	4	1	4	2
14	4	2	3	1
15	4	3	2	4
16	4	4	1	3

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表 4 各组突出高度得分和最佳高度区间比率

Table 4. Salient height scores and optimal height interval ratio for each group

组名	突出高度得分 S	最佳高度区间比率 γ
1	1.686	37.912[(69/182)×100]
2	1.343	23.810[(50/210)×100]
3	1.478	31.395[(54/172)×100]
4	1.639	32.323[(96/297)×100]
5	1.353	26.786[(60/224)×100]
6	1.557	31.527[(64/203)×100]
7	1.607	31.122[(61/196)×100]
8	1.584	33.992[(86/253)×100]
9	1.892	41.423[(99/239)×100]
10	2.207	50.181[(139/277)×100]
11	2.194	51.339[(115/224)×100]
12	2.619	60.428[(113/187)×100]
13	2.481	57.848[(129/223)×100]
14	2.494	53.846[(70/130)×100]
15	2.199	52.907[(91/172)×100]
16	2.193	48.421[(92/190)×100]

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表 5 各因素4水平下突出高度得分的平均响应和效应极差

Table 5. Average response and effect range of salient height scores under four levels of each factor

水平	A	B	C	D
k₁	1.536 5	1.853 0	1.907 5	2.101 5
k₂	1.525 3	1.900 3	1.878 5	1.900 5
k₃	2.228 0	1.869 5	1.862 0	1.807 8
k₄	2.341 8	2.008 8	1.983 5	1.821 8
R	0.816 5	0.155 8	0.121 5	0.293 7
排序	1	3	4	2

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表 6 各因素4水平下最佳高度区间比率的平均响应和效应极差

Table 6. Average response and effect range of optimal height interval ratio under four levels of each factor

水平	A	B	C	D
k₁	31.360 0	40.992 3	42.299 8	45.827 0
k₂	30.856 8	39.841 0	40.982 8	41.747 3
k₃	50.842 8	41.690 8	40.164 0	39.195 8
k₄	53.255 5	43.791 0	42.868 5	39.545 0
R	22.398 7	3.950 0	2.704 5	6.631 2
排序	1	3	4	2

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