圆度误差的神经网络评定及测量不确定度研究

张珂; 张玮; 阎卫增; 侯怀书

doi:10.13433/j.cnki.1003-8728.20180324

圆度误差的神经网络评定及测量不确定度研究

doi: 10.13433/j.cnki.1003-8728.20180324

1.
上海应用技术大学机械工程学院, 上海 201418
2.
上海思博特轴承技术研发有限公司, 上海 201400

基金项目:

上海市联盟计划项目 LM201635

上海应用技术大学协同创新基金项目 XTCX2018-13

详细信息

作者简介:
张珂(1968-), 教授, 博士, 研究方向为机械设计、机械精密测量, zkwy2004@126.com

中图分类号: TH161
计量
- 文章访问数: 157
- HTML全文浏览量: 78
- PDF下载量: 29
- 被引次数: 0
出版历程
- 收稿日期: 2018-05-02
- 刊出日期: 2019-03-05

Research on Evaluation and Uncertainty of Measurement of Circularity Errors via Neural Network Algorithm

1.
School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai 201418, China
2.
Shanghai Sibote Bearing Technology Development Co., Ltd., Shanghai 201400, China

摘要

摘要: 为了更为准确的而又简便的评定圆度误差及其不确定度，根据最小二乘法建立圆度误差模型，基于BP神经网络算法优化目标函数的参数，阐述了BP神经网络优化算法的原理和实现方法。通过求解实例表明该方法对于圆度误差评定的非线性优化问题能得到最优解。采用传统的测量不确定度表示指南方法和蒙特卡洛方法计算得到圆度误差的不确定度，通过实例验证蒙特卡洛法的可靠性和准确性。该方法不需要求出数学模型中的传递系数，利用MATLAB操作简单，为圆度误差测量结果不确定度评定提供了更加简便的方法。
- BP神经网络 /
- 圆度误差 /
- 不确定度 /
- 蒙特卡洛法 /
Abstract: In order to evaluate the circularity errors and its uncertainty more accurately and easily, the circularity error model is established with the least square method, and the parameters of objective function are optimized via BP neural network algorithm. The principle and implementation method of BP neural network algorithm are described. An example is given to show that the method can obtain the optimal solution for the nonlinear optimization problem of circularity errors evaluation. The uncertainty of roundness error is calculated with the Guide to expression of uncertainty in measurement (GUM) and Monte Carlo method. The reliability and accuracy of Monte Carlo method are verified. This method does not need to calculate the transfer coefficient of the model. It is easy to realize via MATLAB and provides a more convenient method for evaluating uncertainty of circularity error measurement results.
- BP neural network /
- circularity errors /
- uncertainty /
- Monte Carlo method /
- MATLAB

HTML全文

图 1 神经网络的结构图

下载: 全尺寸图片幻灯片

图 2 迭代次数与圆度误差均方差值之间的曲线关系

下载: 全尺寸图片幻灯片

图 3 圆度误差的概率分布直方图

下载: 全尺寸图片幻灯片

表 1 圆柱截面测点坐标值

mm
序号	x	y
1	8.357 2	-11.300 8
2	6.279 5	-12.588 6
3	4.005 6	-13.495 8
4	1.621 9	-13.993 3
5	-0.815 7	-14.070 6
6	-3.228 0	-13.725 0
7	-5.539 2	-12.959 8
8	-7.695 2	-11.807 3
9	-9.607 6	-10.303 5
10	-11.253 2	-8.466 8
11	-12.556 8	-6.342 5
12	-13.455 8	-4.069 1
13	-13.948 4	-1.729 3
14	-14.021 8	0.722 4
15	-13.679 1	3.121 1
16	-12.906 4	5.456 1
17	-11.748 9	7.608 1
18	-10.225 1	9.544 7
19	5.480 4	12.804 4
20	7.633 2	11.654 3
21	9.554 3	10.145 4
22	11.189 0	8.311 7
23	12.470 5	6.246 0
24	13.381 5	3.955 6
25	13.873 3	1.588 4
26	13.950 9	-0.866 3
27	13.601 4	-3.279 3
28	12.857 2	-5.552 6
29	11.704 2	-7.719 1
30	10.176 1	-9.674 5
31	-8.403 1	11.160 5
32	-6.322 8	12.443 3
33	-4.070 1	13.340 1
34	-1.681 6	13.835 2
35	0.759 9	13.911 7
36	3.167 2	13.565 3

下载: 导出CSV

表 2 各种圆度误差评定算法计算结果对比

mm
参数	最小二乘法	改进遗传算法^[14]	BP神经网络算法
A	-	-	-0.066 08
B	-	-	-0.154 96
C	-	-	196.338 8
误差	0.009 871	0.009 1	0.008 879

下载: 导出CSV

表 3 不同方法评定测量不确定度结果对比

mm
方法	BP神经网络	最小二乘法
GUM法	0.003 091	0.002 906
蒙特卡洛法	0.002 903	0.002 913

下载: 导出CSV

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