基于图像的车削表面粗糙度测量
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摘要
表面粗糙度是指加工表面上具有的较小间距和峰谷所组成的微观几何形状特性(误差),它直接影响到机器和仪表的使用性能和寿命,特别是对运转速度快、装配精度高、密封性要求严的产品,更具有重要的意义。因此,表面粗糙度测量技术在工程技术研究中占有非常重要的地位。随着现代工业生产和科学技术的发展,人们对表面粗糙度的测量提出了越来越高的要求,对无损便携式表面粗糙度测量仪器的需求也应运而生。
本文综合比较了现有表面粗糙度测量的方法,在分析各个方法优劣的基础之上,通过对图像检测技术和神经网络技术的研究,提出了一种新的表面粗糙度测量方法。首先通过图像获取车削表面的纹理信息进行特征提取,然后建立神经网络对特征值进行训练识别,验证图像特征和表面粗糙度之间存在对应关系,也可以说是完成了表面粗糙度的定性测量。接下来进一步从图像中提取截面轮廓进行滤波处理和参数提取,将得到的粗糙度参数和触针式测量仪测得的结果作对比,通过回归分析找到二者之间的规律,建立数学公式,完成表面粗糙度的定量测量。
本文采用LED光源、摄像机、体式显微镜以及计算机等硬件设备,以MATLAB软件为平台,搭建了表面粗糙度测量系统,并开发了相应的测量软件。本系统充分利用摄像机的USB口高传输速率和MATLAB强大的算法库,实现了表面粗糙度的图像测量。
Surface roughness means microcosmic geometry shape characteristic (error) composed of minor spaces, peaks and valleys in the machining surface. It directly influences machine and instrument's service performance and life, especially makes important sense to products with high running speed, fabrication precision and tightness request. Therefore, surface roughness measuring technique takes a very important place in engineering and technological research. Along with the development of modern industrial production and technology, people put forward higher and higher request on surface roughness measurement, thus the need of nondestructive and portable surface roughness measuring equipment emerges as the times require.
This thesis compared the existing surface roughness measuring methods synthetically. Based on analyzing every method's quality, and through research of image detection and neural net technique, a new method of measuring surface roughness was put forward. Firstly, acquire texture information from turning surface's image and extract feature. Secondly, establish neural net to train and recognition eigenvalues, validate that there is a corresponding relationship between image feature and surface roughness, and thus finish qualitative measurement of surface roughness. Thirdly, extract section contour from images to proceed filtering and extract parameters. Compare the above parameters with the result obtained by stylus measuring equipment, find the rule through regression analysis and create mathematical formula, which finished quantitative measurement of surface roughness.
This thesis adopted LED light source, camera, body microscope and computer as hardware devices, MATLAB as software platform, to build surface roughness measuring system, as well as develop the corresponding measuring software. In virtue of camera's USB high transmission speed and MATLAB's powerful algorithms library, the system realized surface roughness's measurement based on image.
本文综合比较了现有表面粗糙度测量的方法,在分析各个方法优劣的基础之上,通过对图像检测技术和神经网络技术的研究,提出了一种新的表面粗糙度测量方法。首先通过图像获取车削表面的纹理信息进行特征提取,然后建立神经网络对特征值进行训练识别,验证图像特征和表面粗糙度之间存在对应关系,也可以说是完成了表面粗糙度的定性测量。接下来进一步从图像中提取截面轮廓进行滤波处理和参数提取,将得到的粗糙度参数和触针式测量仪测得的结果作对比,通过回归分析找到二者之间的规律,建立数学公式,完成表面粗糙度的定量测量。
本文采用LED光源、摄像机、体式显微镜以及计算机等硬件设备,以MATLAB软件为平台,搭建了表面粗糙度测量系统,并开发了相应的测量软件。本系统充分利用摄像机的USB口高传输速率和MATLAB强大的算法库,实现了表面粗糙度的图像测量。
Surface roughness means microcosmic geometry shape characteristic (error) composed of minor spaces, peaks and valleys in the machining surface. It directly influences machine and instrument's service performance and life, especially makes important sense to products with high running speed, fabrication precision and tightness request. Therefore, surface roughness measuring technique takes a very important place in engineering and technological research. Along with the development of modern industrial production and technology, people put forward higher and higher request on surface roughness measurement, thus the need of nondestructive and portable surface roughness measuring equipment emerges as the times require.
This thesis compared the existing surface roughness measuring methods synthetically. Based on analyzing every method's quality, and through research of image detection and neural net technique, a new method of measuring surface roughness was put forward. Firstly, acquire texture information from turning surface's image and extract feature. Secondly, establish neural net to train and recognition eigenvalues, validate that there is a corresponding relationship between image feature and surface roughness, and thus finish qualitative measurement of surface roughness. Thirdly, extract section contour from images to proceed filtering and extract parameters. Compare the above parameters with the result obtained by stylus measuring equipment, find the rule through regression analysis and create mathematical formula, which finished quantitative measurement of surface roughness.
This thesis adopted LED light source, camera, body microscope and computer as hardware devices, MATLAB as software platform, to build surface roughness measuring system, as well as develop the corresponding measuring software. In virtue of camera's USB high transmission speed and MATLAB's powerful algorithms library, the system realized surface roughness's measurement based on image.
引文
1 袁长良,丁志华,武文堂. 表面粗糙度及其测量. 机械工业出版社,1989:14,43~53
2 刘斌,冯其波,匡萃方. 表面粗糙度测量方法综述. 光学仪器,2004,26(5):54~58
3 强锡富. 电动量仪(上). 机械工业出版社,1983:14,53
4 张剑. 触针接触式表面粗糙度测量仪. 机械工人(冷加工).2004,6:57~58
5 杨晔,童竞,唐加驹,李大成. Talysurf-4型表面粗糙度轮廓仪的智能化.计量技术,1996,(9):17~19
6 D.J. Whitehouse. Comparison between Stylus and Optical Methods for Measuring Surface. Annals of the CIRP, 1988, 37(2): 649~653
7 尤政. 非接触高准确度表面粗糙度测量仪. 计量技术,1995,3:13~15
8 陈晓梅. 表面粗糙度干涉图像处理系统. 航空计测技术,1997,17(3):7~10
9 T.V. Vorburger. Measurements of Roughness of Very Smooth Surfaces. Annals of the CIRP, 1987, 36(2): 503~509
10 D.K. Bowen, M. Wormington. Measurement of Surface Roughness and Topography at Nanometer Levels by Diffuse X-ray Scattering. Annals of the CIRP, 1994, 43(1): 497~500
11 张国维. 表面粗糙度测量的新方法. 机床.1990,2:27~30
12 M.B. Kiran, B. Ramamoorthy and V. Radhakrishnan. Evaluation of Surface Roughness by Vision System. International Journal of Machine Tools and Manufacture, 1998, 38(5-6): 685~690
13 G. Galante, M. Piacentini and V.F. Ruisi. Surface Roughness Detection by Tool Image Processing. Wear, 1991, 148: 211~220
14 D.E.P. HoyF. Yu. Surface Quality Assessment using Computer Vision Methods. Journal of Materials Processing Technology, 1991, 28: 265~274
15 Oana G. Cula. 3D Texture Recognition Using Bidirectional Feature Histograms. International Journal of Machine Vision, 2004, 59(1): 33~60
16 胡金华. 工件表面纹理分析方法研究. 浙江工贸职业技术学院学报,2005,5(4):49~52
17 M. A. Younis. On Line Surface Roughness Measurements Using Image Processing Towards an Adaptive Control. Computers and Industrial Engineering, 1998, 35(1-2): 49~52
18 B.Y. Lee, S.F. Yu and H. Juan. The Model of Surface Roughness Inspection by Vision System in Turning. Mechatronics, 2004, 14(1): 129~141
19 D. Chappard. Image Analysis Measurements of Roughness by Texture and Fractal Analysis Correlate with Contact Profilometry. Biomaterials, 2003, 24: 1399~1407
20 Shinn-Ying Ho, Kuang-Chyi Lee et al. Accurate Modeling and Prediction of Surface Roughness by Computer Vision in Turning Operations Using an Adaptive Neuro-fuzzy Inference System. International Journal of Machine Tools and Manufacture, 2002, 42(13): 1441~1446
21 Kuang-Chyi Lee, Shinn-Jang Ho and Shinn-Ying Ho. Accurate Estimation of Surface Roughness from Texture Features of the Surface Image using an Adaptive Neuro-fuzzy Inference System. Precision Engineering, 2005, 29(1): 95~100
22 Gadelmawla. A Vision System for Surface Roughness Characterization Using the Gray Level Co-occurrence Matrix. NDT and E International, 2004, 7(7): 577~588
23 Rajneesh Kumar. Application of Digital Image Magnification for Surface Roughness Evaluation Using Machine Vision. International Journal of Machine Tools and Manufacture, 2005, 45(2): 228~234
24 李伯奎. 不同机械加工表面轮廓峰密度的研究. 工具技术,2002,36(9):39~42
25 孟克,王东红. 表面粗糙度的光学在线检测. 哈尔滨工程大学学报,2003,24(5):560~562
26 陈爱弟,王信义. 零件表面粗糙度的在线检测方法研究. 中国机械工程,2002,13(6):494~496
27 马聪,黎明,王长坤. 机械加工表面粗糙度的视觉检测. 南昌航空工业学院学报(自然科学版),2003,17(3):88~94
28 王爱囡. 表面粗糙度在线识别的理论与实践. 合肥工业大学学报(自然科学版),2004,27(4):425~429
29 周莉莉,赵学增. 表面粗糙度的激光及相关在线测量方法. 激光杂志,2004,25(3):4~8
30 王仲春,高岳,黄粤熙,胡士凌,高稚允.显微成像检测表面粗糙度.光学技术,1998,(5):46~47
31 靳宏磊,张振华,李立源,陈维南.基于纹理分析的表面粗糙度等级识别.中国图象图形学报,2004,5(6):612~615
32 朱铮涛,黎绍发.基于机器视觉的零件表面粗糙度测量.现代制造工程,2002,(8):46~47
33 张明慧,张尧禹,黄廉卿. 基于视频图像的实时测量技术的实现. 微计算机信息,2004,20(3):57~58
34 阮秋琦.数字图像处理学.电子工业出版社,2001:3
35 罗军辉,冯平等编著. MATLAB7.0 在图像处理中的应用. 机械工业出版社,2005:10
36 http://www.mathworks.com/access/helpdesk/help/toolbox/imaq/
37 朱正德,周龙. 嵌入式图像处理系统在工件表面缺陷检测中的应用. 工艺与检测,2006,(3):59~62
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40 沈国良. 数字图像分析技术在涂漆前后表面评估中的应用. 涂装指南,2005,(4):8~9
41 杨明,白烨,王秋良,余运佳. 面阵CCD摄像机光学镜头参数及选用. 光电子技术与信息,2005,18(3):27~30
42 王玉景. 机械加工表面质量的图像检测技术.哈尔滨理工大学学士论文,2004:41~43
43 刘子男,刘鹏,高丰. 软硬件协同验证中的系统性能评估方法. 电路与系统学报,2003,8(5):149~152
44 沈财余. 从软硬件系统的集成发展论物探一体化. 石油地球物理斟探,2003,38(增刊):126~127
45 邓朝晖,程文涛. 基于神经网络的成形磨削表面粗糙度的研究. 机械制造,2006,44(503):39~40
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2 刘斌,冯其波,匡萃方. 表面粗糙度测量方法综述. 光学仪器,2004,26(5):54~58
3 强锡富. 电动量仪(上). 机械工业出版社,1983:14,53
4 张剑. 触针接触式表面粗糙度测量仪. 机械工人(冷加工).2004,6:57~58
5 杨晔,童竞,唐加驹,李大成. Talysurf-4型表面粗糙度轮廓仪的智能化.计量技术,1996,(9):17~19
6 D.J. Whitehouse. Comparison between Stylus and Optical Methods for Measuring Surface. Annals of the CIRP, 1988, 37(2): 649~653
7 尤政. 非接触高准确度表面粗糙度测量仪. 计量技术,1995,3:13~15
8 陈晓梅. 表面粗糙度干涉图像处理系统. 航空计测技术,1997,17(3):7~10
9 T.V. Vorburger. Measurements of Roughness of Very Smooth Surfaces. Annals of the CIRP, 1987, 36(2): 503~509
10 D.K. Bowen, M. Wormington. Measurement of Surface Roughness and Topography at Nanometer Levels by Diffuse X-ray Scattering. Annals of the CIRP, 1994, 43(1): 497~500
11 张国维. 表面粗糙度测量的新方法. 机床.1990,2:27~30
12 M.B. Kiran, B. Ramamoorthy and V. Radhakrishnan. Evaluation of Surface Roughness by Vision System. International Journal of Machine Tools and Manufacture, 1998, 38(5-6): 685~690
13 G. Galante, M. Piacentini and V.F. Ruisi. Surface Roughness Detection by Tool Image Processing. Wear, 1991, 148: 211~220
14 D.E.P. HoyF. Yu. Surface Quality Assessment using Computer Vision Methods. Journal of Materials Processing Technology, 1991, 28: 265~274
15 Oana G. Cula. 3D Texture Recognition Using Bidirectional Feature Histograms. International Journal of Machine Vision, 2004, 59(1): 33~60
16 胡金华. 工件表面纹理分析方法研究. 浙江工贸职业技术学院学报,2005,5(4):49~52
17 M. A. Younis. On Line Surface Roughness Measurements Using Image Processing Towards an Adaptive Control. Computers and Industrial Engineering, 1998, 35(1-2): 49~52
18 B.Y. Lee, S.F. Yu and H. Juan. The Model of Surface Roughness Inspection by Vision System in Turning. Mechatronics, 2004, 14(1): 129~141
19 D. Chappard. Image Analysis Measurements of Roughness by Texture and Fractal Analysis Correlate with Contact Profilometry. Biomaterials, 2003, 24: 1399~1407
20 Shinn-Ying Ho, Kuang-Chyi Lee et al. Accurate Modeling and Prediction of Surface Roughness by Computer Vision in Turning Operations Using an Adaptive Neuro-fuzzy Inference System. International Journal of Machine Tools and Manufacture, 2002, 42(13): 1441~1446
21 Kuang-Chyi Lee, Shinn-Jang Ho and Shinn-Ying Ho. Accurate Estimation of Surface Roughness from Texture Features of the Surface Image using an Adaptive Neuro-fuzzy Inference System. Precision Engineering, 2005, 29(1): 95~100
22 Gadelmawla. A Vision System for Surface Roughness Characterization Using the Gray Level Co-occurrence Matrix. NDT and E International, 2004, 7(7): 577~588
23 Rajneesh Kumar. Application of Digital Image Magnification for Surface Roughness Evaluation Using Machine Vision. International Journal of Machine Tools and Manufacture, 2005, 45(2): 228~234
24 李伯奎. 不同机械加工表面轮廓峰密度的研究. 工具技术,2002,36(9):39~42
25 孟克,王东红. 表面粗糙度的光学在线检测. 哈尔滨工程大学学报,2003,24(5):560~562
26 陈爱弟,王信义. 零件表面粗糙度的在线检测方法研究. 中国机械工程,2002,13(6):494~496
27 马聪,黎明,王长坤. 机械加工表面粗糙度的视觉检测. 南昌航空工业学院学报(自然科学版),2003,17(3):88~94
28 王爱囡. 表面粗糙度在线识别的理论与实践. 合肥工业大学学报(自然科学版),2004,27(4):425~429
29 周莉莉,赵学增. 表面粗糙度的激光及相关在线测量方法. 激光杂志,2004,25(3):4~8
30 王仲春,高岳,黄粤熙,胡士凌,高稚允.显微成像检测表面粗糙度.光学技术,1998,(5):46~47
31 靳宏磊,张振华,李立源,陈维南.基于纹理分析的表面粗糙度等级识别.中国图象图形学报,2004,5(6):612~615
32 朱铮涛,黎绍发.基于机器视觉的零件表面粗糙度测量.现代制造工程,2002,(8):46~47
33 张明慧,张尧禹,黄廉卿. 基于视频图像的实时测量技术的实现. 微计算机信息,2004,20(3):57~58
34 阮秋琦.数字图像处理学.电子工业出版社,2001:3
35 罗军辉,冯平等编著. MATLAB7.0 在图像处理中的应用. 机械工业出版社,2005:10
36 http://www.mathworks.com/access/helpdesk/help/toolbox/imaq/
37 朱正德,周龙. 嵌入式图像处理系统在工件表面缺陷检测中的应用. 工艺与检测,2006,(3):59~62
38 李伟,向键勇,陈曦,王新怀. 高速超分辨率图像处理系统. 电子科技,2006,(1):20~24
39 陈杰来,尤丽华,张秋菊. 数字图像处理技术在指针式仪表读数系统中的应用. 江南大学学报,2005,4(6):611~614
40 沈国良. 数字图像分析技术在涂漆前后表面评估中的应用. 涂装指南,2005,(4):8~9
41 杨明,白烨,王秋良,余运佳. 面阵CCD摄像机光学镜头参数及选用. 光电子技术与信息,2005,18(3):27~30
42 王玉景. 机械加工表面质量的图像检测技术.哈尔滨理工大学学士论文,2004:41~43
43 刘子男,刘鹏,高丰. 软硬件协同验证中的系统性能评估方法. 电路与系统学报,2003,8(5):149~152
44 沈财余. 从软硬件系统的集成发展论物探一体化. 石油地球物理斟探,2003,38(增刊):126~127
45 邓朝晖,程文涛. 基于神经网络的成形磨削表面粗糙度的研究. 机械制造,2006,44(503):39~40
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