【作者】 王玉景；

【导师】 刘献礼；

【作者基本信息】 哈尔滨理工大学 ， 机械制造及其自动化， 2007， 硕士

【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。更多还原

【Abstract】 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.更多还原