本文選題：工件表面質(zhì)量 + 數(shù)字圖像　； 參考：《蘭州理工大學(xué)》2017年碩士論文

【摘要】：表面粗糙度是指加工零部件表面上存在的,由較小的間距和峰谷所構(gòu)成的微觀幾何形狀特征。隨著產(chǎn)品精度和零部件表面粗糙度要求的不斷提高,接觸式檢測工件表面粗糙度的儀器儀表的測量精度和測量速度等方面要求的不斷提高,推動了現(xiàn)在的非接觸式無損檢測技術(shù)的發(fā)展。本文將數(shù)字圖像的相關(guān)理論與技術(shù)應(yīng)用于工件表面粗糙度的檢測研究,主要研究內(nèi)容和結(jié)果包括:(1)國內(nèi)外關(guān)于檢測工件表面粗糙度的發(fā)展?fàn)顩r進(jìn)行了表述,并且對數(shù)字圖像處理技術(shù)在零部件表面粗糙度檢測中的應(yīng)用以及意義進(jìn)行探討,而且介紹了數(shù)字圖像處理的基本方法以及原理并且運(yùn)用其對工件表面的圖像進(jìn)行分析處理,用不同的數(shù)字圖像的處理方法,針對相同的圖片,從而獲得不能的圖像特性;(2)將灰度變換、灰度拉伸等方法應(yīng)用到零部件加工表面的圖像中分析,經(jīng)過圖像處理后,根據(jù)其灰度直方圖及圖像顯示效果選擇適合表面粗糙度檢測的圖像處理方法,研究了零部件的表面粗糙度Ra、Rz值的測量原理及其實(shí)現(xiàn)的數(shù)字化技術(shù);(3)介紹了圖像噪聲的分類,并且根據(jù)零部件表面圖像中噪聲的不同,運(yùn)用不同的濾波方法來處理圖像中不同類型的噪聲,并且對其進(jìn)行了降噪處理,最終獲得不同的降噪效果;(4)以工件加工表面的圖片為對象,采用傳統(tǒng)的表面粗糙度測量方法,對特征紋理的提取方法進(jìn)行研究;采用最小二乘法,通過對工件加工表面圖中的特征紋理的分布情況進(jìn)行分析、對加工工件的紋理特征的統(tǒng)計(jì),計(jì)算得出零部件表面粗糙度值,并給出零部件表面粗糙度值的最終測量方法;(5)基于Open CV平臺,借助C++語言編出測量加工工件表面粗糙度的程序,實(shí)現(xiàn)了對工件加工表面圖片的處理,從圖像的讀入到表面的紋理特征提取,再到表面粗糙度值的自動測量,最后將測得數(shù)據(jù)與表面粗糙度試驗(yàn)得到的實(shí)際值相比較,將兩組數(shù)據(jù)進(jìn)行參數(shù)回歸分析,得到最終的運(yùn)算結(jié)果,也就實(shí)現(xiàn)了本系統(tǒng)的標(biāo)定。本文將工件表面粗糙度檢測與數(shù)字圖像處理技術(shù)有機(jī)結(jié)合起來,對加工工件的表面圖片進(jìn)行預(yù)處理,在此基礎(chǔ)上,提取了圖像中的紋理特征并編程計(jì)算,最后得到的工件表面的粗糙度值,從而證明了此方法的可行性,研究結(jié)果對工件表面粗糙度的檢測具有非常重要的實(shí)踐意義。
[Abstract]:Surface roughness refers to the micro-geometric characteristics of machining parts which are composed of small spacing and peak and valley. With the continuous improvement of product precision and surface roughness requirements of parts and components, the measurement accuracy and measuring speed of the instruments and instruments used to detect the surface roughness of workpieces have been continuously improved. It promotes the development of non-contact non-destructive testing technology. In this paper, the theory and technology of digital image are applied to the measurement of workpiece surface roughness. The main research contents and results include the development of measuring workpiece surface roughness both at home and abroad. The paper also discusses the application and significance of digital image processing technology in measuring the surface roughness of parts, and introduces the basic method and principle of digital image processing, and uses it to analyze and process the image of workpiece surface. Using different digital image processing methods, aiming at the same picture, we can obtain the image characteristics that can not be obtained. (2) the methods of gray level transformation and gray scale stretching are applied to the analysis of the image of the machined surface of the parts and components. After image processing, the image processing is carried out. According to the grayscale histogram and image display effect, the image processing method suitable for surface roughness detection is selected. The measuring principle of surface roughness Rz value of parts and components and its digitization technology are studied. The classification of image noise is introduced. According to the different noise in the surface image of parts, different filtering methods are used to deal with the different types of noise in the image, and the noise reduction is carried out. Finally, different noise reduction effects are obtained. (1) taking the images of workpiece machined surface as the object, the traditional surface roughness measurement method is used to study the feature texture extraction method, and the least square method is used. By analyzing the distribution of the feature texture in the workpiece machining surface map and counting the texture features of the machined workpiece, the surface roughness value of the parts is calculated. The final measurement method of surface roughness value of parts is given. Based on Open CV platform, the program of measuring workpiece surface roughness is compiled with C language, and the processing of workpiece surface picture is realized. From the image reading to the texture feature extraction of the surface, and then to the automatic measurement of the surface roughness value, the measured data are compared with the actual values obtained from the surface roughness test, and the two groups of data are regressed by parameter regression. Finally, the calibration of the system is realized. In this paper, the surface roughness detection of workpiece is combined with digital image processing technology to preprocess the surface image of the machined workpiece. On this basis, the texture feature of the image is extracted and calculated by programming. Finally, the roughness value of the workpiece surface is obtained, which proves the feasibility of this method. The research results are of great practical significance for the measurement of workpiece surface roughness.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH161;TP391.41

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