發(fā)布時間：2018-07-12 17:57

  本文選題：金屬表面 + 瑕疵檢測�。� 參考：《江南大學》2013年碩士論文

【摘要】：金屬工件廣泛應用，是各種器械不可缺少的部件，隨著生產(chǎn)力的發(fā)展，用戶對其質(zhì)量也有更高的要求，而表面質(zhì)量是最直觀的體現(xiàn)，要求也往往更加嚴格。金屬工件表面瑕疵使工件外表不美觀的同時，更惡劣的是會影響工件的使用性能，使產(chǎn)品的安全性降低，必須在出廠前剔除。目前國內(nèi)大部分廠家還是采用人工目測法檢測，抽檢率低，檢測速度慢，檢測結(jié)果易受檢測人員主觀因素影響，缺乏一致的、科學的指導，各金屬制品企業(yè)亟需先進的表面瑕疵檢測技術和設備。國外設備和技術，不僅價格昂貴，維護費用高，而且還沒有自主知識產(chǎn)權(quán)，這些都迫使我們要開發(fā)出符合企業(yè)需要的自動化瑕疵檢測設備和技術。 本文以實驗室現(xiàn)有各種金屬工件為檢測研究對象，結(jié)合機器視覺、圖像處理和模式識別等技術完成對本課題的研究。 （1）分析了表面瑕疵檢測流程中預處理、圖像分割、特征提取階段現(xiàn)有的一些算法，對其原理和算法實現(xiàn)進行研究，并實驗論證，為下一步的工作提供充分的理論基礎和技術支持。 （2）以軸承為研究對象，提出一種基于機器視覺技術的軸承防塵蓋表面瑕疵檢測方法，從硬件環(huán)境的搭建，到軟件算法的實現(xiàn)進行了詳細說明。采用藍色同軸光源作為檢測系統(tǒng)所用光源，克服金屬反光；采用最小二乘法擬合軸承外圓，根據(jù)軸承型號比例分割出防塵蓋區(qū)域，然后利用Otsu閾值分割和Roberts邊緣提取處理圖像，再與模板軸承比較，求出相差角度，由此將防塵蓋字符、非字符區(qū)域分離，兩部分是否存在瑕疵分開判別，互不干擾。 （3）以鐵氧化物--磁瓦為研究對象，分析其表面瑕疵的類型和特點，運用紋理分析的方法實現(xiàn)特征的提取。通過對Gabor濾波器參數(shù)表達式的研究，構(gòu)造了不同尺度、不同方向的Gabor濾波器組，并針對磁瓦表面瑕疵特點對Gabor濾波器組進行了改進，為了去除數(shù)據(jù)相關性和冗余性，運用主成分分析法和獨立成分分析對提取到的特征進行了降維。 （4）對BP神經(jīng)網(wǎng)絡和支持向量機的基本原理和算法實現(xiàn)方法進行了研究，針對BP神經(jīng)網(wǎng)絡存在的不足，利用附加動量和變學習率學習的方法進行改進；針對支持向量機核參數(shù)c和懲罰因子g選取困難，采用網(wǎng)格法和K-CV法對其實現(xiàn)尋優(yōu)。最后用磁瓦表面瑕疵數(shù)據(jù)對兩種分類器的分類效果進行實驗比較和結(jié)果分析。 通過實驗證明，本文提出的軸承防塵蓋表面瑕疵檢測方法，，檢測系統(tǒng)采集到的軸承圖像清晰，瑕疵檢測算法正確率在96%以上，可實時的完成軸承防塵蓋表面瑕疵自動檢測。通過改進的Gabor濾波器組，實現(xiàn)了磁瓦表面瑕疵的特征提取，采用PCA，ICA分析法實現(xiàn)了特征降維，采用本文算法對磁瓦表面瑕疵進行分類，總體正確率可以達到93%以上，為表面瑕疵檢測分類提供了一種新方法。
[Abstract]:Metal workpieces are widely used and are indispensable parts of various instruments. With the development of productivity, users have higher requirements for their quality, and the surface quality is the most intuitive embodiment, and the requirements are often more stringent. The surface defects of metal workpiece make the appearance of workpiece unattractive, and at the same time, it will affect the performance of workpiece and reduce the safety of product, so it must be eliminated before leaving the factory. At present, most domestic manufacturers still use manual visual testing. The sampling rate is low, the detection speed is slow, the test results are easily influenced by the subjective factors of the examiners, and they lack consistent and scientific guidance. All metal products enterprises need advanced surface flaw detection technology and equipment. Foreign equipment and technology, not only expensive, high maintenance costs, but also do not have independent intellectual property rights, which force us to develop automatic defect detection equipment and technology that meet the needs of enterprises. In this paper, we take all kinds of metal parts in the laboratory as the detection object, and finish the research on this subject with machine vision, image processing and pattern recognition technology. (1) analyze the pretreatment and image segmentation in the process of surface defect detection. At the stage of feature extraction, some existing algorithms are studied, and the experimental results are presented to provide sufficient theoretical basis and technical support for the next work. (2) taking bearing as the research object, This paper presents a method for detecting the surface defects of bearing dust-proof cover based on machine vision technology, from the construction of hardware environment to the realization of software algorithm. The blue coaxial light source is used as the light source of the detection system to overcome the metal reflection, the least square method is used to fit the outer circle of the bearing, and the dust-proof cover area is segmented according to the bearing type ratio, and then the image is extracted and processed by Otsu threshold and Roberts edge. Then compared with the formwork bearing, the angle of difference is calculated, and the dust-proof cover character, the non-character area and the defect of the two parts are separated. (3) the iron-oxide magnetic tile is taken as the research object. The types and characteristics of surface defects are analyzed, and the feature extraction is realized by texture analysis. By studying the expression of Gabor filter parameters, Gabor filter banks with different scales and directions are constructed, and Gabor filter banks are improved to remove data correlation and redundancy. Principal component Analysis (PCA) and Independent component Analysis (ICA) are used to reduce the dimension of the extracted features. (4) the basic principles and algorithms of BP neural network and support vector machine are studied, and the shortcomings of BP neural network are pointed out. The method of learning with additional momentum and variable learning rate is improved, and the kernel parameter c and penalty factor g of support vector machine are difficult to select, and the mesh method and K-CV method are used to optimize them. Finally, the classification effects of the two classifiers are compared and analyzed by using the surface defect data of the magnetic tile. It is proved by experiments that the method proposed in this paper is clear in the image of bearing collected by the detection system, and the correct rate of defect detection algorithm is over 96%, which can be used to detect the surface defects of the dust proof cover in real time. Through the improved Gabor filter bank, the feature extraction of the surface defects of the magnetic tile is realized, and the feature dimension reduction is realized by using the PCACICA analysis method. The algorithm of this paper is used to classify the surface defects of the magnetic tile, and the overall correct rate can reach more than 93%. It provides a new method for the classification of surface defect detection.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP274

【參考文獻】

相關期刊論文 前10條

1 潘洪平,董申,梁迎春,陳汝欣;鋼球表面質(zhì)量評價系統(tǒng)[J];軸承;2000年07期

2 樂靜;郭俊杰;朱虹;;金屬球面缺陷的圖像檢測方法[J];電子學報;2007年06期

3 張志偉;楊帆;夏克文;楊瑞霞;;一種有監(jiān)督的LPP算法及其在人臉識別中的應用[J];電子與信息學報;2008年03期

4 梁亮;楊敏華;李英芳;;基于ICA與SVM算法的高光譜遙感影像分類[J];光譜學與光譜分析;2010年10期

5 趙桂林;朱啟兵;黃敏;;基于高光譜圖像技術的蘋果粉質(zhì)化LLE-SVM分類[J];光譜學與光譜分析;2010年10期

6 米曾真;謝志江;陳濤;楚紅雨;范兵;;重軌圖像增強與邊緣提取的關鍵技術[J];光學精密工程;2012年07期

7 賀秋偉;王龍山;于忠黨;李國發(fā);高立國;;基于圖像處理和支持向量機的微型齒輪缺陷檢測[J];吉林大學學報(工學版);2008年03期

8 張飛;塔西甫拉提·特依拜;丁建麗;買買提·沙吾提;田源;;ICA結(jié)合紋理特征的SVM鹽漬化信息提取研究[J];計算機工程與應用;2010年09期

9 余冰,金連甫,陳平;利用標準化LDA進行人臉識別[J];計算機輔助設計與圖形學學報;2003年03期

10 王宏漫,歐宗瑛;采用PCA/ICA特征和SVM分類的人臉識別[J];計算機輔助設計與圖形學學報;2003年04期

相關博士學位論文 前1條

1 王義文;鋼球表面缺陷檢測關鍵技術研究及樣機研制[D];哈爾濱理工大學;2010年

本文編號：2118012