本文選題：機(jī)器視覺 + 飛機(jī)鉚釘��； 參考：《陜西科技大學(xué)》2017年碩士論文

【摘要】：隨著我國(guó)飛機(jī)制造業(yè)的快速發(fā)展,飛機(jī)鉚釘作為飛機(jī)裝配的必要零件被大量需求。為了保證飛機(jī)鉚釘?shù)馁|(zhì)量,并區(qū)分產(chǎn)品的規(guī)格等級(jí),需要運(yùn)用一定的技術(shù)手段對(duì)鉚釘產(chǎn)品進(jìn)行尺寸測(cè)量和表面缺陷檢測(cè)。然而目前大多飛機(jī)鉚釘?shù)纳a(chǎn)廠家仍然使用傳統(tǒng)的人工檢測(cè)方法對(duì)產(chǎn)品進(jìn)行檢測(cè),該檢測(cè)方法存在著檢測(cè)效率低、檢測(cè)成本高的問題,已經(jīng)不能滿足現(xiàn)代工業(yè)快速、精密和穩(wěn)定的測(cè)量及檢測(cè)要求�；跈C(jī)器視覺的工業(yè)檢測(cè)技術(shù)因其具有非接觸、在線檢測(cè)、實(shí)時(shí)分析和判斷的特點(diǎn)以及速度快、精度高、效率高的優(yōu)點(diǎn),目前已廣泛應(yīng)用于現(xiàn)代工業(yè)大生產(chǎn)的檢測(cè)領(lǐng)域。本文將飛機(jī)鉚釘作為檢測(cè)對(duì)象,研制了一種基于機(jī)器視覺的飛機(jī)鉚釘尺寸測(cè)量和表面缺陷檢測(cè)系統(tǒng)。通過分析飛機(jī)鉚釘?shù)男螤罱Y(jié)構(gòu)及表面缺陷的特征信息,對(duì)檢測(cè)系統(tǒng)的硬件部分進(jìn)行選型和設(shè)計(jì),對(duì)飛機(jī)鉚釘尺寸測(cè)量算法和表面缺陷檢測(cè)算法進(jìn)行研究,并對(duì)檢測(cè)系統(tǒng)的軟件部分進(jìn)行了設(shè)計(jì)。本文的研究工作主要包括以下幾個(gè)部分:(1)根據(jù)鉚釘生產(chǎn)企業(yè)提出的對(duì)飛機(jī)鉚釘檢測(cè)的性能指標(biāo),結(jié)合工藝條件,設(shè)計(jì)了一個(gè)包括運(yùn)動(dòng)控制部分、圖像采集和處理部分以及光源照明系統(tǒng)的機(jī)器視覺檢測(cè)的硬件系統(tǒng)。選擇合適的工業(yè)相機(jī)和光學(xué)鏡頭,對(duì)光源照明系統(tǒng)進(jìn)行選型和設(shè)計(jì),并對(duì)光源照明系統(tǒng)進(jìn)行優(yōu)化,以保證待測(cè)鉚釘圖像的質(zhì)量。(2)對(duì)飛機(jī)鉚釘尺寸測(cè)量算法進(jìn)行了研究。根據(jù)飛機(jī)鉚釘?shù)臏y(cè)量需要及鉚釘特征,確定的算法流程為:首先對(duì)原始鉚釘圖像進(jìn)行預(yù)處理并對(duì)存在畸變的鉚釘圖像進(jìn)行畸變補(bǔ)償,確定鉚釘圖像的邊緣圖像,最后對(duì)鉚釘圖像的圓形部分采用最小二乘法擬合圓的方法進(jìn)行測(cè)量,同時(shí)對(duì)鉚釘圖像的線型特征采用Hough變換的方法確定鉚釘?shù)某叽鐓?shù)。(3)對(duì)飛機(jī)鉚釘表面缺陷檢測(cè)算法進(jìn)行了研究。表面缺陷檢測(cè)算法主要由缺陷補(bǔ)償算法和缺陷判斷算法組成。缺陷補(bǔ)償算法采用稀疏分解和聚類分析的方法獲得飛機(jī)鉚釘?shù)哪０鍒D像,與待測(cè)飛機(jī)鉚釘進(jìn)行差分運(yùn)算后提取鉚釘缺陷圖像,缺陷判斷算法為根據(jù)鉚釘缺陷圖像的幾何特征和形狀特征判斷缺陷的類型。(4)在Visual Studio 2010環(huán)境下,結(jié)合MFC和Open CV庫實(shí)現(xiàn)了軟件系統(tǒng)的設(shè)計(jì)。運(yùn)用MFC設(shè)計(jì)友好的人機(jī)交互界面,采用模塊化編程的思想,利用Open CV軟件實(shí)現(xiàn)了飛機(jī)鉚釘尺寸測(cè)量算法和表面缺陷檢測(cè)算法,并通過數(shù)據(jù)庫對(duì)飛機(jī)鉚釘尺寸測(cè)量和缺陷檢測(cè)的信息進(jìn)行保存。最后,在完成系統(tǒng)構(gòu)建、算法設(shè)計(jì)和軟件實(shí)現(xiàn)的基礎(chǔ)上,通過實(shí)驗(yàn)對(duì)本文所設(shè)計(jì)的檢測(cè)系統(tǒng)的效果進(jìn)行了驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,本文所設(shè)計(jì)的基于機(jī)器視覺的飛機(jī)鉚釘尺寸測(cè)量和表面缺陷檢測(cè)系統(tǒng)能夠?qū)崿F(xiàn)飛機(jī)鉚釘?shù)母呔葴y(cè)量以及準(zhǔn)確的缺陷判斷,滿足各方面的檢測(cè)要求。
[Abstract]:With the rapid development of China's aircraft manufacturing industry, aircraft rivets are required as necessary parts for aircraft assembly. In order to ensure the quality of aircraft rivets and distinguish the grade of products, it is necessary to use certain technical means to measure the size and detect the surface defects of the rivets. However, at present, most manufacturers of aircraft rivets still use the traditional manual testing method to detect the products. The detection method has the problems of low efficiency and high cost, and can not meet the rapid development of modern industry. Precise and stable measurement and testing requirements. The industrial detection technology based on machine vision has been widely used in the field of modern industrial production because of its characteristics of non-contact, on-line detection, real-time analysis and judgment, as well as the advantages of high speed, high precision and high efficiency. In this paper, an aircraft rivet size measurement and surface defect detection system based on machine vision is developed. By analyzing the shape and structure of aircraft rivets and the characteristic information of surface defects, the hardware part of the detection system is selected and designed, and the algorithm of measuring the size of aircraft rivets and the algorithm of detecting surface defects are studied. And the software part of the detection system is designed. The research work of this paper mainly includes the following parts: 1) according to the performance index of the rivet inspection proposed by the rivet manufacturing enterprise and combining with the technological conditions, a motion control part is designed. The part of image acquisition and processing and the hardware system of machine vision detection of light source lighting system. Selecting the appropriate industrial camera and optical lens, selecting and designing the lighting system of light source, and optimizing the lighting system of light source to ensure the quality of rivet image to be tested, the algorithm of measuring the size of aircraft rivet is studied. According to the measurement requirements and rivet characteristics of aircraft rivets, the algorithm flow is as follows: first, preprocess the original rivets image and compensate the distortion of the rivet image with distortion, and determine the edge image of the rivet image. Finally, the circular part of the rivet image is measured by using the least square method to fit the circle. At the same time, the method of Hough transform is used to determine the size parameter of rivets, and the algorithm for detecting the surface defects of aircraft rivets is studied. The surface defect detection algorithm is mainly composed of defect compensation algorithm and defect judgment algorithm. The defect compensation algorithm uses sparse decomposition and clustering analysis to obtain the template image of the aircraft rivets, and then extracts the rivet defect image after differential operation with the aircraft rivet to be tested. The defect judgment algorithm is based on the geometric and shape features of rivets defect image to judge the type of defect. In Visual Studio 2010 environment, the software system is designed by combining MFC and Open CV library. Using MFC to design friendly man-machine interface, using the idea of modular programming, using Open CV software to realize the aircraft rivet size measurement algorithm and surface defect detection algorithm. The information of size measurement and defect detection of aircraft rivets is saved by database. Finally, on the basis of the system construction, algorithm design and software implementation, the effect of the detection system designed in this paper is verified by experiments. The experimental results show that the aircraft rivet size measurement and surface defect detection system based on machine vision can realize the high precision measurement and accurate defect judgment of aircraft rivets, and meet the requirements of all aspects of the detection.
【學(xué)位授予單位】：陜西科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V262.4;TP391.41

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