基于圖像處理技術(shù)的AOI系統(tǒng)的研究
本文選題:印刷電路板 + 圖像處理 ; 參考:《浙江理工大學(xué)》2017年碩士論文
【摘要】:在電路板的生產(chǎn)過(guò)程中,由于元器件一般都是通過(guò)貼片機(jī)、插件機(jī)或者人工的方式放置到電路板上,故往往會(huì)出現(xiàn)一些錯(cuò)誤,例如漏件、錯(cuò)件、方向錯(cuò)誤等。而目前市面上電路板自動(dòng)光學(xué)檢測(cè)設(shè)備基本上都是針對(duì)焊點(diǎn)面的,對(duì)于焊點(diǎn)缺陷或者位置固定的貼片元器件缺陷的檢測(cè)效果較好,但對(duì)于元器件面上的直插元器件的檢測(cè)準(zhǔn)確度差、誤報(bào)率高。因此,對(duì)于元器件面的檢測(cè)大部分都是靠人工檢測(cè),其檢測(cè)過(guò)程枯燥重復(fù),非常容易發(fā)生漏檢和誤檢;谝陨犀F(xiàn)狀,本文研究的基于圖像處理技術(shù)的自動(dòng)光學(xué)檢測(cè)系統(tǒng),能夠?qū)崿F(xiàn)對(duì)電路板元器件面進(jìn)行缺陷檢測(cè)。本系統(tǒng)包括圖像采集模塊、圖像處理模塊、程序界面模塊和數(shù)據(jù)存儲(chǔ)模塊四個(gè)部分。同時(shí),通過(guò)對(duì)本系統(tǒng)實(shí)驗(yàn)結(jié)果的分析,證明了本文所研究的基于圖像處理技術(shù)的AOI系統(tǒng)具有可行性。本文的研究?jī)?nèi)容及主要成果如下:(1)圖像采集模塊的硬件部分主要由VT-EX1400CPS工業(yè)相機(jī)、VT-LEM0814CB-MP8鏡頭、箱式無(wú)影光源組成;軟件部分基于labview語(yǔ)言和VAS視覺(jué)獲取模塊編寫。1400萬(wàn)像素的工業(yè)相機(jī)保證了采集到的圖像分辨率高;無(wú)影光源保證了電路板上光線照射均勻,元器件陰影降到最低。本模塊實(shí)現(xiàn)了高質(zhì)量、低干擾的電路板圖像實(shí)時(shí)采集功能。(2)元器件存在性檢測(cè)提供了三種檢測(cè)方法,分別為顏色提取、計(jì)算相似度、模板匹配,多種檢測(cè)方法能夠滿足絕大多數(shù)元器件檢測(cè)的需求。(3)二極管極性檢測(cè)算法針對(duì)二極管管體位置的不確定性,使用了兩種二極管定位的方法,分別是閾值分割方法和查找管腳方法方法;然后通過(guò)對(duì)二極管管體進(jìn)行分析,從而得出二極管的極性。(4)電解電容極性檢測(cè)算法使用改進(jìn)的霍夫圓檢測(cè)方法定位電解電容;然后將電解電容帶有極性標(biāo)志的圓環(huán)截取下來(lái)并展開(kāi),通過(guò)OTSU方法進(jìn)行閾值分割,從而判斷極性。(5)插座方向檢測(cè)算法針對(duì)插針到兩邊距離的不同以及插針位置的固定性,使用了一種通過(guò)計(jì)算插針到插座邊緣距離的方法來(lái)判斷插座方向。
[Abstract]:In the production process of the circuit board, because the components are usually placed on the circuit board by placement machine, plug-in machine or manual way, there are often some mistakes, such as missing parts, wrong direction and so on. At present, circuit board automatic optical inspection equipment on the market is basically aimed at the solder joint surface, and the detection effect of solder joint defect or fixed position patch component defect is better. But the detection accuracy is poor and false alarm rate is high. Therefore, the detection of components surface mostly rely on manual detection, the detection process is boring and repetitive, and it is easy to miss and misdetect. Based on the above situation, the automatic optical detection system based on image processing technology is developed in this paper, which can detect the defects of circuit board components. The system includes four parts: image acquisition module, image processing module, program interface module and data storage module. At the same time, through the analysis of the experimental results of the system, it is proved that the AOI system based on the image processing technology studied in this paper is feasible. The research contents and main results of this paper are as follows: 1) the hardware of the image acquisition module is mainly composed of the VT-LEM0814CB-MP8 lens of VT-EX1400CPS industrial camera and the box type non-shadow light source. The software is based on the labview language and the VAS visual acquisition module. The 14 million pixel industrial camera ensures the high resolution of the collected image, the non-shadow light source ensures that the light on the circuit board is uniform, and the shadow of the components is reduced to the minimum. In this module, the high quality and low interference function of real-time image acquisition of circuit board is realized. It provides three detection methods, namely, color extraction, calculation of similarity, template matching, etc. A variety of detection methods can meet the needs of the majority of components. The diode polarity detection algorithm uses two diode location methods in view of the uncertainty of diode tube position. The method of threshold segmentation and the method of finding pin are used respectively, and then through the analysis of diode tube, the polarity detection algorithm of electrolytic capacitance is obtained, and the improved Hoff circle detection method is used to locate the electrolytic capacitance. Then the ring with polarity mark is cut off and expanded, and then the threshold value is segmented by OTSU method to judge the direction detection algorithm of polarity. 5) aiming at the different distance between the two sides of the pin and the fixing of the pin position, A method is used to determine the direction of the socket by calculating the distance from the pin to the edge of the socket.
【學(xué)位授予單位】:浙江理工大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2017
【分類號(hào)】:TN41;TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 郭民;王蕊;;AOI技術(shù)在PCB缺陷檢測(cè)中的設(shè)計(jì)與實(shí)現(xiàn)[J];測(cè)控技術(shù);2016年12期
2 鄒承明;薛棟;郭雙雙;趙廣輝;;一種改進(jìn)的圖像相似度算法[J];計(jì)算機(jī)科學(xué);2016年06期
3 季宇;;基于特征提取的圖像相似度研究[J];信息系統(tǒng)工程;2016年01期
4 楊宏強(qiáng);;全球PCB產(chǎn)業(yè)現(xiàn)狀分析及策略研究(2015)[J];印制電路信息;2016年01期
5 黨楠;;自動(dòng)光學(xué)檢測(cè)技術(shù)在工業(yè)質(zhì)量檢測(cè)中的應(yīng)用[J];自動(dòng)化與儀器儀表;2015年09期
6 陳小艷;王強(qiáng);李柏林;;改進(jìn)的Hough變換檢測(cè)圓方法[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2015年08期
7 黃銀花;李東波;;高速高精度印刷電路板表面貼裝自動(dòng)光學(xué)檢測(cè)系統(tǒng)的研究[J];機(jī)床與液壓;2014年21期
8 姜柏軍;鐘明霞;;改進(jìn)的直方圖均衡化算法在圖像增強(qiáng)中的應(yīng)用[J];激光與紅外;2014年06期
9 潘憶江;黃際彥;吳波;母國(guó)才;;PCB中圓形圖像的自動(dòng)光學(xué)檢測(cè)研究[J];現(xiàn)代電子技術(shù);2014年08期
10 晏祖根;李明;徐克非;孫小華;閆志鵬;孫智慧;;高速機(jī)器人分揀系統(tǒng)機(jī)器視覺(jué)技術(shù)的研究[J];包裝與食品機(jī)械;2014年01期
,本文編號(hào):1975312
本文鏈接:http://sikaile.net/shoufeilunwen/xixikjs/1975312.html