【摘要】：在現(xiàn)如今的電子工業(yè)中,印制電路板(PCB)作為電子元器件的載體,其板載元器件的焊接質(zhì)量將直接影響到電子產(chǎn)品的性能,因此對PCB焊接質(zhì)量的檢測對工業(yè)生產(chǎn)而言意義重大。傳統(tǒng)檢測手段耗時耗力且可靠性差,為了提高工業(yè)生產(chǎn)效率、改善電子產(chǎn)品的性能和質(zhì)量,降低生產(chǎn)所耗成本,采用基于圖像處理技術(shù)的自動化焊接缺陷檢測方法,可以達(dá)到非接觸且高精度的檢測效果。 課題研究PCB人工焊接缺陷檢測與識別算法。實驗選用普通人工焊接的PCB為研究對象,經(jīng)掃描及焊點配準(zhǔn)后獲得PCB焊點圖像。對焊點圖像進(jìn)行灰度處理、中值濾波等預(yù)處理操作,以減少圖像噪聲,提高焊點圖像的清晰度。本文研究焊點圖像特征的提取方法,提出基于閾值分割技術(shù)提取焊點圖像的形狀特征；根據(jù)閾值分割結(jié)果提取焊點圖像前景和背景特征點,采用支持向量機(jī)(SVM)方法對原彩色焊點圖像進(jìn)行分類后并做灰度處理,得到只包含前景圖像的焊點灰度圖像,提取焊點灰度圖像的小波特征。 在完成焊點圖像的特征提取后,針對缺陷識別方法進(jìn)行研究,提出一種基于特征聚集度的模糊C均值聚類(FCM)與松弛約束支持向量機(jī)(RSVM)聯(lián)用的分類識別算法。算法首先對樣本特征數(shù)據(jù)進(jìn)行模糊C均值聚類,依據(jù)樣本隸屬度函數(shù)計算不同特征的特征聚集度,并由特征聚集度指標(biāo)改進(jìn)RSVM算法中的松弛量參數(shù),建立最終的分類器模型。實驗表明,本文提出的算法建立了泛化能力更強(qiáng)的分類模型,能有效抑制噪聲及模糊邊界點對分類模型的影響,在人工焊接缺陷識別的應(yīng)用中獲得了滿意的識別結(jié)果。
[Abstract]:In today's electronic industry, printed circuit board (PCB), as the carrier of electronic components, the welding quality of board components will directly affect the performance of electronic products. Therefore, the detection of PCB welding quality is of great significance to industrial production. In order to improve the efficiency of industrial production, improve the performance and quality of electronic products and reduce the cost of production, the automatic welding defect detection method based on image processing technology is adopted. Can achieve non-contact and high-precision detection effect. The PCB artificial welding defect detection and identification algorithm is studied in this paper. The PCB of ordinary manual welding was selected as the research object, and the PCB solder joint image was obtained after scanning and solder joint registration. In order to reduce the image noise and improve the definition of solder joint image, the gray level processing and median filtering are used to preprocess the solder joint image. In this paper, the feature extraction method of solder joint image is studied, and the shape feature of solder joint image is extracted based on threshold segmentation technique. According to the threshold segmentation results, the solder joint image foreground and background feature points are extracted, and the original color solder joint image is classified by support vector machine (SVM) method, and the gray level image containing only foreground image is obtained. Wavelet feature of solder joint gray image is extracted. After the feature extraction of solder joint image is completed, the defect recognition method is studied, and a fuzzy C-means clustering (FCM) based on feature aggregation and relaxation constraint support vector machine (RSVM) is proposed. Firstly, the fuzzy C-means clustering of the sample feature data is carried out, and the characteristic aggregation degree of different features is calculated according to the membership function of the sample. The relaxation parameter in RSVM algorithm is improved by the index of feature aggregation degree, and the final classifier model is established. The experimental results show that the proposed algorithm can effectively suppress the influence of noise and fuzzy boundary points on the classification model and obtain satisfactory recognition results in the application of manual welding defect recognition.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN41;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 周德儉;SMT組裝質(zhì)量檢測中的AOI技術(shù)與系統(tǒng)[J];電子工業(yè)專用設(shè)備;2002年02期

2 姚立新,張武學(xué),連軍莉;AOI系統(tǒng)在PCB中的應(yīng)用[J];電子工業(yè)專用設(shè)備;2004年05期

3 遲學(xué)芬,韓昌元,金輝;CCD推掃遙感成像中的欠采樣噪聲處理[J];電子學(xué)報;2003年09期

4 孫勁光,張文斌,朱世安;圖象灰度的處理方法及實現(xiàn)[J];遼寧工程技術(shù)大學(xué)學(xué)報(自然科學(xué)版);2002年03期

5 鮮飛;;AOI技術(shù)在SMT生產(chǎn)上的實施技巧與策略[J];國內(nèi)外機(jī)電一體化技術(shù);2006年01期

6 李薇;;PCB自動光學(xué)檢測系統(tǒng)[J];工業(yè)控制計算機(jī);2009年01期

7 李捷;唐星科;蔣延軍;;幾種邊緣檢測算法的比較研究[J];信息技術(shù);2007年09期

8 盧盛林;張憲民;鄺泳聰;;基于神經(jīng)網(wǎng)絡(luò)的PCB焊點檢測方法[J];華南理工大學(xué)學(xué)報(自然科學(xué)版);2008年05期

9 萬永菁;張佩;錢佳;;一種融合圖像濾波的FCM圖像分割算法及其應(yīng)用[J];華東理工大學(xué)學(xué)報(自然科學(xué)版);2013年02期

10 吳平峰;代宣軍;;PCB測試技術(shù)的介紹[J];現(xiàn)代機(jī)械;2009年04期

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