發(fā)布時(shí)間：2018-05-08 01:02

  本文選題：機(jī)器視覺 + 焊縫缺陷檢測�。� 參考：《江南大學(xué)》2017年碩士論文

【摘要】：焊接技術(shù)是一門非常重要的基礎(chǔ)工藝學(xué)科,它在現(xiàn)代制造業(yè)中起著重要作用。如今,無損檢測手段如X射線成像等技術(shù),已廣泛應(yīng)用于焊接工件的質(zhì)量檢測中。本文主要針對金屬包裝行業(yè)中常見的薄壁金屬罐的焊縫檢測技術(shù)進(jìn)行研究。由于薄壁金屬罐的特性,常用的無損檢測手段并不適用,而人工抽樣檢測是目前企業(yè)在實(shí)際生產(chǎn)中最為常用的檢測手段。但這種方式比較依賴于檢測人員的經(jīng)驗(yàn),且效率低、具有隨機(jī)性,極大的影響了企業(yè)生產(chǎn)的效率及產(chǎn)品的質(zhì)量。受到人工目測檢測方式的啟發(fā),本文對視覺檢測的方式進(jìn)行了大量研究,設(shè)計(jì)了基于機(jī)器視覺方式的焊縫缺陷檢測及分類系統(tǒng)。系統(tǒng)通過工業(yè)攝像機(jī)等設(shè)備采集焊縫樣本圖像,通過圖像預(yù)處理操作對樣本進(jìn)行初步處理,然后使用改進(jìn)的背景差分和波形檢測等方法對焊縫缺陷進(jìn)行了檢測及類型判別。具體工作如下:首先,本文對系統(tǒng)的整體框架進(jìn)行了介紹,重點(diǎn)介紹了圖像采集系統(tǒng),并簡單介紹了焊縫圖像處理的軟件流程。此外,還對熔焊、虛焊、焊穿這三類典型的焊縫缺陷的成因及特點(diǎn)進(jìn)行了介紹。其次,本文介紹了焊縫圖像預(yù)處理的流程。預(yù)處理流程主要包括對圖像的篩選、對圖像有效區(qū)域的提取、圖像旋轉(zhuǎn)矯正以及焊縫核心區(qū)域的提取。圖像預(yù)處理能夠排除大部分離散偽缺陷的干擾,且將罐身區(qū)域焊縫圖像與兩端焊縫圖像區(qū)分開來,提取了圖像的核心區(qū)域。然后,本文詳細(xì)闡述了罐身區(qū)域焊縫缺陷的檢測與類型判別流程。本文提出了改進(jìn)的背景差分法,用于構(gòu)建焊縫圖像序列的背景模型,提取焊縫圖像的缺陷特征,并通過這些特征在面積、亮度、累加波形等方面的區(qū)別,對缺陷類型判別設(shè)計(jì)了相應(yīng)算法。最后,本文闡述了兩端焊縫缺陷的檢測方法。在這一章中提出了均值閾值分割的方法消除了殘影干擾,使用擬合波形方法判斷焊縫缺陷,并通過橫向累加波形的方法檢測虛焊缺陷。在線檢測實(shí)驗(yàn)結(jié)果表明,本文設(shè)計(jì)的整個(gè)焊縫缺陷檢測與分類系統(tǒng)達(dá)到了99%以上的準(zhǔn)確率,能夠滿足企業(yè)生產(chǎn)的實(shí)際需求。
[Abstract]:Welding technology is a very important basic technology subject, it plays an important role in modern manufacturing. Nowadays, nondestructive testing techniques, such as X-ray imaging, have been widely used in quality testing of welded workpieces. This paper mainly focuses on the weld inspection technology of thin-wall metal tank in metal packaging industry. Because of the characteristics of thin-walled metal tank, the commonly used nondestructive testing method is not suitable, and manual sampling testing is the most commonly used testing method in the actual production of enterprises at present. But this way depends on the experience of the examiner, and the efficiency is low, which has randomness, which greatly affects the production efficiency and the quality of the product. Inspired by the manual visual inspection method, the visual inspection method is studied in this paper, and a weld defect detection and classification system based on machine vision is designed. The system collects the weld seam sample image through the equipment such as the industrial camera, carries on the preliminary processing through the image preprocessing operation, and then uses the improved background difference and the waveform detection method to detect the weld seam defect and to distinguish the type. The main work is as follows: firstly, the whole frame of the system is introduced, the image acquisition system is introduced, and the software flow of weld image processing is briefly introduced. In addition, the causes and characteristics of three kinds of typical weld defects, such as weld welding, virtual welding and weld penetration, are also introduced. Secondly, this paper introduces the process of weld image preprocessing. The process of preprocessing mainly includes image screening, extraction of image effective region, image rotation correction and extraction of weld core area. The image preprocessing can eliminate the interference of most discrete pseudo-defects and distinguish the weld image of the tank body region from the weld image of both ends and extract the core area of the image. Then, this paper expatiates the process of weld defect detection and type discrimination in the tank body area. In this paper, an improved background difference method is proposed to construct the background model of the weld image sequence, extract the defect features of the weld image, and through the differences of these features in area, brightness, cumulative waveform, etc. The corresponding algorithm is designed for the discrimination of defect types. Finally, this paper describes the detection method of weld defects at both ends. In this chapter, the mean value threshold segmentation method is proposed to eliminate the residual interference, and the fitting waveform method is used to judge the weld defect, and the transverse accumulative waveform is used to detect the virtual welding defect. The on-line testing results show that the whole weld defect detection and classification system designed in this paper has the accuracy of more than 99% and can meet the actual requirements of the enterprise production.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG441.7;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張秀峰;管功湖;李祥;黃曉明;黃珍錫;;基于AVR單片機(jī)的多功能阻焊控制器[J];微特電機(jī);2012年06期

2 張小蓮;李群;殷明慧;葉星;鄒云;;一種引入停止機(jī)制的改進(jìn)爬山算法[J];中國電機(jī)工程學(xué)報(bào);2012年14期

3 封秀敏;劉麗婷;;焊接結(jié)構(gòu)的無損檢測技術(shù)[J];焊接技術(shù);2011年06期

4 高煒欣;胡玉衡;穆向陽;武曉萌;;埋弧焊X射線焊縫圖像缺陷分割檢測技術(shù)[J];儀器儀表學(xué)報(bào);2011年06期

5 黃政艷;;焊接設(shè)備技術(shù)現(xiàn)狀及發(fā)展趨勢[J];廣西輕工業(yè);2011年05期

6 高錦春;許佳明;;縫焊技術(shù)原理簡析[J];硅谷;2011年02期

7 邵家鑫;都東;朱新杰;高志凌;王晨;;基于X射線數(shù)字化圖像處理的雙面焊焊縫缺陷檢測[J];焊接學(xué)報(bào);2010年11期

8 康宜華;孫燕華;宋凱;;ERW管焊縫缺陷漏磁檢測方法可行性分析[J];測試技術(shù)學(xué)報(bào);2010年02期

9 鐘映春;;基于決策樹的焊縫缺陷類型識別研究[J];計(jì)算機(jī)工程與應(yīng)用;2008年20期

10 齊秀濱;;激光焊接過程視覺傳感技術(shù)的發(fā)展現(xiàn)狀[J];焊接學(xué)報(bào);2008年02期

相關(guān)碩士學(xué)位論文 前4條

1 梁硼;X射線焊縫圖像缺陷自動提取與識別技術(shù)研究[D];南京航空航天大學(xué);2012年

2 王煜;基于支持向量機(jī)的規(guī)則零件機(jī)器視覺檢測技術(shù)研究[D];東華大學(xué);2012年

3 李大力;底片掃描儀小型化及焊縫故障識別技術(shù)研究[D];東北大學(xué);2010年

4 王慧玲;基于機(jī)器視覺的焊縫缺陷檢測技術(shù)的研究[D];哈爾濱理工大學(xué);2008年

，

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