【摘要】：鋁合金激光焊接工藝廣泛應(yīng)用于民用飛機(jī)壁板等航空航天結(jié)構(gòu)件的制造。在激光焊接缺陷檢測(cè)中,傳統(tǒng)X射線人工檢測(cè)結(jié)果受膠片本身局限、主觀人為因素影響較大。隨著新一代X射線實(shí)時(shí)成像技術(shù)以及圖像模式識(shí)別技術(shù)的飛速發(fā)展,使得基于X射線數(shù)字圖像的計(jì)算機(jī)輔助識(shí)別鋁合金激光焊接缺陷成為可能。本文針對(duì)鋁合金激光焊件X射線實(shí)時(shí)成像技術(shù)、圖像預(yù)處理技術(shù)以及缺陷提取與識(shí)別技術(shù)的應(yīng)用開展研究,初步實(shí)現(xiàn)了鋁合金激光焊接缺陷的自動(dòng)提取與識(shí)別。首先,在合理選擇硬件系統(tǒng)配置的基礎(chǔ)上,設(shè)計(jì)并搭建了面向鋁合金T型接頭激光焊接件的X射線實(shí)時(shí)成像系統(tǒng)。通過搭建的成像系統(tǒng),對(duì)鋁合金T型接頭激光焊件的X射線實(shí)時(shí)成像檢測(cè)技術(shù)進(jìn)行了系統(tǒng)的研究,并得到了最佳的成像工藝參數(shù):管電壓60kV,管電流0.3mA,焦距350mm,放大倍數(shù)2.4。其次,針對(duì)X射線原始圖像的特點(diǎn),通過圖像灰度轉(zhuǎn)換、圖像降噪以及圖像模糊增強(qiáng)的預(yù)處理功能對(duì)焊件X射線圖像進(jìn)行質(zhì)量改善處理。其中,通過對(duì)單一降噪方法的結(jié)合使用,提高了對(duì)混合噪聲的去除效果。之后,對(duì)傳統(tǒng)模糊增強(qiáng)算法的改進(jìn)應(yīng)用增強(qiáng)了圖像的對(duì)比度,為后續(xù)焊縫提取與缺陷分割提供了質(zhì)量良好的X射線圖像。再次,通過進(jìn)一步對(duì)X射線圖像列灰度曲線的分析,采用曲線擬合和灰度差分判定的方法完成了對(duì)復(fù)雜背景條件下焊縫區(qū)域的提取。同時(shí),通過自適應(yīng)形態(tài)學(xué)濾波算法模擬出焊縫背景圖像,再經(jīng)差影檢測(cè)和迭代閾值分割算法的處理,實(shí)現(xiàn)了焊縫中缺陷的分割。之后,利用輪廓提取與種子填充算法完成了對(duì)缺陷的提取。最后,在缺陷區(qū)域標(biāo)記的基礎(chǔ)上,完成了各類特征參數(shù)的提取與計(jì)算。根據(jù)缺陷的特征參數(shù)及其他X射線圖像影像特征,設(shè)計(jì)并開發(fā)了基于正向模糊推理的缺陷識(shí)別與分類專家系統(tǒng)。其中,專家系統(tǒng)知識(shí)庫中的經(jīng)驗(yàn)知識(shí)以規(guī)則的形式存在,用戶可對(duì)規(guī)則進(jìn)行修改、添加與刪除等操作。同時(shí),根據(jù)相關(guān)標(biāo)準(zhǔn)實(shí)現(xiàn)了鋁合金激光焊件圓形缺陷的評(píng)定。本文針對(duì)鋁合金T接頭激光焊件的特點(diǎn),通過X射線實(shí)時(shí)成像實(shí)驗(yàn)和計(jì)算機(jī)編程圖像處理仿真實(shí)驗(yàn),完成了對(duì)鋁合金激光焊件的X射線實(shí)時(shí)成像、X射線圖像處理以及圖像缺陷識(shí)別技術(shù)的研究,為鋁合金激光焊接結(jié)構(gòu)缺陷的無損檢測(cè)提供了新的途徑。
[Abstract]:The laser welding technology of aluminum alloy is widely used in the manufacture of aerospace structures such as civil aircraft wall panels. In laser welding defect detection, the traditional X-ray manual detection results are limited by the film itself, subjective human factors have a greater impact. With the rapid development of the new generation of X-ray real-time imaging technology and image pattern recognition technology, it is possible to identify the defects of aluminum alloy laser welding based on X-ray digital image. In this paper, the application of X-ray real-time imaging technology, image preprocessing technology and defect extraction and recognition technology for laser welding of aluminum alloy are studied, and the automatic extraction and recognition of aluminum alloy laser welding defects are preliminarily realized. Firstly, based on the reasonable selection of hardware system configuration, a real-time X-ray imaging system for laser welding of aluminum alloy T-joints is designed and built. By using the imaging system, the X-ray real time imaging technology for laser welding of aluminum alloy T joint is studied systematically. The optimum imaging parameters are obtained as follows: tube voltage 60 kV, tube current 0.3 Ma, focal length 350 mm, tube voltage 60 kV, tube current 0.3 Ma, focal length 350 mm, tube voltage 60 kV, tube current 0.3 Ma, focal length 350 mm. The magnification is 2.4. Secondly, according to the characteristics of the original X-ray image, the image quality is improved by image gray conversion, image denoising and image fuzzy enhancement. Among them, the combined use of a single noise reduction method improves the removal effect of mixed noise. Then, the improvement of the traditional fuzzy enhancement algorithm enhances the contrast of the image, and provides a good quality X-ray image for the subsequent weld extraction and defect segmentation. Thirdly, through the further analysis of X-ray image column gray curve, the method of curve fitting and gray difference judgment is used to complete the extraction of weld area under complex background conditions. At the same time, the weld seam background image is simulated by the adaptive morphological filtering algorithm, and then the defect segmentation in the weld is realized by differential image detection and iterative threshold segmentation algorithm. After that, the defect extraction is accomplished by contour extraction and seed filling algorithm. Finally, on the basis of defect area marking, the extraction and calculation of all kinds of feature parameters are completed. A defect recognition and classification expert system based on forward fuzzy reasoning is designed and developed according to the feature parameters of defects and other X-ray image features. Among them, the empirical knowledge in expert system knowledge base exists in the form of rules. Users can modify, add and delete rules. At the same time, the circular defects of laser welding parts of aluminum alloy are evaluated according to the relevant standards. In this paper, according to the characteristics of laser welding parts of aluminum alloy T-joints, X-ray real-time imaging of aluminum alloy laser welds has been completed by means of X-ray real-time imaging experiment and computer programming image processing simulation experiment. The research of X-ray image processing and image defect recognition provides a new way for nondestructive detection of aluminum alloy laser welding structural defects.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TG441.7

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