本文選題：焊接缺陷 + 缺陷檢測　； 參考：《南昌航空大學(xué)》2017年碩士論文

【摘要】：數(shù)字射線檢測是未來射線檢測的主流技術(shù),傳統(tǒng)人工評片方法已不適合數(shù)字圖像評定。隨著計(jì)算機(jī)技術(shù)的發(fā)展,焊接缺陷的自動檢測與識別是目前焊接缺陷無損檢測的熱點(diǎn)問題。以某型大尺寸環(huán)焊縫DR(Digital Radiography)采集圖像為研究對象,進(jìn)行焊接缺陷的自動檢測與識別研究,以Visual Studio 2010和OpenCV 3.0開源代碼為開發(fā)工具,開發(fā)焊接缺陷射線DR圖像自動檢測與識別軟件系統(tǒng)。主要研究工作如下:(1)通過DR圖像預(yù)處理算法提高DR圖像的對比度,提出了一種焊接缺陷射線DR圖像自動檢測算法。首先設(shè)置平滑半徑r,構(gòu)造(2r+1)×(2r+1)的平滑模板。然后采用圖像中值濾波創(chuàng)建模擬理想焊縫圖像,將模擬理想焊縫圖像與原始圖像進(jìn)行圖像減影運(yùn)算。最后尋找所有減影差值超過灰度連通性(給定閾值)的區(qū)域做為可疑缺陷。分析了灰度連通性和平滑半徑兩個(gè)檢測參數(shù)對焊接缺陷自動檢測結(jié)果的影響。(2)根據(jù)焊接缺陷自動檢測后生成的二值圖像和原始灰度圖像對所有可疑缺陷進(jìn)行特征參數(shù)分析與計(jì)算,提出了9個(gè)特征參數(shù)和計(jì)算算法,并得到了相應(yīng)的結(jié)果。根據(jù)缺陷特征參數(shù)提出了焊接缺陷的定性分析算法,實(shí)現(xiàn)了焊接缺陷的自動定性分析。(3)進(jìn)行了焊接缺陷射線DR圖像自動識別軟件系統(tǒng)的設(shè)計(jì)與集成。首先進(jìn)行了焊接缺陷自動識別軟件系統(tǒng)的總體框架設(shè)計(jì)。其次,設(shè)計(jì)并實(shí)現(xiàn)了自動識別系統(tǒng)的主界面、圖像幾何變換模塊、圖像預(yù)處理模塊、缺陷檢測模塊、缺陷統(tǒng)計(jì)與質(zhì)量評級模塊。最后主要采用Visual Studio 2010開發(fā)環(huán)境自主開發(fā)完成了一套焊接缺陷射線DR圖像自動識別軟件系統(tǒng),實(shí)現(xiàn)的主要功能有DR采集圖像的導(dǎo)入、DR圖像幾何變換、DR圖像預(yù)處理、焊接缺陷自動檢測、焊接缺陷參數(shù)計(jì)算、焊接缺陷定性分析、缺陷顯示列表、缺陷實(shí)時(shí)定位、缺陷特征編輯、缺陷統(tǒng)計(jì)、特殊缺陷描述、超標(biāo)缺陷信息顯示、質(zhì)量自動評級、質(zhì)量評級信息存儲等。(4)以分散氣孔、密集氣孔、夾鎢、未焊透等典型焊接缺陷圖像進(jìn)行軟件的測試分析,軟件測試結(jié)果表明:當(dāng)檢測參數(shù)選擇合理時(shí),焊接缺陷自動檢測結(jié)果效果與實(shí)際缺陷信息一致,缺陷定性分析算法基本合理,軟件總體運(yùn)行良好。
[Abstract]:Digital ray detection is the mainstream technology in the future. Traditional manual assessment method is not suitable for digital image evaluation. With the development of computer technology, automatic detection and identification of welding defects is a hot issue in nondestructive testing of welding defects. The automatic detection and identification of welding defects are carried out with the image acquisition of a large size ring weld DRM Digital Radiography. The open source code of Visual Studio 2010 and OpenCV 3.0 is used as the development tool. A software system for automatic detection and recognition of welding defect Dr images is developed. The main research work is as follows: (1) the contrast of Dr image is improved by Dr image preprocessing algorithm, and an automatic detection algorithm of welding defect ray Dr image is proposed. First, the smoothing radius r is set and the smooth template of 2r 1 脳 2r 1) is constructed. Then the image median filter is used to create the simulated ideal weld image, and the image subtraction operation is carried out between the simulated ideal weld image and the original image. Finally, all regions whose subtraction difference exceeds gray connectivity (given threshold) are found as suspicious defects. The influence of two detection parameters of gray connectivity and smooth radius on the automatic detection results of welding defects is analyzed. (2) based on the binary image and original gray image generated by automatic detection of welding defects, all suspicious defects are characterized. Parameter analysis and calculation, Nine characteristic parameters and calculation algorithms are proposed, and the corresponding results are obtained. According to the characteristic parameters of welding defects, a qualitative analysis algorithm for welding defects is proposed, and the automatic qualitative analysis of welding defects is realized. The software system for automatic recognition of welding defects based on X-ray Dr images is designed and integrated. Firstly, the overall frame of the software system for automatic recognition of welding defects is designed. Secondly, the main interface of automatic recognition system, image geometric transformation module, image preprocessing module, defect detection module, defect statistics and quality rating module are designed and implemented. Finally, a software system for automatic recognition of welding defect X-ray Dr image is developed by using Visual Studio 2010 development environment. The main functions of this system are the import of Dr images into Dr images and the pre-processing of Dr images. Welding defect automatic detection, welding defect parameter calculation, welding defect qualitative analysis, defect display list, defect real-time location, defect feature editing, defect statistics, special defect description, defect information display, quality automatic rating, The quality rating information is stored, etc.) the software is used to test and analyze the typical welding defect images, such as dispersed pores, dense pores, intercalated tungsten, not welded thoroughly, etc. The results of the software test show that: when the detection parameters are reasonable, The results of automatic detection of welding defects are consistent with the actual defect information, the qualitative analysis algorithm of defects is basically reasonable, and the software is running well.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 溫宗周;李健全;段俊瑞;劉W，

本文編號：2038064