發(fā)布時間：2018-04-10 23:33

  本文選題：工業(yè)CT + 圖像處理�。� 參考：《河南科技大學》2017年碩士論文

【摘要】：材料服役過程中產(chǎn)生的損傷是導致產(chǎn)品安全可靠性和服役壽命降低的關鍵因素。隨著我國航空航天、國防軍工、軌道交通等重大工程的實施,關鍵零部件服役條件日益苛刻,安全可靠性要求越來越高,開展材料損傷研究顯得尤為重要。全面、準確獲取損傷信息是研究損傷的前提,傳統(tǒng)物理解剖法和數(shù)值模擬法因其盲目性、破壞性、不準確性導致無法全面獲得材料內(nèi)部損傷信息。工業(yè)CT因具有直觀、無損、高分辨率等優(yōu)勢為材料工作者研究損傷提供了新的思路。然而,目前基于工業(yè)CT技術開展材料損傷研究的對象多為實驗室條件下單一拉伸、壓縮或彎曲加載環(huán)境產(chǎn)生的損傷,同時,在CT圖像處理方法的探索及損傷行為的有效表征方面不夠完善。本文以實際服役條件下產(chǎn)生的典型裂紋損傷為研究對象,通過工業(yè)CT系統(tǒng)對其進行360°掃描,獲得了含有損傷信息的Top、Front、Right視圖方向灰度切片圖像。采用Matlab、ImageJ等軟件,對各視圖方向圖像進行了詳細、深入的分析,提出了具有針對性的圖像處理方法,實現(xiàn)了基于圖像處理的損傷行為全方位信息有效表征,并對結果進行了驗證。結果表明:Top視圖方向灰度切片圖像最佳處理方法為:中值濾波(9×9濾波器)+log算子邊緣檢測;Front視圖方向灰度切片圖像最佳處理方法為:預處理(對比度增強(0.8)+中值濾波(7×7濾波器))+迭代法圖像分割+log算子邊緣檢測。針對Right視圖方向典型灰度切片圖像,提出了基于圖像各行Gaussian擬合統(tǒng)計分布特征的局部閾值算法,該算法的核心在于:以行為單位,基于Gaussian函數(shù)擬合曲線3?準則確定各行代表分割閾值,最后通過對圖像每行進行分割而得到整體圖像,同時對失真和噪聲問題給出了解決方案,處理結果較為理想。將典型圖像處理結果與實際情況進行對比,發(fā)現(xiàn)處理誤差均控制在2%以內(nèi)。其中,代表性圖像Top522中裂紋區(qū)面積為9.62 mm2;連續(xù)而封閉的裂紋邊緣周長為61.30 mm,裂紋區(qū)等效直徑為29.68 mm;試樣區(qū)等效直徑為30.95mm,試樣區(qū)邊緣圓度為0.9。Right286圖像中裂紋末端距試樣上表面高度為30.24 mm,距凹槽底端的距離為23.13 mm。裂紋末端尾部距試樣左邊界為16.24mm;距右邊界為14.45 mm。試樣總體高度為39.60 mm。本文研究結果實現(xiàn)了工業(yè)CT圖像的有效處理以及材料內(nèi)部損傷全方位信息的定量表征,不僅可以彌補物理解剖法的盲目,又可為數(shù)值模擬結果的驗證提供依據(jù),對重大工程領域?qū)嶋H服役工況下的材料損傷分析具有重要指導意義。
[Abstract]:Materials produced in the process of injury is the key factor leading to reduce the safety and reliability of the product and service life. As China's aerospace, defense industry, the implementation of rail transportation and other major projects, key parts of the increasingly harsh conditions of service, safety and reliability of the increasingly high demand, to carry out the research on material damage is very important. A comprehensive, accurate injury information is the premise of the traditional physical injury, anatomical method and numerical simulation for the blind, destructive, inaccuracy can lead to full access to internal damage information materials. Industrial CT because of its intuitive, nondestructive, provides a new way for studying high resolution damage material. However, based on the current research object material damage to carry out industrial CT technology for the laboratory under a tensile, compression and bending loading environment damage, at the same time, in the CT image The exploration of effective characterization and damage behavior of physical method is not perfect. In this paper, the actual service conditions typical crack damage caused by the industry as the research object, CT system is 360 degrees on the scanning, the damage information containing Top, Front, Right view direction gray slice images. By using Matlab, ImageJ and other software. The image of the view direction of a detailed, in-depth analysis, proposed the image processing method, the damage behavior of image processing based on the characterization of a full range of information effectively, and the results were validated. The results show that the Top view direction gray slice image processing method is the best: the median filter (9 x 9 filter) +log edge detection; Front view direction gray slice image processing method for the optimal pretreatment (contrast enhancement (0.8) + (7 x 7 median filter filter)) + +log iterative method for image segmentation Edge detection. According to the Right view direction of typical gray images, we propose a local threshold algorithm for image feature line Gaussian fitting based on statistic distribution, the core of the algorithm is in units based on curve fitting function Gaussian 3? Criteria determine the row generation threshold by the end of the table, each row of the image segmentation and get the whole at the same time, image distortion and noise solutions were given, satisfactory result. Comparing the typical image processing results with the actual situation, found that the processing error is controlled within 2%. Among them, on behalf of crack image in the Top522 area of 9.62 mm2; continuous and closed crack edge perimeter is 61.30 mm, the crack area equivalent diameter is 29.68 mm; sample area equivalent diameter is 30.95mm, sample edge roundness for crack tip in the 0.9.Right286 image from the specimen surface height is 30 .24 mm, from the bottom of the groove distance is 23.13 mm. at the end of the tail from the left edge of crack for 16.24mm; from the right boundary of 14.45 mm. sample overall height is 39.60 mm. the results of this paper can achieve a quantitative sign of industrial CT image processing and effective material internal damage of a full range of information, not only can make up for the physical anatomy method blind, provide the basis for verification and numerical simulation results, the major project in the field of actual service material damage condition analysis has important guiding significance.

【學位授予單位】：河南科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TB302;TP391.41

【參考文獻】

相關期刊論文 前10條

1 薛巖;周廣言;李佳;閆臣;;油氣管道環(huán)焊縫自動超聲檢測與射線檢測方法對比[J];無損檢測;2016年11期

2 梁志虎;張小寧;岳俊峰;屠震濤;黃泰鈞;梁鵬飛;王利民;;基于雙線性插值最值濾波的Mura改善方法[J];光子學報;2016年07期

3 萬響亮;李光強;吳開明;;低合金高強鋼針狀鐵素體組織特征和形成機理[J];鋼鐵研究學報;2016年06期

4 楊志軍;陳亮;劉延雷;陳德姝;劉玉琢;;焊縫裂紋缺陷的磁粉檢測自動識別方法研究[J];制造業(yè)自動化;2016年04期

5 劉紅麗;薛克敏;左標;李萍;;H13等徑角擠壓模具早期失效分析及結構優(yōu)化方案[J];制造技術與機床;2016年03期

6 譚成;馬黨參;王華昆;周健;遲宏宵;;H13鋼壓鑄模具的失效分析[J];機械工程材料;2016年01期

7 艾軼博;王楠;闕紅波;楊斌;張衛(wèi)冬;;工業(yè)CT的高鐵齒輪箱體材料缺陷識別[J];哈爾濱工業(yè)大學學報;2015年10期

8 王璐;李鵬志;王正;王蕊;;基于CT掃描的2A12鋁合金疲勞裂紋三維重建方法研究[J];機械工程學報;2015年24期

9 巴鈞濤;高建軍;李金良;王歡;邵奎祥;;20MnMoNb鍛件超標缺陷解剖分析[J];大型鑄鍛件;2015年05期

10 郭智敏;倪培君;曹玉玲;齊子誠;喬日東;;工業(yè)CT系統(tǒng)空間分辨率兩種測試方法分析與評價[J];CT理論與應用研究;2015年03期

相關博士學位論文 前2條

1 劉慧;基于CT圖像處理的凍結巖石細觀結構及損傷力學特性研究[D];西安科技大學;2013年

2 李林升;基于CT圖像邊緣提取的工件應力分析及疲勞壽命預測研究[D];重慶大學;2011年

相關碩士學位論文 前4條

1 盧鵬;基于高分辨率CT成像的疲勞裂紋擴展行為研究[D];南昌航空大學;2015年

2 丁艷紅;含夾雜材料熱變形及損傷行為研究[D];太原科技大學;2015年

3 侯佳琳;介質(zhì)破壞的梯度損傷理論及其數(shù)值模擬研究[D];長沙理工大學;2012年

4 王鈺;基于回旋曲面圖像的三維重構與紋理拼接[D];西安理工大學;2010年

，

本文編號：1733456