机械工程材料, 2014, 38(8): 88-92.
承压状态下氟橡胶O型密封圈耐酸性介质腐蚀性能
曾德智 1, , 李坛 2, , 雷正义 3, {"currentpage":1,"firstResult":0,"maxresult":10,"pagecode":5,"pageindex":{"endPagecode":5,"startPagecode":1},"records":[{"abstractinfo":"系统采用PCI-1428图像采集卡、CCD图像传感器及摄像机、PC机搭建VFD显示图像缺陷检测硬件平台,采集VFD图像；并用LabVIEW与IMAQ-VISION软件进行图像自动拼接、图像缺陷检测、工作台控制以及数据库等系统软件设计.为了获得较好的图像效果,针对采集的VFD图像特点,先用灰度变换、平滑滤波、灰度阈值分割、图像二值化等方法对其进行预处理,接着完成VFD显示图像的图案连码、断码、缺损和疵点等多种缺陷检测.经测试和实际运行,结果表明该检测系统具有速度快、范围宽、精度高、漏检误检率低等一系列优点,检测结果理想,实现了预期的检测功能和检测效果.","authors":[{"authorName":"李定珍","id":"a14eec9b-20b9-42a1-ad04-f8a96bcaadcc","originalAuthorName":"李定珍"},{"authorName":"王萍","id":"94e9588f-701a-403e-9f56-af0fe7ec20bb","originalAuthorName":"王萍"}],"doi":"10.3788/YJYXS20132801.0138","fpage":"138","id":"7795427a-9ff9-445d-8947-07e437c42549","issue":"1","journal":{"abbrevTitle":"YJYXS","coverImgSrc":"journal/img/cover/YJYXS.jpg","id":"72","issnPpub":"1007-2780","publisherId":"YJYXS","title":"液晶与显示 "},"keywords":[{"id":"491db58b-b180-48ca-80fa-3ce9979e79a1","keyword":"真空荧光显示屏","originalKeyword":"真空荧光显示屏"},{"id":"8b354e73-a248-4b2e-8d3d-3bcb923bba55","keyword":"缺陷检测","originalKeyword":"缺陷检测"},{"id":"5fd419c4-8772-46d6-af61-09f7e7bf6a2e","keyword":"图像拼接","originalKeyword":"图像拼接"},{"id":"e91fe387-2337-4adb-ac96-d8ffe875a840","keyword":"模版匹配","originalKeyword":"模版匹配"},{"id":"291e0aaa-b4ab-435b-b661-f7ed2fbfcbfc","keyword":"疵点检测","originalKeyword":"疵点检测"}],"language":"zh","publisherId":"yjyxs201301025","title":"VFD显示图像缺陷检测技术研究","volume":"28","year":"2013"},{"abstractinfo":"针对带钢表面缺陷的特点,提出了一种基于图像零均值化的检测方法.首先,通过对测试图像进行零均值化,以消除光照对检测的影响；其次,利用维纳滤波对零均值化图像进行滤波除噪；在此基础上,采用Sobel进行锐化处理；最后,通过最大类间方差法进行图像分割,从而实现对带钢表面缺陷的检测.试验表明,本方法能够有效抑制图像背景干扰,有效地实现带钢缺陷的快速检测.","authors":[{"authorName":"管声启","id":"39fd8919-fab5-4a85-a8be-f8b8ec13cbf1","originalAuthorName":"管声启"},{"authorName":"师红宇","id":"3ef7129a-c7aa-461d-95f4-12cd04f14f68","originalAuthorName":"师红宇"},{"authorName":"王燕妮","id":"06287912-c5cf-434e-92cd-7b343253b121","originalAuthorName":"王燕妮"}],"doi":"","fpage":"59","id":"be95fc60-f94c-4b3c-9fdf-d22ca12dcb89","issue":"4","journal":{"abbrevTitle":"GTYJXB","coverImgSrc":"journal/img/cover/GTYJXB.jpg","id":"30","issnPpub":"1001-0963","publisherId":"GTYJXB","title":"钢铁研究学报"},"keywords":[{"id":"a710e8be-5f04-41c3-8a2a-64579f4cce76","keyword":"带钢缺陷","originalKeyword":"带钢缺陷"},{"id":"fbeeb524-56a1-44db-b060-3efed64107da","keyword":"零均值化","originalKeyword":"零均值化"},{"id":"0075136d-3d27-4204-bfd0-8dfd22133a9b","keyword":"维纳滤波","originalKeyword":"维纳滤波"},{"id":"e2b64ef3-6394-4a2c-8a8d-a3bdbefff279","keyword":"缺陷检测","originalKeyword":"缺陷检测"}],"language":"zh","publisherId":"gtyjxb201304012","title":"基于图像零均值化的带钢缺陷检测","volume":"25","year":"2013"},{"abstractinfo":"针对木板表面节子缺陷被染料染色后难以识别的问题，本文利用图像融合技术提出一种新的木板表面缺陷检测方法。该方法采集被染色木板的近红外图像和可见光图像，使用加权平均法、主成分分析(PCA)算法、小波变换、Laplacian金字塔变换等不同的融合算法对采集的近红外图像和可见光图像进行融合，然后对不同算法融合后的图像仔细观察分析和比对并计算其信息熵。实验结果证实，融合后的图像能够明显地辨别出染色后的木板缺陷，并且基于 Laplacian 金字塔算法的融合效果最好。","authors":[{"authorName":"李漫丽","id":"060cf2d1-b05b-489d-be43-63c2647576b2","originalAuthorName":"李漫丽"},{"authorName":"赵鹏","id":"6cf4700b-6a35-450b-95af-d1fd58f4c8ac","originalAuthorName":"赵鹏"}],"doi":"10.3788/YJYXS20163109.0882","fpage":"882","id":"30001f02-013f-4a8a-85b3-df14c38a238f","issue":"9","journal":{"abbrevTitle":"YJYXS","coverImgSrc":"journal/img/cover/YJYXS.jpg","id":"72","issnPpub":"1007-2780","publisherId":"YJYXS","title":"液晶与显示 "},"keywords":[{"id":"f5ab22c5-9314-44b0-ab95-29bdd71a95e1","keyword":"图像融合","originalKeyword":"图像融合"},{"id":"ef244f38-5e9d-4211-9c71-f8fcfc8c0c69","keyword":"缺陷检测","originalKeyword":"缺陷检测"},{"id":"5e3c759c-794f-4e41-832e-430dadff9780","keyword":"近红外图像","originalKeyword":"近红外图像"},{"id":"6049e6d3-9102-4d1f-aad5-1d7af1f3c1d7","keyword":"可见光图像","originalKeyword":"可见光图像"}],"language":"zh","publisherId":"yjyxs201609006","title":"基于图像融合的木板表面缺陷检测研究","volume":"31","year":"2016"},{"abstractinfo":"针对钢铁铸坯表面检测及缺陷识别问题,从图像处理及机器学习角度,提出一种基于Adaboost算法的进行钢铁铸坯表面缺陷检测,并结合Gabor小波和Canny边缘检测进行处理,排除伪缺陷的新方法.大量试验表明:该方法能够较好地检出具有缺陷的钢铁铸坯,且具有准确率高、速度快、易实施等优点.","authors":[{"authorName":"吴家伟","id":"4992a0b7-ba95-4aec-8064-9f56f5473cee","originalAuthorName":"吴家伟"},{"authorName":"严京旗","id":"ebbeb0f4-1357-4520-870a-212c7c3619bc","originalAuthorName":"严京旗"},{"authorName":"方志宏","id":"436b83bb-4756-4350-b7e4-075ac1850828","originalAuthorName":"方志宏"},{"authorName":"夏勇","id":"128f11fa-7c00-4d07-9530-a981fa328e65","originalAuthorName":"夏勇"}],"doi":"","fpage":"59","id":"b05d9ab3-fc11-46c0-9572-937e0ad40d2e","issue":"9","journal":{"abbrevTitle":"GTYJXB","coverImgSrc":"journal/img/cover/GTYJXB.jpg","id":"30","issnPpub":"1001-0963","publisherId":"GTYJXB","title":"钢铁研究学报"},"keywords":[{"id":"152682f5-08e6-4d6b-847b-066b33065406","keyword":"缺陷检测","originalKeyword":"缺陷检测"},{"id":"c8c57a23-ff71-43da-bc6f-ff076b925c25","keyword":"Adaboost算法","originalKeyword":"Adaboost算法"},{"id":"7e16af1d-9a41-48d7-a6d5-16374170f0c7","keyword":"Haar特征","originalKeyword":"Haar特征"},{"id":"232e7cab-4072-4135-8345-368c5ad07809","keyword":"Gabor小波","originalKeyword":"Gabor小波"},{"id":"6477361c-e46f-4d7c-ac9f-ceff84c820ce","keyword":"Canny边缘检测","originalKeyword":"Canny边缘检测"}],"language":"zh","publisherId":"gtyjxb201209012","title":"基于Adaboost改进算法的铸坯表面缺陷检测方法","volume":"24","year":"2012"},{"abstractinfo":"针对带钢表面缺陷的特点,提出了1种基于图像预处理消除光照不均等的干扰且用神经网络进行缺陷识别的检测方法.带钢缺陷的检测分为3步:首先,对采集的图像进行预处理,通过图像零均值化以消除光照对检测的影响,分别利用维纳滤波和sobel算子对图像进行滤波除噪和锐化处理；其次,通过最大类间方差法进行图像分割以及计算面积来判断是否存在缺陷；最后,在提取图像特征的基础上,通过设计人工神经网络识别带钢缺陷类型.实验表明,采用的方法能够有效抑制图像背景干扰,能够有效地实现带钢缺陷的快速检测.","authors":[{"authorName":"管声启","id":"e0731f5c-3231-46d9-ac60-5ed62b7db540","originalAuthorName":"管声启"},{"authorName":"王燕妮","id":"da9368dd-43fc-4f4f-80b1-5fcfff6d1908","originalAuthorName":"王燕妮"},{"authorName":"师红宇","id":"4534f3dc-b38d-4f5a-8951-97e7c5ac9963","originalAuthorName":"师红宇"}],"doi":"","fpage":"22","id":"8716ab7f-c41e-4721-8f43-7849d62f3dfb","issue":"1","journal":{"abbrevTitle":"GTYJ","coverImgSrc":"journal/img/cover/GTYJ.jpg","id":"29","issnPpub":"1001-1447","publisherId":"GTYJ","title":"钢铁研究"},"keywords":[{"id":"de44107d-6a1c-4491-ae34-455b8722576c","keyword":"带钢缺陷","originalKeyword":"带钢缺陷"},{"id":"e7887377-51f3-4dce-a69c-c76c83640a9e","keyword":"图像预处理","originalKeyword":"图像预处理"},{"id":"1c829cac-0498-45b3-9bb0-26d969180f82","keyword":"人工神经网络","originalKeyword":"人工神经网络"},{"id":"b7442f33-a6ad-4f58-b08f-5eee215b1f6f","keyword":"缺陷检测","originalKeyword":"缺陷检测"}],"language":"zh","publisherId":"gtyj201301006","title":"基于图像预处理的神经网络带钢缺陷检测","volume":"41","year":"2013"},{"abstractinfo":"提出一种以MATLAB为主要工具的TFT-LCD屏显示缺陷检测方案,该方案根据CMOS工业摄像机采集到的数字图像,结合二维图像拟合技术和以韦伯定律为原理的自动阈值获取技术,使用MATLAB作为数字图像分析工具实现对屏幕缺陷的检测.介绍了该方案的硬件组成和检测原理.给出了MATLAB算法流程和部分代码、介绍了GUT检测界面的设计和详细的检测步骤及结果,为TFT-LCD液晶屏显示缺陷的检测提供了一种快速有效的方法.","authors":[{"authorName":"刘毅","id":"c6608f18-b430-47a7-bec7-1ade6d5c0823","originalAuthorName":"刘毅"},{"authorName":"郑学仁","id":"d2fbfc88-198e-422c-a693-e2bb9eba0956","originalAuthorName":"郑学仁"},{"authorName":"王亚南","id":"af30dbb8-747a-438d-8a3f-260027fc5f59","originalAuthorName":"王亚南"},{"authorName":"梁志明","id":"0f2943e1-43b3-482e-9302-118b32ee14a0","originalAuthorName":"梁志明"}],"doi":"10.3969/j.issn.1007-2780.2007.06.016","fpage":"731","id":"6dda346f-a526-41cb-9605-63d400a58926","issue":"6","journal":{"abbrevTitle":"YJYXS","coverImgSrc":"journal/img/cover/YJYXS.jpg","id":"72","issnPpub":"1007-2780","publisherId":"YJYXS","title":"液晶与显示 "},"keywords":[{"id":"1aeb9e70-a2aa-4c0b-a5c8-ed680248cc15","keyword":"TFT-LCD","originalKeyword":"TFT-LCD"},{"id":"27313e5b-ff18-41c3-8390-019948a4abfc","keyword":"MURA","originalKeyword":"MURA"},{"id":"782901d6-99d1-46a6-877d-feb5ccc55c66","keyword":"MATLAB","originalKeyword":"MATLAB"},{"id":"cb787a15-1260-45fe-82b8-6262cac7a35e","keyword":"缺陷检测","originalKeyword":"缺陷检测"}],"language":"zh","publisherId":"yjyxs200706016","title":"MATLAB在TFT-LCD屏显示MURA缺陷检测的应用","volume":"22","year":"2007"},{"abstractinfo":"针对FED显示屏电极的特征,提出一种FED电极缺陷检测系统,用于检测FED电极的短路和断路等缺陷.系统分为硬件和软件两部分,硬件部分由CCD摄像头初始定位和对准模块、单片机数据测试和传输模块、计算机数据接收和处理模块组成;软件部分包括单片机预处理部分的底层程序设计和计算机部分面向对象的高级程序设计.经过硬件设计安装和软件编程调试,该FED电极缺陷检测系统已经在实验中得到应用.","authors":[{"authorName":"张永爱","id":"58fc51fc-99d9-4af6-959d-ce1ad003c9dd","originalAuthorName":"张永爱"},{"authorName":"张杰","id":"3b2696d5-43cd-4578-b6fa-8eea34fd8ab9","originalAuthorName":"张杰"},{"authorName":"许华安","id":"9322202c-e36f-4140-81cc-f4ac7be520fe","originalAuthorName":"许华安"},{"authorName":"姚亮","id":"ac638376-b157-4d40-9f77-e86910474f88","originalAuthorName":"姚亮"},{"authorName":"郭太良","id":"5e316485-1df8-4681-8c0c-bd7eaaa7fcc5","originalAuthorName":"郭太良"}],"doi":"10.3969/j.issn.1007-2780.2010.02.012","fpage":"215","id":"1db2dcc0-93aa-41d5-b71f-58d3cd02121e","issue":"2","journal":{"abbrevTitle":"YJYXS","coverImgSrc":"journal/img/cover/YJYXS.jpg","id":"72","issnPpub":"1007-2780","publisherId":"YJYXS","title":"液晶与显示 "},"keywords":[{"id":"9fea867f-8cdf-47b1-baa2-d60ab234ccb3","keyword":"FED电极","originalKeyword":"FED电极"},{"id":"9295bcd9-ddd0-4a4e-b19e-1dd63237f20c","keyword":"CCD","originalKeyword":"CCD"},{"id":"7439de94-9a96-493f-b202-42af7fce9849","keyword":"单片机","originalKeyword":"单片机"},{"id":"6a070ccb-89e1-461d-8b31-ddb25667cf9d","keyword":"Visual C++","originalKeyword":"Visual C++"},{"id":"72dc3ce9-9e12-477a-a1a4-de8863f928c1","keyword":"缺陷检测","originalKeyword":"缺陷检测"}],"language":"zh","publisherId":"yjyxs201002012","title":"基于探针法的FED电极缺陷检测系统设计","volume":"25","year":"2010"},{"abstractinfo":"在电池的生产过程中,不可避免地会生产出一些次品,因此有必要依托信息技术设计出一套合理的算法来自动完成不合格次品的检出.利用图像采集设备采集纽扣电池表面图像,对采集的图像依次进行混合噪声滤除、OSTU最佳阈值分割、图像字符定位分割、缺陷模式提取、BP神经网络缺陷分类,每一步在满足检测精度的前提下,以算法简洁、高效作为衡量标准,为算法移植到生产实践中的实时检测奠定基础.","authors":[{"authorName":"肖阔华","id":"79398573-3288-42d8-960c-2ffb87b2264d","originalAuthorName":"肖阔华"},{"authorName":"刘羽","id":"3180cadb-b313-486b-8f8e-5247d12c8e40","originalAuthorName":"刘羽"}],"doi":"","fpage":"127","id":"b85329a0-0bf9-4f18-aeb2-c441e33678e7","issue":"1","journal":{"abbrevTitle":"BMJS","coverImgSrc":"journal/img/cover/BMJS.jpg","id":"3","issnPpub":"1001-3660","publisherId":"BMJS","title":"表面技术 "},"keywords":[{"id":"00f19073-332e-4ec9-abda-8d819a4bae46","keyword":"缺陷检测","originalKeyword":"缺陷检测"},{"id":"d5ebd9ea-a22a-4af3-bb34-ab376ea0d66f","keyword":"数字图像处理","originalKeyword":"数字图像处理"},{"id":"f11bbcba-c6e2-41a7-ab10-83daff1b04db","keyword":"BP神经网络","originalKeyword":"BP神经网络"}],"language":"zh","publisherId":"bmjs201301036","title":"纽扣电池表面缺陷检测算法的研究","volume":"42","year":"2013"},{"abstractinfo":"为了跟踪和记录热障涂层缺陷产生、发展直至脱落的过程,对热障涂层试片组进行热循环实验,在实验前和不同实验阶段分别对各个试片进行闪光灯激励红外热像检测;对发现的可疑区进行了解剖分析,识别出微裂纹;利用含有自然缺陷和人工缺陷的试片对红外热像检测的检测能力进行了评价.结果表明:闪光灯激励红外热像检测技术能够检测出直径小于0.5mm的脱粘缺陷,还能够识别出微裂纹态,这种状态用肉眼从涂层表面无法识别;闪光灯激励红外热像检测技术不仅可用于热障涂层的缺陷检测,还有希望用于热障涂层的寿命评估.","authors":[{"authorName":"刘颖韬","id":"8059fa1f-3b1c-4a2f-ac3c-2854a26a7e50","originalAuthorName":"刘颖韬"},{"authorName":"牟仁德","id":"5ffef087-7d1f-431d-a40c-6a176c4a2392","originalAuthorName":"牟仁德"},{"authorName":"郭广平","id":"7e1bd9d8-f7da-4302-9306-41c27228b296","originalAuthorName":"郭广平"},{"authorName":"杨党纲","id":"f18480f2-a6df-4000-bcec-645c53ebb0ac","originalAuthorName":"杨党纲"},{"authorName":"唐佳","id":"9cbfbab4-e3ed-4f74-8fb3-f9f433394fe0","originalAuthorName":"唐佳"}],"doi":"10.11868/j.issn.1005-5053.2015.6.014","fpage":"83","id":"3518ceea-f5d5-46cb-a8bb-4030117a0dc5","issue":"6","journal":{"abbrevTitle":"HKCLXB","coverImgSrc":"journal/img/cover/HKCLXB.jpg","id":"41","issnPpub":"1005-5053","publisherId":"HKCLXB","title":"航空材料学报"},"keywords":[{"id":"8f71c859-2520-45cc-b5cb-24569ea522df","keyword":"无损检测","originalKeyword":"无损检测"},{"id":"0d987a00-c634-411f-a77c-b93fe90a38a9","keyword":"热障涂层","originalKeyword":"热障涂层"},{"id":"936eb2cd-421e-4fc1-91ae-0af682ca30b8","keyword":"红外热像检测","originalKeyword":"红外热像检测"},{"id":"a06a9b8d-f562-492b-a658-ca5638d8b3fb","keyword":"缺陷检测","originalKeyword":"缺陷检测"}],"language":"zh","publisherId":"hkclxb201506014","title":"热障涂层闪光灯激励红外热像检测","volume":"35","year":"2015"},{"abstractinfo":"复合材料检测技术在复合材料生产应用中起着非常重要的作用.本文介绍了国内外复合材料检测研究的不同方法,包括传统的声、光、电磁波等检测技术以及近年来发展比较迅速的新的检测技术,并扼要阐述了小波变换、神经网络等几种在复合材料检测过程中有着相当重要作用的信号处理方法.","authors":[{"authorName":"葛邦","id":"1c060bbc-4905-4c42-97fd-ad3315c81f95","originalAuthorName":"葛邦"},{"authorName":"杨涛","id":"ed06cead-6a4d-4842-b296-5ee556a0004e","originalAuthorName":"杨涛"},{"authorName":"高殿斌","id":"0493d346-0946-4a8e-bfcd-181b38c36cfd","originalAuthorName":"高殿斌"},{"authorName":"李明","id":"f77d5548-2986-4ad9-9de1-d815e74ba7b6","originalAuthorName":"李明"}],"doi":"10.3969/j.issn.1003-0999.2009.06.018","fpage":"67","id":"5eeb38b5-7903-4b49-84ed-7b2c92f239c9","issue":"6","journal":{"abbrevTitle":"BLGFHCL","coverImgSrc":"journal/img/cover/BLGFHCL.jpg","id":"6","issnPpub":"1003-0999","publisherId":"BLGFHCL","title":"玻璃钢/复合材料"},"keywords":[{"id":"180cef54-938e-4fc4-bce8-d35fbad668bc","keyword":"复合材料","originalKeyword":"复合材料"},{"id":"4322dc4e-9fd8-49e7-8def-5caf7af88c36","keyword":"无损检测","originalKeyword":"无损检测"},{"id":"244866d5-b271-4c03-9adb-7dbff9951fd0","keyword":"缺陷检测","originalKeyword":"缺陷检测"},{"id":"19f4ce58-d1ed-4375-b75e-cc3353b0a2d0","keyword":"信号处理","originalKeyword":"信号处理"}],"language":"zh","publisherId":"blgfhcl200906018","title":"复合材料无损检测技术研究进展","volume":"","year":"2009"}],"totalpage":1478,"totalrecord":14778}