基于视觉信息的钢板连续生产线激光焊接关键技术的研究
|
摘要
在钢铁行业，板材在整个钢铁产品中的比重是衡量钢铁生产水平的重要依据。因此，板材生产线的连续稳定运行已是一个关键的研究课题。如何确保激光焊接的焊缝质量能够达到板材连续生产线的要求已经成为钢铁行业的关键技术之一。作为板材连续生产线的第一个工艺步骤，高功率激光焊接焊缝的焊后焊缝检测技术是判断焊缝质量是否满足连续生产线稳定连续运行的重要依据。
本文将机器视觉技术融入到针对钢板连续生产线激光焊接中。从分析激光焊接的机理入手，研究激光功率、焦点位置、焊接速度等钢板连续生产激光深熔焊的主要影响因素，针对激光焊机这个机电复杂的系统，分别从机械和电气两部分着手，剖析了某生产线激光焊机的各部分组成结构和特点，通过解析焊接各环节和约束条件，研究了激光焊机的工艺过程。
主要研究了激光焊接的微细焊缝的视觉特征提取方法。提出了一种区域搜索算法实现最优感兴趣区域的确定,在保留最多信息的同时，最大化地减小的计算负担。提出一种焊缝特征点鲁棒提取方法,利用最小二乘法，样条曲线拟合方法实现了结构光条纹信息的直线部分，曲线部分精确提取，进而实现了宽度、错边、余高等维系焊缝特征参数的提取提供了丰富的数据信息，以便于进一步的焊缝质量评估。
针对激光焊接的微细焊缝，研究了其三维重建的关键技术。提出了一种基于显微视觉的结构光视觉测量方法，建立了视觉模型，结构光测量模型，得到了焊缝视觉特征对应的笛卡尔空间的位置信息。进而提出一种基于弦长的B样条曲线拟合方法，减弱了测量噪声的影响，提高了焊缝曲线拟合的精度，最终结合视觉测量和拟合方法完成了激光焊接微细焊缝的三维重建。
分析了激光焊接焊缝缺陷种类与质量等级的基础上，重点研究了激光焊接焊缝质量监控技术，设计了一种基于结构光视觉图像的焊缝质量智能评估方法，采用一种递归神经网络分类器，根据图像处理技术所获取的焊缝表面几何特征对焊后焊缝的焊接质量进行等级划分；基于Labview虚拟仪器技术设计了一款新颖的焊缝质量监控软件，可识别焊缝的关键质量参数，实现了焊缝特征参数的计算、数据三维可视化和数据的存储管理,为提高焊接质量提供了有力的保证。根据激光焊接工艺分析和系统结构，提出钢板连续生产激光焊机的位置伺服跟踪方案，实现焊缝、焦点位置以及间隙调整等精确的速度和位置控制，为提高焊接质量提供了有力的保证。
In the iron and steel industry, the ratio of metal sheets in the whole steel products is animportant index mesauring the level of steel production. Thus, the stability, operating safety ofthe metal sheets production line have always been a key research project. Therefore, how toguarantee the quality of the laser welding seam to meet the requirements on the plate productionline continuous operation has become one of core issues. As first processing step, the high powerlaser welder post weld quality detection technology is a very valuable judgment whether thewelding quality of the high power laser welder meets the requirements of continuous operationof metal sheets production line
At First, from the analysis of the mechanism of the laser welding, the thesis studies somefactors that influence the high laser welding quality, such as laser power, the focus of theposition, the welding speed and so on. For the electromechanical complex systems of the laserwelder, all the parts of the structure and characteristics of the high laser welder are analysizedfrom the mechanical and electrical two parts. Then the laser welding process is determined by allthe parts of the structure of the high laser welde and related electrical constraint conditions.
The dissertation mainly studies the visual feature extraction method of the micro laser weldseam. A kind of region search algorithm is proposed to determine the optimal region of interest,which can keep the most information, maximize reduce calculation burden, at the sametime.Then, a weld feature point robust extraction method is presented by using the least squaremethod, the spline curve fitting method, which realizes the accurate extraction of straight part,curve part in the structure light stripe. And then some weld feature parameters, such as weldwidth, misalignment, reinforcement, can be recognized, which provides abundant datainformation, so as to further weld quality evaluation.
Then, a three dimension reconstructure method for the laser weld micro and the thin seam isstudied. A structure light vision measuring methods based on microscopic visual is proposed.And the corresponding visual model, structure light measurement model is established so as toobtain the weld visual characteristics corresponding position information in Cartesian space.Furthermore, And a kind of b-spline curve fitting method based on chord long is put forward,which help to reduce the influence of measurement noise and improve the weld curve fittingaccuracy. Finally the laser welding micro weld3d reconstruction is completed by combiningvisual measurement and fitting method.
On the basis of the analysis of the laser welding of weld defect types and quality level, thelaser welding seam quality monitoring technology is studied. And a kind of intelligent weldquality assessment methods based on structure light vision image is designed by using arecurrent neural network classifier. Then, the seam quality can be classed by using the weldgeometry characteristics from image processing results.Based on Labview virtual instrumenttechnology, a novel weld quality monitoring softwar that can identify the key parameters of theweld quality, realize the weld characteristic parameter calculation, data3d visualization and datastorage management is accomplished, which help to improve the laser weld quality and ensurethe safe operation of the metal sheet production line. According to the laser welding process analysis and system structure, a steel continuous production laser welder position servo trackingscheme that can weld and accurate speed and position control of the focus position and gapadjustment and provide an effective scheme to improve the quality of welding is proposed.
本文将机器视觉技术融入到针对钢板连续生产线激光焊接中。从分析激光焊接的机理入手，研究激光功率、焦点位置、焊接速度等钢板连续生产激光深熔焊的主要影响因素，针对激光焊机这个机电复杂的系统，分别从机械和电气两部分着手，剖析了某生产线激光焊机的各部分组成结构和特点，通过解析焊接各环节和约束条件，研究了激光焊机的工艺过程。
主要研究了激光焊接的微细焊缝的视觉特征提取方法。提出了一种区域搜索算法实现最优感兴趣区域的确定,在保留最多信息的同时，最大化地减小的计算负担。提出一种焊缝特征点鲁棒提取方法,利用最小二乘法，样条曲线拟合方法实现了结构光条纹信息的直线部分，曲线部分精确提取，进而实现了宽度、错边、余高等维系焊缝特征参数的提取提供了丰富的数据信息，以便于进一步的焊缝质量评估。
针对激光焊接的微细焊缝，研究了其三维重建的关键技术。提出了一种基于显微视觉的结构光视觉测量方法，建立了视觉模型，结构光测量模型，得到了焊缝视觉特征对应的笛卡尔空间的位置信息。进而提出一种基于弦长的B样条曲线拟合方法，减弱了测量噪声的影响，提高了焊缝曲线拟合的精度，最终结合视觉测量和拟合方法完成了激光焊接微细焊缝的三维重建。
分析了激光焊接焊缝缺陷种类与质量等级的基础上，重点研究了激光焊接焊缝质量监控技术，设计了一种基于结构光视觉图像的焊缝质量智能评估方法，采用一种递归神经网络分类器，根据图像处理技术所获取的焊缝表面几何特征对焊后焊缝的焊接质量进行等级划分；基于Labview虚拟仪器技术设计了一款新颖的焊缝质量监控软件，可识别焊缝的关键质量参数，实现了焊缝特征参数的计算、数据三维可视化和数据的存储管理,为提高焊接质量提供了有力的保证。根据激光焊接工艺分析和系统结构，提出钢板连续生产激光焊机的位置伺服跟踪方案，实现焊缝、焦点位置以及间隙调整等精确的速度和位置控制，为提高焊接质量提供了有力的保证。
In the iron and steel industry, the ratio of metal sheets in the whole steel products is animportant index mesauring the level of steel production. Thus, the stability, operating safety ofthe metal sheets production line have always been a key research project. Therefore, how toguarantee the quality of the laser welding seam to meet the requirements on the plate productionline continuous operation has become one of core issues. As first processing step, the high powerlaser welder post weld quality detection technology is a very valuable judgment whether thewelding quality of the high power laser welder meets the requirements of continuous operationof metal sheets production line
At First, from the analysis of the mechanism of the laser welding, the thesis studies somefactors that influence the high laser welding quality, such as laser power, the focus of theposition, the welding speed and so on. For the electromechanical complex systems of the laserwelder, all the parts of the structure and characteristics of the high laser welder are analysizedfrom the mechanical and electrical two parts. Then the laser welding process is determined by allthe parts of the structure of the high laser welde and related electrical constraint conditions.
The dissertation mainly studies the visual feature extraction method of the micro laser weldseam. A kind of region search algorithm is proposed to determine the optimal region of interest,which can keep the most information, maximize reduce calculation burden, at the sametime.Then, a weld feature point robust extraction method is presented by using the least squaremethod, the spline curve fitting method, which realizes the accurate extraction of straight part,curve part in the structure light stripe. And then some weld feature parameters, such as weldwidth, misalignment, reinforcement, can be recognized, which provides abundant datainformation, so as to further weld quality evaluation.
Then, a three dimension reconstructure method for the laser weld micro and the thin seam isstudied. A structure light vision measuring methods based on microscopic visual is proposed.And the corresponding visual model, structure light measurement model is established so as toobtain the weld visual characteristics corresponding position information in Cartesian space.Furthermore, And a kind of b-spline curve fitting method based on chord long is put forward,which help to reduce the influence of measurement noise and improve the weld curve fittingaccuracy. Finally the laser welding micro weld3d reconstruction is completed by combiningvisual measurement and fitting method.
On the basis of the analysis of the laser welding of weld defect types and quality level, thelaser welding seam quality monitoring technology is studied. And a kind of intelligent weldquality assessment methods based on structure light vision image is designed by using arecurrent neural network classifier. Then, the seam quality can be classed by using the weldgeometry characteristics from image processing results.Based on Labview virtual instrumenttechnology, a novel weld quality monitoring softwar that can identify the key parameters of theweld quality, realize the weld characteristic parameter calculation, data3d visualization and datastorage management is accomplished, which help to improve the laser weld quality and ensurethe safe operation of the metal sheet production line. According to the laser welding process analysis and system structure, a steel continuous production laser welder position servo trackingscheme that can weld and accurate speed and position control of the focus position and gapadjustment and provide an effective scheme to improve the quality of welding is proposed.
引文
[1]屈玉福,浦昭邦,王亚爱.视觉检测系统中亚像素边缘检测技术的对比研究.仪器仪表学报,2003,24(zl):460-462.
[2]周武,胡跃明.视基于相位相关和重采样的亚像素图像匹配算法.华南理工大学学报(自然科学版),2010,38(10):68-78.
[3]胡坤,周富强，张广军.一种快速结构光条纹中心亚像素精度提取方法.仪器仪表学报,2006,27(10):1326-1329.
[4]李金泉,王建伟，陈善本，吴林.一种改进的Zernike正交矩亚像素边缘检测算法.光学技术,2003,29(4):500-503.
[5] H B Chen, T Lin, S B Chen, J F Wang, J Q Jia, H Zhang. Adaptive control on wire feeding in robot arcwelding system. Proceeding of the IEEE International Conference on Robotics, Automation andMechatronics,2008, Chengdu, China, pp.119-122.
[6] P Koseeyaporn, G E Cook, A M Strauss. Adaptive voltage control in fusion arc welding. IEEETransactions on Industry Applications,2000,36(5):1300-1307.
[7] D V Nishar, J L Schiano, W R Perkins. Adaptive control of temperature in arc welding. IEEE ControlSystems Magazine,1994,14(4):4-12.
[8] G H Kim, S I Kang, S B Lee. A study on the estimate of weld bead shape and the compensation ofwelding parameters by considering weld defects in horizontal fillet welding. Proceeding of the ThirdInternational Conference on Knowledge-Based Intelligent Information Engineering Systems,1999,Adelaide, Australia, pp.212-216.
[9] Y M Zhang, R Kovacevic, L Li. Adaptive control of full penetration gas tungsten arc welding. IEEETransactions on Control Systems Technology,1996,4(4):394-403.
[10] W Lu, Y M Zhang, W Y Lin. Nonlinear interval model control of quasi-keyhole arc welding process.Automatica,2004,40:805-813.
[11] Y M Zhang, Y C Liu. Control of dynamic keyhole welding process. Automatica,2007,43:876-884.
[12] L Zhou, T Lin, S B Chen. Autonomous acquisition of seam coordinates for arc welding robot based onvisual servoing. Journal of Intelligent and Robotic System,2006,47:239-255.
[13] J S Kim, Y T Son, H S Cho, K Koh. A robust method for vision-based seam tracking in robotic arcwelding. Proceeding of the International Symposium on Intelligent Control,1995,363-368.
[14] S B Chen, Y Zhang, T Qiu, T Lin. Robotic welding systems with vision-sensing and self-learningneuron control of arc welding dynamic process. Journal of Intelligent and Robotic Systems,2003,36:191-208.
[15] P Q Xu, X H Tang, S Yao. Application of circular laser vision sensor (CLVS) on welded seam tracking.Journal of Materials Processing Technology,2008,25:404-410.
[16] C G Rafael, E W Richard. Digital image processing.电子工业出版社,北京，2002.
[17]周律,陈善本.打断支线条算法及其在焊缝图像处理中的应用.机械工程学报,2009,45(3):282-285.
[18]李秀忠.小波分析在焊缝图像处理中的应用.焊接技术,2010,39(7):40-43.
[19]任大海，尤政，孙长库，叶声华.焊接缺陷实时成像自动检测技术的研究.清华大学学报(自然科学版),2001,41(2):25-29.
[20] G Pritschow，S Mueller，H Horber. Fast and robust image processing for laser stripe sensors in arcwelding automation. Proceeding of the IEEE International Symposium on Industrial Electronics,2002,2:651-656.
[21]舒新宇，王国荣，刘苏宜.一种基于数学形态学的焊缝图像处理方法.电焊机,2006,36(3):48-51.
[22]许敏，赵明扬，邹媛媛.不等厚激光拼板焊缝质量检测图像处理方法.焊接技术,2010,39(4):56-61.
[23] Z J Fang，D Xu，M Tan. Visual seam tracking system for butt weld of thin plate. International Journal ofAdvanced Manufacturing Technology,2010,49:519-526.
[24]朱虹.数字图像处理基础.科学出版社,北京,2005.
[25]李国进，王国荣，钟继光，石永华.水下焊缝自动跟踪路径的识别.焊接学报,2005,26(3):58-62.
[26]鄢治国，徐德，李原.一种基于自适应二值化阈值的焊缝边缘特征提取方法.焊接学报,2008,29(7):34-38.
[27] J Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis andMachine Intelligence,1986,8(6):679-698.
[28] J Canny. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Manand Cybernetics,1979,9(1):62-66.
[29] M A Fischler, R C Bolles. Random sample consensus: a paradigm for model fitting with applications toimage analysis and automated cartography. Graphics and Image Processing,1981,24(6):381-395.
[30] T Y Zhang, C Y Suen. A fast parallel algorithm for thinning digital patterns. Communications of theACM,1984,27(3):236-239.
[31] Y H Shi, G R Wang, G J Li. Adaptive robotic welding system using laser vision sensing for underwaterengineering. Proceeding of IEEE International Conference on Control and Automation,2007, Beijing,China, pp.1213-1218.
[32] J Wu, J S Smith, J Lucas. Weld bead placement system for multipass welding. Proceeding of the IEEEInternational Conference on Science, Measurement and Technology,1996,143(2):85-90.
[33] Z J Fang, D Xu. Image-based visual seam tracking system for fillet joint. Proceeding of the IEEEInternational Conference on Robotics and Biomimetics,2009, Guilin, China, pp.1230-1235.
[34]李原，徐德，沈扬，谭民.一种焊缝结构光图像处理与特征提取方法.焊接学报,2006,27(9):25-30.
[35]喻宁娜，莫胜撼，钟义广，戴建树.基于结构光的V性焊缝图像实时处理.电焊机,2010,40(3):60-63.
[36] C G Fan, F L Lv, S B Chen. A visual sensing system for welding control and seam tracking in aluminumalloy gas tungsten arc welding. Proceeding of the33rd Annual Conference of the IEEE IndustrialElectronics Society,2007, Taipei, Taiwan, pp.2700-2705.
[37] H Y Shen, T Lin, S B Chen. A study on vision-based real-time seam tracking in robotic arc welding.Lecture Notes in Control and Information Sciences,2007,362:311-318.
[38] S K Pal, R A King. On edge detection of X-ray images using fuzzy sets. IEEE Transactions on PatternAnalysis and Machine Intelligence,1983,5(1):69-77.
[39] F Russo, G Ramponi. A fuzzy operator for the enhancement of blurred and noisy images.IEEETransactions on Image Processing,1995,4(8):1169-1174.
[40] T Law, H Itoh, H Seki. Image filtering, edge detection, and edge tracing using fuzzy reasoning. IEEETransactions on Pattern Analysis and Machine Intelligence,1996,18(5):481-491.
[41]高顶，刘云祥，张晓光.基于广义模糊算子的工业RT图像增强.华东理工大学学报(自然科学版),2006,32(3):332-335.
[42]崔桐，韩峰，张双娟.基于多层次模糊增强的自适应Canny边缘检测算法.内蒙古工业大学学报,2006,25(1):71-75.
[43] M Y Kim，K W Ko，H S Cho, J H Kim. Visual sensing and recognition of welding environment forintelligent shipyard welding robots. Proceeding of the IEEE/RSJ International Conference on IntelligentRobots and Systems,2000,3:2159-2165.
[44] J Peng，Q Chen，J Lu, J Jin, C A Luttervelt. Real time optimization of robotic arc welding based onmachine vision and neural networks. Proceeding of the33rd Annual Conference of the IEEE IndustrialElectronics Society,2007, Taipei, Taiwan, pp.1279-1284.
[45]岳宏，孙立新，李慨，蔡鹤皋.基于自适应共振人工神经网络的焊缝类型自动识别方法.中国机械工程,1999,10(8):1-7.
[46]林尚扬,杜兵,焊接行业现状与自主创新战略[J],焊接,2006, No.6, pp.9-15.
[47]张雷,赵明扬,邹援援,高世一,焊缝图像处理技术在激光拼焊中的应用[J],仪器仪表学报,2006,Vol.27, No.6, pp.2196-2197.
[48] J. M. Tsai, H. W. Leea, N.J. Annb, Machine vision based path planning for a robotic golf club headwelding system[J], Robotics and Computer-Integrated Manufacturing,2011, Vol.27, pp.843–849.
[49] D. Xu, M. Tan, X. Zhao, Z. Tu, Seam Tracking and Visual Control for Robotic Arc Welding Based OnStructured Light Stereovision[J], International Journal of Automation and Computing,2004, Vol.1, No.1pp.63-75.
[50] A. Matsushita, T. Hokari, S. Kaneko,etc, Image Measurement on Curvature Around Seam Line forMotion Control of Laser Welding Robot[J], International Journal of Automation Technology,2010, Vol.4, No.6, pp.536-541.
[51]陈善本,林涛,智能化焊接机器人技术[M],北京：机械工业出版社,2006.
[52]张广军,冷孝宇,吴林,弧焊机器人结构光视觉传感焊缝跟踪[J],焊接学报,2008, Vol.29, No.9, pp.8-10.
[53] W. Z. Zhang, Q. Chen, G. X. Zhang, Z. G. Sun and D. Du, Seam Tracking of Articulated Robot forLaser Welding Based on Visual Feedback Control[C], Robotic Welding Intelligence and Automation,Lecture Notes in Control and Information Sciences,2007, Vol.362, pp.281-287.
[54] Z. J. Fang, D. Xu, M. Tan, A Vision-Based Self-Tuning Fuzzy Controller for Fillet Weld SeamTracking[J], IEEE/ASME Transactions on Mechatronics,2011,Vol.16, No.3, pp.540–550.
[55]高向东,仲训杲,游德勇,Katayama Seiji,色噪声下卡尔曼滤波焊缝跟踪算法与试验研究,控制理论与应用,2011,Vol.28, No.7, pp.931-935.
[56] A. F. Villan, R. G. Acevedo, E. A. Alvarez; Low-Cost System for Weld Tracking Based on ArtificialVision[J], IEEE Transactions on Industry Applications,2011, Vol.47, No.3, pp.1159-1167.
[57] J. Nelson, A. B. Orozco, B. R. Paul, Real-time control of laser-hybrid welding using weld qualityattributes[C], Proceedings of the23rd International Congress on Applications of Lasers andEletro-Optics, San Franciso, Califonia, Ocotober4-7,2004, pp.126-132.
[58] G. H. Ma, J. Qin, F. R. Jiang and H. B. Wang, Depth Extraction by Simplified Binocular Vision[J],Lecture Notes in Electrical Engineering,2011, Vol.88, No.2, pp.179-183.
[59] S. A. Hutchinson, G. D. Hager, and P. I. Corke, A tutorial on visual servo control [J]. IEEE Transactionon Robotics and Automation,1996, Vol.12, No.5, pp.651--670.
[60] K. Umer, J. Ibrar, I. Naeem, D. Jian, Uncalibrated eye-in-hand visual servoing: an LMI approach [J],Industrial Robot: An International Journal, Vol.38, No.2,2011, pp.130-138.
[61] S. B. Lin, X. B. Fu, C. L. Fan, C. L. Yang and L. Luo, Development of an automatic weld surfaceappearance inspection system using machine vision[J], China Welding,2009, Vol.18, No.3, pp.74-80.
[62] W. M. Steen, J. Mazumder, Laser Material Processing[M], London:Springer-Verlag London Limited,2010, pp.240-241.
[63] T. Kim, S. Lee, S. Baek, K. Boo, A study on the welding seam tracking by using Laser vision sensor[C],international Conference on control, Aotomation and Systems, KINTEX, Gyeonggi-do, Korea,2010,pp.779-782.
[64] Y. Li, Y. F. Li, Q. L. Wang, D. Xu, M. Tan, Measurement and Defect Detection of the Weld Bead Basedon Online Vision Inspection[J], IEEE Transactions on Instrumentation and Measurement,2010, Vol.59,No.7, pp.1841-1849.
[65] M. Graaf, R. Aartsa, B. Jonkera and J. Meijera. Real-time seam tracking for robotic laser welding usingtrajectory-based control[J], Control Engineering Practice,2010, Vol.18, No.8, pp.1-10.
[66] R. A. White, J. S. Smith, J. Lucas, Vision-based gauge for online weld profile metrology[C], IEEProceedings of Science, Measurement and Technology,1994, Vol.141, No.6, pp.521-526.
[67] C. Reichert, Pre-and Post-weld inspection using laser vision[C], Proceedings of NondestructiveEvaluation of Materials and Composites II, San Antonio, United States,1998, pp.244-254.
[68] J. Lee, P. Im, Y. Park, J. Kim, Welding bead and chamfer inspection by means of laser vision[C],Proceedings of Optomechatronic Systems, Boston, USA,2001, Vol.4190, pp.41-50.
[69] Z. X. Pan, J. Polden, N. Larkin, S. V. Duin,J. Norrish, Recent progress on programming methods forindustrial robots[J], Robotics and Computer-Integrated Manufacturing,2012, Vol.28, No.2, pp.87–94.
[70] M. Dinham, F. Gu, Low cost simultaneous calibration of a stereo vision system and a welding robot[C],2010IEEE International Conference on Robotics and Biomimetics (ROBIO),2010, pp.1452–1456.
[71]高向东,莫玲,仲训杲,游德勇,Katayama Seiji,大功率光纤激光焊焊缝跟踪偏差红外检测方法,物理学报,2011, Vol.60, No.8, pp.1-8.
[72]陈海永,孙鹤旭,徐德,一类窄焊缝的结构光图像特征提取方法[J]，焊接学报，2012, Vol.33, No.1,pp.61-65.
[73] M. zcan, Determining seam profile tracking of very narrow butt welding on sheet metal [J], ScientificResearch and Essays,2011, Vol.6, No.23, pp.5040-5048.
[74] G. Allibert, E. Courtial, What can prediction bring to image-based visual servoing[J], in Proc. IEEE/RSJInt. Conf. Intell. Robots Syst., St.Louis, MO,2009, pp.5210–5215.
[75] Y. B. Deng, H. Zhang, G. H. Ma, Predictive Contro Simulation on Binocular Visual Servo SeamTracking System[J], Electrical Engineering and Control, Lecture Notes in Electrical Engineering,2011,Vol.98, pp.461-468.
[76] F. Chaumette, S. Hutchinson, Visual Servo Control, Part I: Basic Approaches [J]. IEEE Robotics andAutomation Magazine,2006, Vol.13, No.4, pp.82-90.
[77] F. Chaumette, S. Hutchinson, Visual Servo Control, Part II: Advanced Approaches [J]. IEEE Roboticsand Automation Magazine,2007, Vol.14, No.1, pp.109-118.
[78] E. Malis, Y. Mezouar, P. Rives, Robustness of Image-Based Visual Servoing With a Calibrated Camerain the Presence of Uncertainties in the Three-Dimensional Structure [J], IEEE Transactions onRobotics,2010,Vol.26, No.1, pp.112-120.
[79] G Allibert, E. Courtial, F. Chaumette, Predictive control for constrained image-based visual servoing [J],IEEE Transactions on Robotics,2010,Vol.26, No.5, pp.933-939.
[80] N. Garcia-Aracil, E. Malis, R.Aracil-Santonja, and C. Pérez-Vidal, Continuous visual servoing despitethe changes of visibility in image features [J], IEEE Transactions on Robotic, Vol.21, No.6, pp.1214–1220,2005.
[81]陈彦宾，编著，现代激光焊接技术，科学出版社
[82]陈武柱，著.激光焊接与切割质量控制，机械工业出版社
[83]李原,徐德,沈扬,等.一种焊缝结构光图像处理与特征提取方法[J],焊接学报,2006,27(9):25-31.
[84]胡斌,李德华,金刚,等.基于方向模板的结构光条纹中心检测方法[J].计算机工程与应用，2002,38(11):59-60.
[85]李和平,李德华,朱洲,等.基于遗传算法的结构光条纹中心的检测方法[J].光学精密工程,2004,12(1):82-87.
[86] Wei Huang, Radovan Kovacevic. A Laser-Based Vision System for Weld Quality Inspection [J]，Sensors2011,11:506-521.
[87] White R A, Smith J S, Lucas J. Vision-based Gauge for Online Weld Profile Metrology [C], IEEProceedings-Science, Measurement and Technology,1994,141(6):521-526.
[88] J.Noruk. Vision Weld Inspection Enters the New Millennium [J], Sensor Review,2001,21(4):278-282.
[89]伏喜斌,林三宝,杨春利,等.基于激光视觉传感的角焊缝外形尺寸检测[J],焊接学报,2008,29(7),47-50.
[90] J.Lee, P.Im,Y, Park,J, Kim.J. Welding Bead and Chamfer Inspection by means of Laser Vision[J],Society of Photo-Optical Instrumentation Engineers,2001:41-50.
[91]伏喜斌,林三宝,杨春利.激光技术在焊接质量控制和检测中的应用[J],焊接,2006(10):24-29.
[92] M.Geiger, Christian K. Michael Schmidt High-power laser welding of contaminated steel sheets Prod [J],Eng. Res. Devel,2008,2:235-240.
[93]孙宏琦,施维颖,巨永锋.利用中值滤波进行图像处理[J],长安大学学报,2003,23(2):104-106.
[94]李久芳.基于二值化的边缘图像滤波方法[J].电子工业专用设备,2010,(181):25-27.
[95]马艳娥,陈思,陈娟,等.基于多幅图像平均法对数字图像降噪的研究[J],电子测试,2011(6):1-3.
[96]陈海永,徐德,王宏.三维曲线焊缝两种建模方法的比较[J],焊接学报,2008,29(6):41-44.
[96] Haiyong chen, De xu, Hong wang.Three Dimension Curve Welding Seam Modeling for Seam Tracking[C],17th IFAC world congress, korea,2008:302-325.
[97]党林涛.基于激光检测的三维特征区分和提取研究[D],南京:南京航空航天大学,2011.
[98]王俊龙,曲兴华,赵阳.多目视觉检测技术中的照明系统设计[J],光电技术应用,2009,24(4):1-5.
[99]张洪涛.钢板表面缺陷在线视觉检测系统关键技术研究[D].天津:天津大学,2008.
[100]廖强,刘兆东,郭静,等.主动视觉技术在精密测量中的应用研究[J],计算机工程与应用,2009,45(10):218-220.
[101]党林涛.基于激光检测的三维特征区分和提取研究[D],南京:南京航空航天大学,2011.
[102]胡琳. CCD图像传感器的现状及未来发展[J],电子科技,2010,23(6):82-85.
[103]宋勇,郝群等.CMOS图像传感器与CCD的比较及发展现状[J],仪器仪表学报,2001(3)增刊:387-389.
[104]陈天飞.线结构光传感器结构优化设计及其摄像机标定技术[D],大连,大连海事大学,2010.
[105]张广军.视觉测量[M],北京,科学出版社,2008.
[106]张晓光，李浴，徐健健.基于神经网络的焊缝缺陷识别专家系统.计算机工程，2003,29(17):22-23,28
[107] Ioannis Valavanis, Dimitrios Kosmopoulos. Multiclass defect detection and classification in weldradiographic images using geometric and texture features, Expert Systems with Applications,2010,37(12):7606-7614
[108]许敏，赵明扬，邹媛媛.不等厚激光拼焊板焊缝质量检测图像处理方法.焊接技术,2010,39(4):56-61
[109]申清明，高建民，李成.焊缝缺陷类型识别方法的研究，西安交通大学学报.2010,44(7):100-103.
[110]高岚,胡亮,罗乐,田凯伟.基于BP神经网络的船舶焊缝缺陷图像识别.武汉理工大学学报,2012,34(3):271-274.
[111]张万江,许敏.基于结构光视觉的激光拼焊焊缝背面质量检测方法研究.仪表技术与传感器,2010,9:84-87.
[112]张万江,许敏.基于结构光视觉的激光拼焊焊缝质量检测方法研究.机械设计与制造,2011,3:156-258
[113]陈方林,刘彦.基于支持向量机的X射线焊缝缺陷检测,机械工程与自动化,2010,2:122-123,126
[114]彭文季,罗兴锜,李福松,陈志敏.基于频谱法和带偏差单元递归神经网络的水电机组振动故障诊断,机械科学与技术,2006,25(11):1281-1284
[115]唐富华,郭银景,杨阳等.一种改进的递归神经网络及其仿真研究.北京理工大学学报,2005,25(5):399-301
[116]高世一,吴瑞珉,陈卫东,杨赛丹,乔俊良.激光焊接过程监测及焊缝质量检测技术研究现状.世界钢铁,2010,3:51-54,63
[2]周武,胡跃明.视基于相位相关和重采样的亚像素图像匹配算法.华南理工大学学报(自然科学版),2010,38(10):68-78.
[3]胡坤,周富强，张广军.一种快速结构光条纹中心亚像素精度提取方法.仪器仪表学报,2006,27(10):1326-1329.
[4]李金泉,王建伟，陈善本，吴林.一种改进的Zernike正交矩亚像素边缘检测算法.光学技术,2003,29(4):500-503.
[5] H B Chen, T Lin, S B Chen, J F Wang, J Q Jia, H Zhang. Adaptive control on wire feeding in robot arcwelding system. Proceeding of the IEEE International Conference on Robotics, Automation andMechatronics,2008, Chengdu, China, pp.119-122.
[6] P Koseeyaporn, G E Cook, A M Strauss. Adaptive voltage control in fusion arc welding. IEEETransactions on Industry Applications,2000,36(5):1300-1307.
[7] D V Nishar, J L Schiano, W R Perkins. Adaptive control of temperature in arc welding. IEEE ControlSystems Magazine,1994,14(4):4-12.
[8] G H Kim, S I Kang, S B Lee. A study on the estimate of weld bead shape and the compensation ofwelding parameters by considering weld defects in horizontal fillet welding. Proceeding of the ThirdInternational Conference on Knowledge-Based Intelligent Information Engineering Systems,1999,Adelaide, Australia, pp.212-216.
[9] Y M Zhang, R Kovacevic, L Li. Adaptive control of full penetration gas tungsten arc welding. IEEETransactions on Control Systems Technology,1996,4(4):394-403.
[10] W Lu, Y M Zhang, W Y Lin. Nonlinear interval model control of quasi-keyhole arc welding process.Automatica,2004,40:805-813.
[11] Y M Zhang, Y C Liu. Control of dynamic keyhole welding process. Automatica,2007,43:876-884.
[12] L Zhou, T Lin, S B Chen. Autonomous acquisition of seam coordinates for arc welding robot based onvisual servoing. Journal of Intelligent and Robotic System,2006,47:239-255.
[13] J S Kim, Y T Son, H S Cho, K Koh. A robust method for vision-based seam tracking in robotic arcwelding. Proceeding of the International Symposium on Intelligent Control,1995,363-368.
[14] S B Chen, Y Zhang, T Qiu, T Lin. Robotic welding systems with vision-sensing and self-learningneuron control of arc welding dynamic process. Journal of Intelligent and Robotic Systems,2003,36:191-208.
[15] P Q Xu, X H Tang, S Yao. Application of circular laser vision sensor (CLVS) on welded seam tracking.Journal of Materials Processing Technology,2008,25:404-410.
[16] C G Rafael, E W Richard. Digital image processing.电子工业出版社,北京，2002.
[17]周律,陈善本.打断支线条算法及其在焊缝图像处理中的应用.机械工程学报,2009,45(3):282-285.
[18]李秀忠.小波分析在焊缝图像处理中的应用.焊接技术,2010,39(7):40-43.
[19]任大海，尤政，孙长库，叶声华.焊接缺陷实时成像自动检测技术的研究.清华大学学报(自然科学版),2001,41(2):25-29.
[20] G Pritschow，S Mueller，H Horber. Fast and robust image processing for laser stripe sensors in arcwelding automation. Proceeding of the IEEE International Symposium on Industrial Electronics,2002,2:651-656.
[21]舒新宇，王国荣，刘苏宜.一种基于数学形态学的焊缝图像处理方法.电焊机,2006,36(3):48-51.
[22]许敏，赵明扬，邹媛媛.不等厚激光拼板焊缝质量检测图像处理方法.焊接技术,2010,39(4):56-61.
[23] Z J Fang，D Xu，M Tan. Visual seam tracking system for butt weld of thin plate. International Journal ofAdvanced Manufacturing Technology,2010,49:519-526.
[24]朱虹.数字图像处理基础.科学出版社,北京,2005.
[25]李国进，王国荣，钟继光，石永华.水下焊缝自动跟踪路径的识别.焊接学报,2005,26(3):58-62.
[26]鄢治国，徐德，李原.一种基于自适应二值化阈值的焊缝边缘特征提取方法.焊接学报,2008,29(7):34-38.
[27] J Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis andMachine Intelligence,1986,8(6):679-698.
[28] J Canny. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Manand Cybernetics,1979,9(1):62-66.
[29] M A Fischler, R C Bolles. Random sample consensus: a paradigm for model fitting with applications toimage analysis and automated cartography. Graphics and Image Processing,1981,24(6):381-395.
[30] T Y Zhang, C Y Suen. A fast parallel algorithm for thinning digital patterns. Communications of theACM,1984,27(3):236-239.
[31] Y H Shi, G R Wang, G J Li. Adaptive robotic welding system using laser vision sensing for underwaterengineering. Proceeding of IEEE International Conference on Control and Automation,2007, Beijing,China, pp.1213-1218.
[32] J Wu, J S Smith, J Lucas. Weld bead placement system for multipass welding. Proceeding of the IEEEInternational Conference on Science, Measurement and Technology,1996,143(2):85-90.
[33] Z J Fang, D Xu. Image-based visual seam tracking system for fillet joint. Proceeding of the IEEEInternational Conference on Robotics and Biomimetics,2009, Guilin, China, pp.1230-1235.
[34]李原，徐德，沈扬，谭民.一种焊缝结构光图像处理与特征提取方法.焊接学报,2006,27(9):25-30.
[35]喻宁娜，莫胜撼，钟义广，戴建树.基于结构光的V性焊缝图像实时处理.电焊机,2010,40(3):60-63.
[36] C G Fan, F L Lv, S B Chen. A visual sensing system for welding control and seam tracking in aluminumalloy gas tungsten arc welding. Proceeding of the33rd Annual Conference of the IEEE IndustrialElectronics Society,2007, Taipei, Taiwan, pp.2700-2705.
[37] H Y Shen, T Lin, S B Chen. A study on vision-based real-time seam tracking in robotic arc welding.Lecture Notes in Control and Information Sciences,2007,362:311-318.
[38] S K Pal, R A King. On edge detection of X-ray images using fuzzy sets. IEEE Transactions on PatternAnalysis and Machine Intelligence,1983,5(1):69-77.
[39] F Russo, G Ramponi. A fuzzy operator for the enhancement of blurred and noisy images.IEEETransactions on Image Processing,1995,4(8):1169-1174.
[40] T Law, H Itoh, H Seki. Image filtering, edge detection, and edge tracing using fuzzy reasoning. IEEETransactions on Pattern Analysis and Machine Intelligence,1996,18(5):481-491.
[41]高顶，刘云祥，张晓光.基于广义模糊算子的工业RT图像增强.华东理工大学学报(自然科学版),2006,32(3):332-335.
[42]崔桐，韩峰，张双娟.基于多层次模糊增强的自适应Canny边缘检测算法.内蒙古工业大学学报,2006,25(1):71-75.
[43] M Y Kim，K W Ko，H S Cho, J H Kim. Visual sensing and recognition of welding environment forintelligent shipyard welding robots. Proceeding of the IEEE/RSJ International Conference on IntelligentRobots and Systems,2000,3:2159-2165.
[44] J Peng，Q Chen，J Lu, J Jin, C A Luttervelt. Real time optimization of robotic arc welding based onmachine vision and neural networks. Proceeding of the33rd Annual Conference of the IEEE IndustrialElectronics Society,2007, Taipei, Taiwan, pp.1279-1284.
[45]岳宏，孙立新，李慨，蔡鹤皋.基于自适应共振人工神经网络的焊缝类型自动识别方法.中国机械工程,1999,10(8):1-7.
[46]林尚扬,杜兵,焊接行业现状与自主创新战略[J],焊接,2006, No.6, pp.9-15.
[47]张雷,赵明扬,邹援援,高世一,焊缝图像处理技术在激光拼焊中的应用[J],仪器仪表学报,2006,Vol.27, No.6, pp.2196-2197.
[48] J. M. Tsai, H. W. Leea, N.J. Annb, Machine vision based path planning for a robotic golf club headwelding system[J], Robotics and Computer-Integrated Manufacturing,2011, Vol.27, pp.843–849.
[49] D. Xu, M. Tan, X. Zhao, Z. Tu, Seam Tracking and Visual Control for Robotic Arc Welding Based OnStructured Light Stereovision[J], International Journal of Automation and Computing,2004, Vol.1, No.1pp.63-75.
[50] A. Matsushita, T. Hokari, S. Kaneko,etc, Image Measurement on Curvature Around Seam Line forMotion Control of Laser Welding Robot[J], International Journal of Automation Technology,2010, Vol.4, No.6, pp.536-541.
[51]陈善本,林涛,智能化焊接机器人技术[M],北京：机械工业出版社,2006.
[52]张广军,冷孝宇,吴林,弧焊机器人结构光视觉传感焊缝跟踪[J],焊接学报,2008, Vol.29, No.9, pp.8-10.
[53] W. Z. Zhang, Q. Chen, G. X. Zhang, Z. G. Sun and D. Du, Seam Tracking of Articulated Robot forLaser Welding Based on Visual Feedback Control[C], Robotic Welding Intelligence and Automation,Lecture Notes in Control and Information Sciences,2007, Vol.362, pp.281-287.
[54] Z. J. Fang, D. Xu, M. Tan, A Vision-Based Self-Tuning Fuzzy Controller for Fillet Weld SeamTracking[J], IEEE/ASME Transactions on Mechatronics,2011,Vol.16, No.3, pp.540–550.
[55]高向东,仲训杲,游德勇,Katayama Seiji,色噪声下卡尔曼滤波焊缝跟踪算法与试验研究,控制理论与应用,2011,Vol.28, No.7, pp.931-935.
[56] A. F. Villan, R. G. Acevedo, E. A. Alvarez; Low-Cost System for Weld Tracking Based on ArtificialVision[J], IEEE Transactions on Industry Applications,2011, Vol.47, No.3, pp.1159-1167.
[57] J. Nelson, A. B. Orozco, B. R. Paul, Real-time control of laser-hybrid welding using weld qualityattributes[C], Proceedings of the23rd International Congress on Applications of Lasers andEletro-Optics, San Franciso, Califonia, Ocotober4-7,2004, pp.126-132.
[58] G. H. Ma, J. Qin, F. R. Jiang and H. B. Wang, Depth Extraction by Simplified Binocular Vision[J],Lecture Notes in Electrical Engineering,2011, Vol.88, No.2, pp.179-183.
[59] S. A. Hutchinson, G. D. Hager, and P. I. Corke, A tutorial on visual servo control [J]. IEEE Transactionon Robotics and Automation,1996, Vol.12, No.5, pp.651--670.
[60] K. Umer, J. Ibrar, I. Naeem, D. Jian, Uncalibrated eye-in-hand visual servoing: an LMI approach [J],Industrial Robot: An International Journal, Vol.38, No.2,2011, pp.130-138.
[61] S. B. Lin, X. B. Fu, C. L. Fan, C. L. Yang and L. Luo, Development of an automatic weld surfaceappearance inspection system using machine vision[J], China Welding,2009, Vol.18, No.3, pp.74-80.
[62] W. M. Steen, J. Mazumder, Laser Material Processing[M], London:Springer-Verlag London Limited,2010, pp.240-241.
[63] T. Kim, S. Lee, S. Baek, K. Boo, A study on the welding seam tracking by using Laser vision sensor[C],international Conference on control, Aotomation and Systems, KINTEX, Gyeonggi-do, Korea,2010,pp.779-782.
[64] Y. Li, Y. F. Li, Q. L. Wang, D. Xu, M. Tan, Measurement and Defect Detection of the Weld Bead Basedon Online Vision Inspection[J], IEEE Transactions on Instrumentation and Measurement,2010, Vol.59,No.7, pp.1841-1849.
[65] M. Graaf, R. Aartsa, B. Jonkera and J. Meijera. Real-time seam tracking for robotic laser welding usingtrajectory-based control[J], Control Engineering Practice,2010, Vol.18, No.8, pp.1-10.
[66] R. A. White, J. S. Smith, J. Lucas, Vision-based gauge for online weld profile metrology[C], IEEProceedings of Science, Measurement and Technology,1994, Vol.141, No.6, pp.521-526.
[67] C. Reichert, Pre-and Post-weld inspection using laser vision[C], Proceedings of NondestructiveEvaluation of Materials and Composites II, San Antonio, United States,1998, pp.244-254.
[68] J. Lee, P. Im, Y. Park, J. Kim, Welding bead and chamfer inspection by means of laser vision[C],Proceedings of Optomechatronic Systems, Boston, USA,2001, Vol.4190, pp.41-50.
[69] Z. X. Pan, J. Polden, N. Larkin, S. V. Duin,J. Norrish, Recent progress on programming methods forindustrial robots[J], Robotics and Computer-Integrated Manufacturing,2012, Vol.28, No.2, pp.87–94.
[70] M. Dinham, F. Gu, Low cost simultaneous calibration of a stereo vision system and a welding robot[C],2010IEEE International Conference on Robotics and Biomimetics (ROBIO),2010, pp.1452–1456.
[71]高向东,莫玲,仲训杲,游德勇,Katayama Seiji,大功率光纤激光焊焊缝跟踪偏差红外检测方法,物理学报,2011, Vol.60, No.8, pp.1-8.
[72]陈海永,孙鹤旭,徐德,一类窄焊缝的结构光图像特征提取方法[J]，焊接学报，2012, Vol.33, No.1,pp.61-65.
[73] M. zcan, Determining seam profile tracking of very narrow butt welding on sheet metal [J], ScientificResearch and Essays,2011, Vol.6, No.23, pp.5040-5048.
[74] G. Allibert, E. Courtial, What can prediction bring to image-based visual servoing[J], in Proc. IEEE/RSJInt. Conf. Intell. Robots Syst., St.Louis, MO,2009, pp.5210–5215.
[75] Y. B. Deng, H. Zhang, G. H. Ma, Predictive Contro Simulation on Binocular Visual Servo SeamTracking System[J], Electrical Engineering and Control, Lecture Notes in Electrical Engineering,2011,Vol.98, pp.461-468.
[76] F. Chaumette, S. Hutchinson, Visual Servo Control, Part I: Basic Approaches [J]. IEEE Robotics andAutomation Magazine,2006, Vol.13, No.4, pp.82-90.
[77] F. Chaumette, S. Hutchinson, Visual Servo Control, Part II: Advanced Approaches [J]. IEEE Roboticsand Automation Magazine,2007, Vol.14, No.1, pp.109-118.
[78] E. Malis, Y. Mezouar, P. Rives, Robustness of Image-Based Visual Servoing With a Calibrated Camerain the Presence of Uncertainties in the Three-Dimensional Structure [J], IEEE Transactions onRobotics,2010,Vol.26, No.1, pp.112-120.
[79] G Allibert, E. Courtial, F. Chaumette, Predictive control for constrained image-based visual servoing [J],IEEE Transactions on Robotics,2010,Vol.26, No.5, pp.933-939.
[80] N. Garcia-Aracil, E. Malis, R.Aracil-Santonja, and C. Pérez-Vidal, Continuous visual servoing despitethe changes of visibility in image features [J], IEEE Transactions on Robotic, Vol.21, No.6, pp.1214–1220,2005.
[81]陈彦宾，编著，现代激光焊接技术，科学出版社
[82]陈武柱，著.激光焊接与切割质量控制，机械工业出版社
[83]李原,徐德,沈扬,等.一种焊缝结构光图像处理与特征提取方法[J],焊接学报,2006,27(9):25-31.
[84]胡斌,李德华,金刚,等.基于方向模板的结构光条纹中心检测方法[J].计算机工程与应用，2002,38(11):59-60.
[85]李和平,李德华,朱洲,等.基于遗传算法的结构光条纹中心的检测方法[J].光学精密工程,2004,12(1):82-87.
[86] Wei Huang, Radovan Kovacevic. A Laser-Based Vision System for Weld Quality Inspection [J]，Sensors2011,11:506-521.
[87] White R A, Smith J S, Lucas J. Vision-based Gauge for Online Weld Profile Metrology [C], IEEProceedings-Science, Measurement and Technology,1994,141(6):521-526.
[88] J.Noruk. Vision Weld Inspection Enters the New Millennium [J], Sensor Review,2001,21(4):278-282.
[89]伏喜斌,林三宝,杨春利,等.基于激光视觉传感的角焊缝外形尺寸检测[J],焊接学报,2008,29(7),47-50.
[90] J.Lee, P.Im,Y, Park,J, Kim.J. Welding Bead and Chamfer Inspection by means of Laser Vision[J],Society of Photo-Optical Instrumentation Engineers,2001:41-50.
[91]伏喜斌,林三宝,杨春利.激光技术在焊接质量控制和检测中的应用[J],焊接,2006(10):24-29.
[92] M.Geiger, Christian K. Michael Schmidt High-power laser welding of contaminated steel sheets Prod [J],Eng. Res. Devel,2008,2:235-240.
[93]孙宏琦,施维颖,巨永锋.利用中值滤波进行图像处理[J],长安大学学报,2003,23(2):104-106.
[94]李久芳.基于二值化的边缘图像滤波方法[J].电子工业专用设备,2010,(181):25-27.
[95]马艳娥,陈思,陈娟,等.基于多幅图像平均法对数字图像降噪的研究[J],电子测试,2011(6):1-3.
[96]陈海永,徐德,王宏.三维曲线焊缝两种建模方法的比较[J],焊接学报,2008,29(6):41-44.
[96] Haiyong chen, De xu, Hong wang.Three Dimension Curve Welding Seam Modeling for Seam Tracking[C],17th IFAC world congress, korea,2008:302-325.
[97]党林涛.基于激光检测的三维特征区分和提取研究[D],南京:南京航空航天大学,2011.
[98]王俊龙,曲兴华,赵阳.多目视觉检测技术中的照明系统设计[J],光电技术应用,2009,24(4):1-5.
[99]张洪涛.钢板表面缺陷在线视觉检测系统关键技术研究[D].天津:天津大学,2008.
[100]廖强,刘兆东,郭静,等.主动视觉技术在精密测量中的应用研究[J],计算机工程与应用,2009,45(10):218-220.
[101]党林涛.基于激光检测的三维特征区分和提取研究[D],南京:南京航空航天大学,2011.
[102]胡琳. CCD图像传感器的现状及未来发展[J],电子科技,2010,23(6):82-85.
[103]宋勇,郝群等.CMOS图像传感器与CCD的比较及发展现状[J],仪器仪表学报,2001(3)增刊:387-389.
[104]陈天飞.线结构光传感器结构优化设计及其摄像机标定技术[D],大连,大连海事大学,2010.
[105]张广军.视觉测量[M],北京,科学出版社,2008.
[106]张晓光，李浴，徐健健.基于神经网络的焊缝缺陷识别专家系统.计算机工程，2003,29(17):22-23,28
[107] Ioannis Valavanis, Dimitrios Kosmopoulos. Multiclass defect detection and classification in weldradiographic images using geometric and texture features, Expert Systems with Applications,2010,37(12):7606-7614
[108]许敏，赵明扬，邹媛媛.不等厚激光拼焊板焊缝质量检测图像处理方法.焊接技术,2010,39(4):56-61
[109]申清明，高建民，李成.焊缝缺陷类型识别方法的研究，西安交通大学学报.2010,44(7):100-103.
[110]高岚,胡亮,罗乐,田凯伟.基于BP神经网络的船舶焊缝缺陷图像识别.武汉理工大学学报,2012,34(3):271-274.
[111]张万江,许敏.基于结构光视觉的激光拼焊焊缝背面质量检测方法研究.仪表技术与传感器,2010,9:84-87.
[112]张万江,许敏.基于结构光视觉的激光拼焊焊缝质量检测方法研究.机械设计与制造,2011,3:156-258
[113]陈方林,刘彦.基于支持向量机的X射线焊缝缺陷检测,机械工程与自动化,2010,2:122-123,126
[114]彭文季,罗兴锜,李福松,陈志敏.基于频谱法和带偏差单元递归神经网络的水电机组振动故障诊断,机械科学与技术,2006,25(11):1281-1284
[115]唐富华,郭银景,杨阳等.一种改进的递归神经网络及其仿真研究.北京理工大学学报,2005,25(5):399-301
[116]高世一,吴瑞珉,陈卫东,杨赛丹,乔俊良.激光焊接过程监测及焊缝质量检测技术研究现状.世界钢铁,2010,3:51-54,63
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |