运载火箭动力系统五通连接器机器人GTAW质量控制系统
|
摘要
焊接自动化是焊接技术发展的一个趋势,由于现有的“示教再现型”焊接机器人在焊接过程中缺少对外部信息传感反馈和实时调节的功能,不能满足航天高技术产品复杂焊缝精密焊接的要求。焊接过程中的热变形、错边以及焊缝间隙的变化等是不可预知的,这些因素都会直接影响到焊缝成形质量。
本文运用综合分析的手段,找到影响焊接质量的关键因素以及这些因素之间的相互关系并进行量化,对产品的焊接质量进行重点、定量的控制。从焊接变形预测与工艺优化、系统设计、焊接动态过程建模以及焊缝成形实时控制等多个角度实现五通连接器精度焊接成形质量控制。
以非线性有限元技术为基础,运用MSC.Marc软件对运载火箭动力系统五通连接器焊接过程进行了数值模拟,通过调整焊接顺序、改变焊接热输入量获得了五通连接器焊接变形影响规律。在此基础上,进一步优化了焊接工艺和夹具设计。
为了保证焊接过程中的稳定性,必须精确控制焊接热输入量、根据焊接热变形、间隙情况实时调整焊接规范参数。为此,建立了基于视觉传感的弧焊机器人在线质量监控系统,使其能够完成从起弧、工艺参数设定、熔池图像自动采集及图像尺寸计算、焊接规范实时调节以及自动熄弧的完整焊接工作过程。
焊接过程视觉传感是为了实时准确地提取表征熔池形状和大小的特征信息,定义了焊缝正、反面熔宽,焊缝余高、间隙及焊缝位置对熔池形状及运动方向进行了描述。运用DT_CWT+BivaShrink小波降噪、改进的约束最小二乘方法进行对焊缝噪声图像进行降噪恢复和复原,提出了一种适合在焊缝图像中寻找目标区域的高精度自适应阈值分割算法。针对熔池图像特点,采用边界表示与描述算法来获取熔池图像的形状参数,并借助分段多项式拟合的方法对熔池边缘进行了恢复,提取到焊缝正面熔宽尺寸;而对焊缝间隙图像,直接采用面积滤波及Hough变换处理方法获取间隙尺寸信息。最后,运用上述算法对平焊法兰、五通连接器等实际工件的焊接图像进行处理,进一步验证了算法的鲁棒性和实时性。
弧焊焊接过程是被焊金属在电弧热源热输入的作用下,产生局部熔化,形成熔池,最后液态金属凝固之后形成焊缝。根据焊接热过程的这一特点,本文引入非线性Hammerstein模型描述焊接热过程,并建立了IpVf-WbHt之间的关系模型。通过实际焊接过程观测数据与模型实际输出进行了比较,模型精度满足焊接过程控制的要求。针对实际焊接过程中背面熔宽无法实时检测,本文还建立了焊接规范参数同熔池正面参数联合预测熔池反面宽度的RPROP网络动态模型,该算法具有更好的学习效率与泛化能力,预测模型的准确度高于传统分析方法。
焊接过程是一个复杂的、时变的、不确定的过程,本文基于大量工艺试验基础上,提出了送丝速度、焊接电流同焊缝间隙变化量的定量法则。在此基础上,设计了基于Hammerstein模型的Ip-Wb非线性自校正控制器非线性自校正控制器和基于参数预置前馈的复合智能控制器,并进行了仿真和控制器有效性验证试验。焊接峰值电流作为单变量的非线性自校正控制器,能够较好地克服外界干扰、保证熔池反面宽度比较均匀一致。然而,利用送丝速度、焊接电流多变量参数预置前馈复合智能控制器即使在变间隙和变错边的干扰下都能得到焊缝正、反面成形均匀一致的理想焊缝。
通过对平焊法兰、螺旋管的工艺试验,进一步验证了基于参数预置前馈的复合智能控制器的可靠性和稳定性,统计计算结果表明,平焊法兰和螺旋管焊缝背面熔宽和正面余高剩余标准差分别为:(0.84 mm,0.27 mm)和(0.61 mm,0.30mm),符合航天标准要求,并确定了该控制器允许的焊接间隙大小变化范围(0,1.8 mm)。
最后,基于本文研究成果构建的焊接机器人GTAW焊接质量控制系统,对运载火箭动力系统五通连接器进行了焊接。焊后X,Y,Z方向上的变形位移量分别为UX=0.6mm, UY=0.8mm, UZ=-0.3mm,其中Z方向焊接变形较传统手工TIG方法降低了73%。所有焊缝均能满足YS010-97规定的Ⅰ级焊缝标准,产品焊接成形质量得到了极大改进,为进一步工程应用奠定了基础。
Welding automation is a trend of welding technical development. Since the current“teaching and playback type”robot lack external information sensor feedback and the function of real-time adjusting, it could not meet the demands for the precision welding of complicated seam in aerospace high-tech product. Considering the uncertain factors of welding process, such as the welding distortion, alternate edge in the welded seams and the variable quantity of the gap, and they would affect the weld appearance quality directly.
In this paper, several influence factors were analyzed synthetically by systematic method to emphasize in quantitative control of welding quality. Analysis and discussion from different angles, such as the prediction of the weld distortion, welding process optimization, system design, welding dynamic process modeling and weld appearance control, was studied. It is demonstrated that systematic method can meet the precision welding demands.
Based on the non-linear finite element method, the numerical simulation of five-port connector was carried out using MSC.Marc software. The complex models are presented to normalize mechanical boundaries, thermal boundaries and heat source model. Based on the normalized finite element model, the above aerospace structure is calculated again, respectively studying the effects of the welding sequence and welding heat input quantity to welding distortion. On this basis, the welding fixture of the five-port connector was presented for the optimization design.
In order to ensure the welding stability, welding heat input should be controlled precisely. And then the welding parameters could be adjusted according to the welding deformation and the dimension of the weld gap. Therefore, online quality control system for arc welding robot was established. The main functions of this system are listed as follows: arc start operation, welding parameters setting, the welding pool information acquisition and processing, real time adjusting and controlling the main parameters, and arc off operation.
In order to extract the characteristic information of the weld pool accurately, several parameters about weld pool geometry and welding direction are defined. We make de-noising weld image by combining the Dual Tree Complex Transform Wavelet (DT_CWT) with the Biva-Shrink method. A new threshold segmentation algorithm of welding image with self-adaptive capacity and high precision was introduced. Aimed at the characteristic of the degradation weld pool, the shape parameter of the weld pool was obtained using boundary representation and description algorithm. Then, the restoration and geometry of the weld pool was extracted through the piecewise polynomial fitting method. For top-side weld pool image, the dimension information of the weld gap was extracted directly by the area filtering and Hough transform method. At last, plane flange weld and five port connector weld image was processed using above algorithms, and the results were reliable and stable.
Arc welding procedure is the process, in which the welded material under the heat input started melting locally and then form weld pool. According to the characteristic of the weld process, non-linear Hammerstein model was led into the welding process. On this basis, IpVf-WbHt relation model was established. The results show that the model precision can meet the demands of the actual control. Meanwhile, the direct measurement of back-side width of the weld pool is very difficult, so RPROP dynamic prediction model was built to predict the Wb through procedure parameters and top-side information of the weld pool. The result shows that the RPROP algorithm provides both higher learning efficiency and stronger generalization capacity versus traditional method.
The welding process is a complex, time-variant and uncertain system. In this paper, a quantitative rule (about wire feeding rate, welding current and the variable quantity of the gap) is introduced. On this basis, a nonlinear self-corrected controller and the compound intelligent controller with parameter preset was designed and its validation is conformed by Matlab Simulink toolbox. Butt welding experiments were conducted on unequal thickness test plate with varied gap. The results show that nonlinear self-corrected controller with weld current control variable could get better controlled performance.For the requirement of stabilizing backside width and weld reinforcement simultaneously, the compound intelligent controller with parameter preset can stabilize the shape of the weld pool under the conditions of the varied gap and alternate edge.
In order to testify the reliability and stability of the compound intelligent controller in farther, the experiments were conducted on the plane flange and the spiral tube.It has been shown that the residual standard deviation of the back-side weld width and weld reinforcement are 0.84 mm,0.27 mm , 0.61 mm and 0.30mm, and it meets the demands of the space flight standard. Meanwhile, the maxium dimension of the gap is 1.8mm.
In the end, based on the above development of robotic GTAW welding quality control system, the experiments were conducted on the five-port connector. After welding process, the distortion of the X,Y,Z direction are UX=0.6mm, UY=0.8mm, UZ=-0.3mm. The distortion of the Z direction was decreased by 73% than the traditional TIG process. The quality of the weldments met the standard of first-order (according to standard YS010-97).In general, the production quality was inproved increasingly. The work has laid the foundation for the engineering application in further.
本文运用综合分析的手段,找到影响焊接质量的关键因素以及这些因素之间的相互关系并进行量化,对产品的焊接质量进行重点、定量的控制。从焊接变形预测与工艺优化、系统设计、焊接动态过程建模以及焊缝成形实时控制等多个角度实现五通连接器精度焊接成形质量控制。
以非线性有限元技术为基础,运用MSC.Marc软件对运载火箭动力系统五通连接器焊接过程进行了数值模拟,通过调整焊接顺序、改变焊接热输入量获得了五通连接器焊接变形影响规律。在此基础上,进一步优化了焊接工艺和夹具设计。
为了保证焊接过程中的稳定性,必须精确控制焊接热输入量、根据焊接热变形、间隙情况实时调整焊接规范参数。为此,建立了基于视觉传感的弧焊机器人在线质量监控系统,使其能够完成从起弧、工艺参数设定、熔池图像自动采集及图像尺寸计算、焊接规范实时调节以及自动熄弧的完整焊接工作过程。
焊接过程视觉传感是为了实时准确地提取表征熔池形状和大小的特征信息,定义了焊缝正、反面熔宽,焊缝余高、间隙及焊缝位置对熔池形状及运动方向进行了描述。运用DT_CWT+BivaShrink小波降噪、改进的约束最小二乘方法进行对焊缝噪声图像进行降噪恢复和复原,提出了一种适合在焊缝图像中寻找目标区域的高精度自适应阈值分割算法。针对熔池图像特点,采用边界表示与描述算法来获取熔池图像的形状参数,并借助分段多项式拟合的方法对熔池边缘进行了恢复,提取到焊缝正面熔宽尺寸;而对焊缝间隙图像,直接采用面积滤波及Hough变换处理方法获取间隙尺寸信息。最后,运用上述算法对平焊法兰、五通连接器等实际工件的焊接图像进行处理,进一步验证了算法的鲁棒性和实时性。
弧焊焊接过程是被焊金属在电弧热源热输入的作用下,产生局部熔化,形成熔池,最后液态金属凝固之后形成焊缝。根据焊接热过程的这一特点,本文引入非线性Hammerstein模型描述焊接热过程,并建立了IpVf-WbHt之间的关系模型。通过实际焊接过程观测数据与模型实际输出进行了比较,模型精度满足焊接过程控制的要求。针对实际焊接过程中背面熔宽无法实时检测,本文还建立了焊接规范参数同熔池正面参数联合预测熔池反面宽度的RPROP网络动态模型,该算法具有更好的学习效率与泛化能力,预测模型的准确度高于传统分析方法。
焊接过程是一个复杂的、时变的、不确定的过程,本文基于大量工艺试验基础上,提出了送丝速度、焊接电流同焊缝间隙变化量的定量法则。在此基础上,设计了基于Hammerstein模型的Ip-Wb非线性自校正控制器非线性自校正控制器和基于参数预置前馈的复合智能控制器,并进行了仿真和控制器有效性验证试验。焊接峰值电流作为单变量的非线性自校正控制器,能够较好地克服外界干扰、保证熔池反面宽度比较均匀一致。然而,利用送丝速度、焊接电流多变量参数预置前馈复合智能控制器即使在变间隙和变错边的干扰下都能得到焊缝正、反面成形均匀一致的理想焊缝。
通过对平焊法兰、螺旋管的工艺试验,进一步验证了基于参数预置前馈的复合智能控制器的可靠性和稳定性,统计计算结果表明,平焊法兰和螺旋管焊缝背面熔宽和正面余高剩余标准差分别为:(0.84 mm,0.27 mm)和(0.61 mm,0.30mm),符合航天标准要求,并确定了该控制器允许的焊接间隙大小变化范围(0,1.8 mm)。
最后,基于本文研究成果构建的焊接机器人GTAW焊接质量控制系统,对运载火箭动力系统五通连接器进行了焊接。焊后X,Y,Z方向上的变形位移量分别为UX=0.6mm, UY=0.8mm, UZ=-0.3mm,其中Z方向焊接变形较传统手工TIG方法降低了73%。所有焊缝均能满足YS010-97规定的Ⅰ级焊缝标准,产品焊接成形质量得到了极大改进,为进一步工程应用奠定了基础。
Welding automation is a trend of welding technical development. Since the current“teaching and playback type”robot lack external information sensor feedback and the function of real-time adjusting, it could not meet the demands for the precision welding of complicated seam in aerospace high-tech product. Considering the uncertain factors of welding process, such as the welding distortion, alternate edge in the welded seams and the variable quantity of the gap, and they would affect the weld appearance quality directly.
In this paper, several influence factors were analyzed synthetically by systematic method to emphasize in quantitative control of welding quality. Analysis and discussion from different angles, such as the prediction of the weld distortion, welding process optimization, system design, welding dynamic process modeling and weld appearance control, was studied. It is demonstrated that systematic method can meet the precision welding demands.
Based on the non-linear finite element method, the numerical simulation of five-port connector was carried out using MSC.Marc software. The complex models are presented to normalize mechanical boundaries, thermal boundaries and heat source model. Based on the normalized finite element model, the above aerospace structure is calculated again, respectively studying the effects of the welding sequence and welding heat input quantity to welding distortion. On this basis, the welding fixture of the five-port connector was presented for the optimization design.
In order to ensure the welding stability, welding heat input should be controlled precisely. And then the welding parameters could be adjusted according to the welding deformation and the dimension of the weld gap. Therefore, online quality control system for arc welding robot was established. The main functions of this system are listed as follows: arc start operation, welding parameters setting, the welding pool information acquisition and processing, real time adjusting and controlling the main parameters, and arc off operation.
In order to extract the characteristic information of the weld pool accurately, several parameters about weld pool geometry and welding direction are defined. We make de-noising weld image by combining the Dual Tree Complex Transform Wavelet (DT_CWT) with the Biva-Shrink method. A new threshold segmentation algorithm of welding image with self-adaptive capacity and high precision was introduced. Aimed at the characteristic of the degradation weld pool, the shape parameter of the weld pool was obtained using boundary representation and description algorithm. Then, the restoration and geometry of the weld pool was extracted through the piecewise polynomial fitting method. For top-side weld pool image, the dimension information of the weld gap was extracted directly by the area filtering and Hough transform method. At last, plane flange weld and five port connector weld image was processed using above algorithms, and the results were reliable and stable.
Arc welding procedure is the process, in which the welded material under the heat input started melting locally and then form weld pool. According to the characteristic of the weld process, non-linear Hammerstein model was led into the welding process. On this basis, IpVf-WbHt relation model was established. The results show that the model precision can meet the demands of the actual control. Meanwhile, the direct measurement of back-side width of the weld pool is very difficult, so RPROP dynamic prediction model was built to predict the Wb through procedure parameters and top-side information of the weld pool. The result shows that the RPROP algorithm provides both higher learning efficiency and stronger generalization capacity versus traditional method.
The welding process is a complex, time-variant and uncertain system. In this paper, a quantitative rule (about wire feeding rate, welding current and the variable quantity of the gap) is introduced. On this basis, a nonlinear self-corrected controller and the compound intelligent controller with parameter preset was designed and its validation is conformed by Matlab Simulink toolbox. Butt welding experiments were conducted on unequal thickness test plate with varied gap. The results show that nonlinear self-corrected controller with weld current control variable could get better controlled performance.For the requirement of stabilizing backside width and weld reinforcement simultaneously, the compound intelligent controller with parameter preset can stabilize the shape of the weld pool under the conditions of the varied gap and alternate edge.
In order to testify the reliability and stability of the compound intelligent controller in farther, the experiments were conducted on the plane flange and the spiral tube.It has been shown that the residual standard deviation of the back-side weld width and weld reinforcement are 0.84 mm,0.27 mm , 0.61 mm and 0.30mm, and it meets the demands of the space flight standard. Meanwhile, the maxium dimension of the gap is 1.8mm.
In the end, based on the above development of robotic GTAW welding quality control system, the experiments were conducted on the five-port connector. After welding process, the distortion of the X,Y,Z direction are UX=0.6mm, UY=0.8mm, UZ=-0.3mm. The distortion of the Z direction was decreased by 73% than the traditional TIG process. The quality of the weldments met the standard of first-order (according to standard YS010-97).In general, the production quality was inproved increasingly. The work has laid the foundation for the engineering application in further.
引文
1. B.E. Paton,吴林,航空航天焊接技术的发展与未来,航空制造技术,2004,Vol.11(3):37-42.
2.张文钺,迎接21世纪我国焊接技术的新发展,焊接,2000, Vol.7:6-10.
3.范平章,航天运载器推进系统焊接自动化技术,航天工艺,2000,Vol.3:42-48.
4.林尚扬,陈善本,李成桐,焊接机器人及其应用,北京,机械工业出版社,2000.
5.马颂德,计算机视觉,北京,北京科学出版社,1998.
6. Marc Ebner, Andreas Zell, Centering behavior with a mobile robot using monocular foveated vision, Robotics and Autonomous Systems, 2000, Vol.32, 207-218.
7. F.Heimes, H.H.Nagel, Real-time tracking of intersections in image sequences of a moving camera, Engineering Applications of Artificial Intelligence, 1998, 11,215-227.
8. Francois Marmoiton,Francois Collange,Jean Pierre Derutin,Location and relative speed estimation of vehicles by monocular vision, Proceedings of the IEEE Intelligent Vehicles Symposium,2000,Dearborn USA:227-232.
9. Kai.O.Arras, Nicola Tomatis, Roland Siegwart, Multi-sensor On-the-Fly Localization Using Laser and Vision, Proceedings of the 2000 IEEE/RS International Conference on intelligent Robots and Systems: 462-467.
10.张业鹏,何涛,机器视觉在工业测量中的应用与研究,光学精密工程,2001,Vol.9(4):324-330.
11.应义斌,傅宾忠,蒋亦元等,机器视觉技术在农业生产自动化中的应用,农业工程学报,1999,Vol.15(3): 33-37.
12. Min-Fan Ricky Lee,Clarence W.de Silva,Elizabeth A.Croft,etc.Machine vision system for curved surface inspection,Machine Vision and Applications,2000(12): 177-188.
13. Ramesh Jain,Rangachar Kasturi,Brian G.Schunck,机器视觉(英文版),北京,机械工业出版社,2003,76-86.
14.王耀男,李树涛,毛建旭,计算机图像处理与识别技术,北京,高等教育出版社,2001.
15. Kenneth R.Castleman著,朱志刚,林学訚,石定机等译,数字图像处理(第二版),北京,电子工业出版社,2002.
16.陈志翔,J.P.Boillot,先进激光视觉传感技术及其在焊接中的应用,机械工人,2007,Vol.6:37-41.
17.何方殿,王克争,苏勇,视觉传感器焊接跟踪系统的研究和发展,电焊机,1993,Vol.4: 8-13.
18.廖宝剑,吴世德,潘际銮,CO2气体保护焊的电弧传感自动跟踪,见第八次全国焊接会议论文集,北京,机械工业出版社,1997,第3册,161-163.
19.蒋鹏飞,Joho.D.Wood,用声发射传感器的焊缝跟踪控制研究,电焊机,1994, Vol.4: 4-8.
20.黄石生,钱迎雪,基于ART人工神经网络的焊缝跟踪检测算法,机械工程学报,1994,Vol.2: 93-97.
21.胡绳荪,Fuzzy-P控制超声波传感埋弧焊焊缝的跟踪系统,焊接学报,1998, Vol.2: 104-109.
22.蔡志勇,基于视觉的焊缝识别与其DSP实现[博士学位论文],南昌,南昌大学,2004.
23.王军波,孙振国,陈强等,基于CCD传感器的球罐焊接机器人焊缝跟踪,焊接学报,2001,Vol.22(2):31-34.
24. Y Suga, M Narus, T Tkiwa, Application of neural network to visual sensing of welding and automatic tracking in robot welding in the world, 1994, Vol.34:225-282.
25. Min Young Kim,Kuk-won KO,Hyung Suck Cho,etc.Visual Sensing and Recognition of Welding Environment for Intelligent Shipyard Welding Robots,in Proceedings of the 2000 /EEE/RSJ international Conference on Intelligent Robots and Systems,2159-2165.
26. Shigetomo Matsui,Gokhan Goktug,Slit laser sensor guided real-time seam tracking arc welding robot system for non-uniform joint gaps,Industrial Technology,IEEE ICIT'02,2002,159-162.
27. Huang Nan,Abbott M.G.,Beattie,R.J.,Approaches to low level image processing for vision guided seam tracking systems,Pattern Recognition,1988,9th International Conference,Vol.1, 601-603.
28.林尚扬,从第13届埃森焊接展览会看焊接技术的走向,中国机械工程,1994, Vol.3: 67-69.
29.王建军,铝合金脉冲TIG焊熔池动态特征的视觉信息获取与自适应控制[博士学位论文],上海,上海交通大学,2003.
30. J.Wu, J.S.Smsith, J.Lucas, Weld bead placement system for multipass welding, Science, Measurement and Technology, IEE Proceedings, 1996, Vol.14 (2): 85-90.
31. L.Kreft, W.Scheller, Arc welding seam tracking system based on artificial neural networks Intelligent Systems Engineering, 1994, Second International Conference on Systems Engineering, 177-182.
32. Chen S.B.,Chen X.Z.,Qiu T.,etc.Acquisition of weld seam dimensional position information for arc welding robot based on vision computing,Journal of Intelligent and Robotic Systems:Theory and Applications,2005,Vol.43(1):77-97.
33. Umeagukwu Charles, McCormick James. Investigation of an array technique for robotic seam tracking of weld joints [J]. IEEE transaction on Industrial electronics. 1991, 38(3): 223-229.
34. J.E.Agapakis, J.M.Katz, J.M.Friedman. Vision-aided robotic Welding: An approach and a flexible implementation [J]. Int JRobotics Res. 1989, 9(5): 17-34.
35. J.E.Agapakis, N.Wittels, K.Masubuchi. Automated Visual Weld Inspection for Robotic WeldingFabrication. In: Proc Int Conf on Automation and Robotization of Welding and Allied Processes, Oxford: Pergmon Press; 1985. p. 151-160.
36. Gonz′alez-Galv′an Emilio J., Loredo-Flores Ambrocio,et al. An Optimal Path-Tracking Algorithm for Unstructured Environments based on Uncalibrated Vision. In: Proc. of the 2005 IEEE Int. Conf. on Robotics and Automation; Barcelona, Spain; 2005.
37. Kuno Y., Numagami H., Ishikawa M.. Three-dimensinal vision trchniqurs for an advanced robot system. In: Proc. Robotics and Automation 1985 IEEE Int. Conf., 1985:1-16.
38. G.Agapiou, C.Kasiouras, A.A.Serafetinides, A detailed analysis of the MIG spectrum for the development of laser-based seam tracking sensors, Optics&Laser Technology, 1999, Vol.31:157-161.
39. Jae Seon Kim,Young Tak Son,Hyung Suck Cho etc.,A robust visual seam tracking system for robotic arc welding, Mechatronics,1996,Vol.6(2): 141-163.
40. http://www.meta-mvs.com.
41.徐培全,唐新华,芦凤桂,基于机器人焊接的视觉传感系统研究综述,焊接,2005, Vol.8: 11-14.
42. http://www.servorobot.com.
43. G.J.Zhang, Z.H.Yan and L.Wu, Visual Sensing of Weld Pool in Variable Polarity TIG Welding of Aluminum Alloy, Transactions of Nonferrous Metals Society of China, 2006, Vol.16 (3):522-526.
44. L.P.Li, S.B.Chen and T.Lin, The Modeling of Welding Pool Surface Reflectance of Aluminum Alloy Pulse GTAW.Materials Science and Engineering A, 2005, Vol.394 (2):320-326.
45. C.Balfour, J.S.Smith, and A.I. AI-Shamma, A novel edge feature correlation algorithm for real-time computer vision-based molten weld pool measurements, Welding Journal, 2006 Vol.1:1-8s.
46.李鹏九,焊接过程弧光传感的应用基础研究[博士学位论文],哈尔滨,哈尔滨工业大学. 1997.
47.赵冬斌,基于三维视觉传感的填丝脉冲GTAW熔池形状动态智能控制[博士学位论文],哈尔滨,哈尔滨工业大学,2000.
48. Pietrzak, K. A., and Packer S. M.,Vision-Based Weld Pool Width Control. ASME Journal of Engineering for Industry,1994, Vol.116(2):86-92.
49.王军波,孙振国,陈强等,基于CCD传感器的球罐焊接机器人焊缝跟踪,焊接学报,2001,Vol.22(2),31-34.
50. K.-Y.Bae,T.-H.Lee,K.-C.Ahn,An optical sensing system for seam tracking and weld pool control in gas metal arc welding of steel pipe,Journal of Materials Processing Technology,2002, Vol.120,458-465.
51. D. Brzakovic, D.T. Khani. Weld Pool Edge Detection for Automated Control of Welding,IEEETransactions on Robotics and Automation, 1991, Vol.7 (3):397-403.
52.娄亚军,基于熔池图像传感的脉冲GTAW动态过程智能控制[博士学位论文],哈尔滨,哈尔滨工业大学,1998.
53.赵冬斌,由单目图像获得表面高度算法的分析和实现,计算机学报, 2000, Vol.23 (2):147-152.
54. Zhang, G. J.; Chen, S. B., Wu, L,Intelligent control of pulsed GTAW with filler metal, Welding Journal, 2005, 84 (1): 9-16.
55. K. Oshima, M. Morita,Observation and Digital Control of the Molten Pool in Pulsed MIG Welding,Welding International. 1988, (3):234-240.
56. Oshima, M. Morita,Sensing and Digital Control of Weld Pool in Pulsed MIG Welding, Transactions of the Japan Welding Society. 1992, Vol. 23(4):36-42.
57.石玗,张得峰,樊丁,梁卫东,用于铝合金MIG焊的机器人熔池视觉传感系统,电焊机, 2006 Vol.36 (3): 27-32.
58.石玗,樊丁,李建军,陈剑虹,视觉传感铝合金脉冲MIG焊熔宽控制系统,焊接学报, 2007,Vol.28 (2): 9-12.
59.周龙早,刘顺洪,付朝杰,CO2焊接熔池直接视觉图像传感研究,电焊机, 2006 Vol.36 (9): 62-65.
60.曹一鹏,陈强,孙振国,CO2短路过渡焊接熔池图像传感器,焊接学报, 2004,Vol.25 (2): 9-12.
61. Zhonghua Liu, Qilong Wang, Bing Zheng,Process control based on double-side image sensing of the keyhole in VPPA welding,Journal of Materials Processing Technology, 2001(115): 373-379.
62.葛景国,何德孚,倪纯珍,PAW视觉传感焊缝跟踪检测系统的设计与实现,上海交通大学学报,2003 Vol.37 No.10: 1529-1531.
63.奥凯尔勃洛姆,焊接变形与应力,雷原译,北京,机械工业出版社,1958.
64. Y.Ueda, H.Murakawa, Applications of computer and numerical analysis techniques in welding research.Transactions of JWRI, 1984, Vol.13 (2):165-174.
65. D.拉达伊,焊接热效应,北京,机械工业出版社,1997.
66. D.H.Kang, K.J.Son, Y.S.Yang.Analysis of laser weldment distortion in the EDFA LD pump packaging,Finite Element in Analysis and Design, 2001, Vol.37:749-760.
67.王国凡,初福民,陈鹭滨,多节框架的焊接工艺及变形控制,焊接技术,2001,Vol.30(4):45-47.
68.李辉,陈卫,集装箱平车中梁焊接变形的工艺分析,焊接,2002(4):41-42.
69.陈丙森,计算机辅助焊接技术,北京,机械工业出版社,1999.
70.谢雷,基于固有应变的大型焊接结构的变形预测的研究[学位论文],上海,上海交通大学,2004.
71.姚君山,张彦华,张崇显,有源强化传热控制薄板焊接压曲变形的研究,机械工程学报,2000,Vol.36(9):55-60.
72. Q.Guan, D.L.Guo, C.Q.Li, Low stress non-distortion (LSND) welding—a new technique for thin materials, Welding in the World, 1994, Vol.33 (3):160-167.
73. Q.Guan, C.X.Zhang, D.L.Guo.Dynamic control of welding distortion by moving spot heat sink, welding in the world, 1994, Vol.33 (4):308-312.
74.李铸国,吴毅雄,林涛,复杂汽车零部件精度焊接成形质量保证系统,焊接学报,2001,Vol.22(5):65-68.
75.汪建华,焊接数值模拟技术及其应用,上海,上海交通大学出版社,2003.10.
76. Zhao P C, Wu C S and Zhang Y M, Numerical simulation of dynamic characteristics of weld pool geometry with step changes of welding parameters, Modeling and Simulation in Materials Science and Engineering, 2004(12): 765-780.
77.陈茂爱,GMAW焊接熔滴过渡动态过程的数值模拟,山东大学, 2003.
78.史清宇,焊接过程三维数值模拟的研究及应用[博士学位论文],北京,清华大学, 2000.
79.曹振宁,TIG/MIG焊接熔透熔池流场与热场的数值分析[博士学位论文],哈尔滨,哈尔滨工业大学工学,1993.
80.李鹤岐,大岛健司,用微机图像法对脉冲MAG焊接熔池进行观察和控制的研究,焊接学报,1988,Vol.9(1):37-43.
81. Jeng-Ywan Jeng, Prediction of Laser Butt Joint Welding Parameters Using Back Propagation and Learning Vector Quantization Networks, Journal of Materials Processing Technology, 2000(99):207-218.
82. Yasuo SUGA, Measurement of Molten Pool Shape and Penetration Control Applying Neural Network in TIG Welding of Thin Steel Plates, ISIJ international,1999, Vol.39 (10):1075-1080.
83.张裕明,TIG焊熔透正面视觉自适应控制的研究[博士学位论文],哈尔滨,哈尔滨工业大学,1990.
84. Y.S. Tarng, Modeling, Optimization and Classification of Weld Quality in Tungsten Inert Gas Welding, International Journal of Machine Tools & Manufacture, 1999, 39:1427-1438.
85. S.C.Juang,Y.S.Tarng, A Comparison between the Back-propagation and Counter-propagation Networks in the Modeling of the TIG Welding Process, Journal of Materials Processing Technology,1998(75):54-62.
86. Billy Chan, Modeling Gas Metal Arc Weld Geometry Using Artificial Neural Network Technology, Canadian Metallurgical Quarterly, 1999, Vol.38 (1): 43-51.
87. George E. Cook, Weld Modeling and Control Using Artificial Neural Networks, IEEE Transactions on Industry Applications, 1995, Vol.31 (6): 1484-1491.
88. R. Kovacevic, Y.M. Zhang, and L. Li, Monitoring of Weld Penetration Based on Weld Pool Geometrical Appearance, Welding Journal, 1996, Vol.75 (10):317-328s.
89.李迪,用人工神经元网络技术对焊接质量的智能控制[博士学位论文],广州,华南理工大学,1993.
90. Zhang, G. J.; Chen, S. B., Wu, L. Intelligent control of pulsed GTAW with filler metal, Welding Journal, 2005, Vol.84 (1): 9-16.
91.杜全营,填丝脉冲GTAW熔池三维特征实时提取与智能控制[博士学位论文],上海,上海交通大学,2006.
92. Wang, B., Chen, S. B., and Wang, J. J., Rough set based knowledge modeling for the aluminum alloy pulsed gtaw process, The International Journal of Advanced Manufacturing Technology, 2005, 25(9), 902-908.
93.黎文航,基于变精度粗糙集理论的焊接动态过程知识建模方法研究[博士学位论文],上海,上海交通大学, 2007.
94. Chu, W.-H. and Tung, P.-C., Development of an automatic arc welding system using a sliding mode control, International Journal of Machine Tools & Manufacture, 2005, 45, 933-939.
95. Zhang, Y. M., Adaptive Control of Full Penetration Gas Tungsten Arc Welding, IEEE Transactions on Control System Technology, 1996, 4(4), 394-403.
96. Chen, S. B., Wu, L., Wang, Q. L., et al., Self-learning fuzzy neural network and computer vision for control of pulsed GTAW, Welding Journal, 1997, 76(5), 201-209s.
97. Chen, S. B., Wu, L., and Wang, Q. L., Self-learning fuzzy neural networks for control of uncertain systems with time delays, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 1997, 27(1), 142-148.
98. Du Quanying, Chen Shanben, and Lin Tao, Inspection on shape of weld based on shape from shading, The international Journal of Advanced Manufacturing Technology, 2006, 27, 667-671.
99. Quan Ying Du, Shan Ben Chen, and Lin, T., An application of shape from shading, in ICARCV 2004-Proceedings of The 8th International Conference on Control, Automation, Robotics and Vision. 2004. p. 184-189.
100.赵伟,李午申,赵春明,焊接产品质量分析及诊断的混合型专家系统的一种开发方法,信息与控制,2002,Vol.29(3):266-271.
101.朱援祥,钟剑,张彦华,基于知识库的焊接裂纹诊断专家系统,焊接学报,2001,Vol.22(3):59-62.
102.格拉德柯夫,控制和预测焊接质量的计算机可视系统,航空工艺技术,1999(2):49-50.
103.付荣华,焊接专家系统的应用现状和发展,热加工工艺,2006,Vol.35(3):53-55.
104.宋永伦,机器人弧焊过程焊缝质量信息的在线判读,焊接技术,2000(29): 15-17.
105.蔡志鹏,大型结构焊接变形数值模拟的研究与应用[博士学位论文],北京,清华大学, 2001.
106.刘玉池,计算机视觉脉冲TIG焊正面实时检测与熔宽控制的研究[硕士学位论文],哈尔滨,哈尔滨工业大学,1994.
107.冶金工业部科技情报产品标准研究所编译,光谱线波长表,北京,中国工业出版社,1971.
108.王惠钧,图像传感变极性等离子弧焊缝稳定成形闭环控制[博士学位论文],哈尔滨,哈尔滨工业大学,1998,19-23.
109. Milan Sonka, Vaclav Hlavac and Roger Boyle, Image processing, analysis, and machine vision, Brooks/Cole, a division of Thomson Asia Pte.Ltd, U.S, 2002.
110.张远鹏,董海,周文灵,计算机图像处理技术基础,北京,北京大学出版社,1996.
111.樊重建,变间隙铝合金脉冲GTAW熔池视觉特征获取及其智能控制研究[博士学位论文],上海,上海交通大学,2008.
112.许传祥,二进小波的构造理论,自然科学进展,1997,Vol.7(3):271-276.
113. MallatS, WangW, SignularitydetectionandProeessingwithwavelets, IEEETrans.Informat, Theory,1992, Vol.38(2):617一643.
114. Levent Sendur, Bivariate Shrinkage with Local Variance Estimation, IEEE SIGNAL PROCESSING LETTERS, 2002, Vol. 9(12):438-441.
115.蔡红苹,基于小波变换的图像去噪方法研究[硕士论文],长沙,国防科学技术大学,2003.
116. Kingsbury, Complex wavelets for shift invariant analysis and filtering of signals, Joumal ofAppliedandComputational HarmonicAnalysis, 2001, Vol.10 (3):234-253.
117.范九伦,赵凤,灰度图像的二维Otsu曲线阈值分割法,电子学报, 2007, Vol.35(4): 751-755.
118.卿粼波,何小海,基于最小二乘准则的模糊估计和图像复原,四川大学学报, 2008, Vol.40(2):129-133.
119.方崇智,萧德云,系统辨识,北京,清华大学出版社,1988.
120.候媛彬,汪梅,王立琦,系统辨识及其Matlab仿真,北京,科学出版社,2004.
121. Van Den, Boom, A.J.W., The Determination of the orders process and noise dynamics, Automatic, 1974, vol.10: 245-256.
122. Riedmiller M, Braun H, A direct adaptive method for faster back-propagation learning: The RPROP algorithm, In Proceedings of the IEEE International Conference on Neural Networks,IEEE,1993.
123.徐丽娜,神经网络控制,哈尔滨,哈尔滨工业大学出版社,1998.
124. Simon Haykin,神经网络原理,北京,机械工业出版社,2003.
125.方崇智,萧德云,系统辨识,北京,清华大学出版社,1988.
126.候媛彬,汪梅,王立琦,系统辨识及其Matlab仿真,北京,科学出版社,2004.
127. Jeremy S. Smith and Chris Balfour. Real-time top-face vision based control of weld pool size. Industrial Robot, 2005, Vol.32 (4): 334-340.
128.谢新民,自适应控制系统,北京,清华大学出版社,2002.
2.张文钺,迎接21世纪我国焊接技术的新发展,焊接,2000, Vol.7:6-10.
3.范平章,航天运载器推进系统焊接自动化技术,航天工艺,2000,Vol.3:42-48.
4.林尚扬,陈善本,李成桐,焊接机器人及其应用,北京,机械工业出版社,2000.
5.马颂德,计算机视觉,北京,北京科学出版社,1998.
6. Marc Ebner, Andreas Zell, Centering behavior with a mobile robot using monocular foveated vision, Robotics and Autonomous Systems, 2000, Vol.32, 207-218.
7. F.Heimes, H.H.Nagel, Real-time tracking of intersections in image sequences of a moving camera, Engineering Applications of Artificial Intelligence, 1998, 11,215-227.
8. Francois Marmoiton,Francois Collange,Jean Pierre Derutin,Location and relative speed estimation of vehicles by monocular vision, Proceedings of the IEEE Intelligent Vehicles Symposium,2000,Dearborn USA:227-232.
9. Kai.O.Arras, Nicola Tomatis, Roland Siegwart, Multi-sensor On-the-Fly Localization Using Laser and Vision, Proceedings of the 2000 IEEE/RS International Conference on intelligent Robots and Systems: 462-467.
10.张业鹏,何涛,机器视觉在工业测量中的应用与研究,光学精密工程,2001,Vol.9(4):324-330.
11.应义斌,傅宾忠,蒋亦元等,机器视觉技术在农业生产自动化中的应用,农业工程学报,1999,Vol.15(3): 33-37.
12. Min-Fan Ricky Lee,Clarence W.de Silva,Elizabeth A.Croft,etc.Machine vision system for curved surface inspection,Machine Vision and Applications,2000(12): 177-188.
13. Ramesh Jain,Rangachar Kasturi,Brian G.Schunck,机器视觉(英文版),北京,机械工业出版社,2003,76-86.
14.王耀男,李树涛,毛建旭,计算机图像处理与识别技术,北京,高等教育出版社,2001.
15. Kenneth R.Castleman著,朱志刚,林学訚,石定机等译,数字图像处理(第二版),北京,电子工业出版社,2002.
16.陈志翔,J.P.Boillot,先进激光视觉传感技术及其在焊接中的应用,机械工人,2007,Vol.6:37-41.
17.何方殿,王克争,苏勇,视觉传感器焊接跟踪系统的研究和发展,电焊机,1993,Vol.4: 8-13.
18.廖宝剑,吴世德,潘际銮,CO2气体保护焊的电弧传感自动跟踪,见第八次全国焊接会议论文集,北京,机械工业出版社,1997,第3册,161-163.
19.蒋鹏飞,Joho.D.Wood,用声发射传感器的焊缝跟踪控制研究,电焊机,1994, Vol.4: 4-8.
20.黄石生,钱迎雪,基于ART人工神经网络的焊缝跟踪检测算法,机械工程学报,1994,Vol.2: 93-97.
21.胡绳荪,Fuzzy-P控制超声波传感埋弧焊焊缝的跟踪系统,焊接学报,1998, Vol.2: 104-109.
22.蔡志勇,基于视觉的焊缝识别与其DSP实现[博士学位论文],南昌,南昌大学,2004.
23.王军波,孙振国,陈强等,基于CCD传感器的球罐焊接机器人焊缝跟踪,焊接学报,2001,Vol.22(2):31-34.
24. Y Suga, M Narus, T Tkiwa, Application of neural network to visual sensing of welding and automatic tracking in robot welding in the world, 1994, Vol.34:225-282.
25. Min Young Kim,Kuk-won KO,Hyung Suck Cho,etc.Visual Sensing and Recognition of Welding Environment for Intelligent Shipyard Welding Robots,in Proceedings of the 2000 /EEE/RSJ international Conference on Intelligent Robots and Systems,2159-2165.
26. Shigetomo Matsui,Gokhan Goktug,Slit laser sensor guided real-time seam tracking arc welding robot system for non-uniform joint gaps,Industrial Technology,IEEE ICIT'02,2002,159-162.
27. Huang Nan,Abbott M.G.,Beattie,R.J.,Approaches to low level image processing for vision guided seam tracking systems,Pattern Recognition,1988,9th International Conference,Vol.1, 601-603.
28.林尚扬,从第13届埃森焊接展览会看焊接技术的走向,中国机械工程,1994, Vol.3: 67-69.
29.王建军,铝合金脉冲TIG焊熔池动态特征的视觉信息获取与自适应控制[博士学位论文],上海,上海交通大学,2003.
30. J.Wu, J.S.Smsith, J.Lucas, Weld bead placement system for multipass welding, Science, Measurement and Technology, IEE Proceedings, 1996, Vol.14 (2): 85-90.
31. L.Kreft, W.Scheller, Arc welding seam tracking system based on artificial neural networks Intelligent Systems Engineering, 1994, Second International Conference on Systems Engineering, 177-182.
32. Chen S.B.,Chen X.Z.,Qiu T.,etc.Acquisition of weld seam dimensional position information for arc welding robot based on vision computing,Journal of Intelligent and Robotic Systems:Theory and Applications,2005,Vol.43(1):77-97.
33. Umeagukwu Charles, McCormick James. Investigation of an array technique for robotic seam tracking of weld joints [J]. IEEE transaction on Industrial electronics. 1991, 38(3): 223-229.
34. J.E.Agapakis, J.M.Katz, J.M.Friedman. Vision-aided robotic Welding: An approach and a flexible implementation [J]. Int JRobotics Res. 1989, 9(5): 17-34.
35. J.E.Agapakis, N.Wittels, K.Masubuchi. Automated Visual Weld Inspection for Robotic WeldingFabrication. In: Proc Int Conf on Automation and Robotization of Welding and Allied Processes, Oxford: Pergmon Press; 1985. p. 151-160.
36. Gonz′alez-Galv′an Emilio J., Loredo-Flores Ambrocio,et al. An Optimal Path-Tracking Algorithm for Unstructured Environments based on Uncalibrated Vision. In: Proc. of the 2005 IEEE Int. Conf. on Robotics and Automation; Barcelona, Spain; 2005.
37. Kuno Y., Numagami H., Ishikawa M.. Three-dimensinal vision trchniqurs for an advanced robot system. In: Proc. Robotics and Automation 1985 IEEE Int. Conf., 1985:1-16.
38. G.Agapiou, C.Kasiouras, A.A.Serafetinides, A detailed analysis of the MIG spectrum for the development of laser-based seam tracking sensors, Optics&Laser Technology, 1999, Vol.31:157-161.
39. Jae Seon Kim,Young Tak Son,Hyung Suck Cho etc.,A robust visual seam tracking system for robotic arc welding, Mechatronics,1996,Vol.6(2): 141-163.
40. http://www.meta-mvs.com.
41.徐培全,唐新华,芦凤桂,基于机器人焊接的视觉传感系统研究综述,焊接,2005, Vol.8: 11-14.
42. http://www.servorobot.com.
43. G.J.Zhang, Z.H.Yan and L.Wu, Visual Sensing of Weld Pool in Variable Polarity TIG Welding of Aluminum Alloy, Transactions of Nonferrous Metals Society of China, 2006, Vol.16 (3):522-526.
44. L.P.Li, S.B.Chen and T.Lin, The Modeling of Welding Pool Surface Reflectance of Aluminum Alloy Pulse GTAW.Materials Science and Engineering A, 2005, Vol.394 (2):320-326.
45. C.Balfour, J.S.Smith, and A.I. AI-Shamma, A novel edge feature correlation algorithm for real-time computer vision-based molten weld pool measurements, Welding Journal, 2006 Vol.1:1-8s.
46.李鹏九,焊接过程弧光传感的应用基础研究[博士学位论文],哈尔滨,哈尔滨工业大学. 1997.
47.赵冬斌,基于三维视觉传感的填丝脉冲GTAW熔池形状动态智能控制[博士学位论文],哈尔滨,哈尔滨工业大学,2000.
48. Pietrzak, K. A., and Packer S. M.,Vision-Based Weld Pool Width Control. ASME Journal of Engineering for Industry,1994, Vol.116(2):86-92.
49.王军波,孙振国,陈强等,基于CCD传感器的球罐焊接机器人焊缝跟踪,焊接学报,2001,Vol.22(2),31-34.
50. K.-Y.Bae,T.-H.Lee,K.-C.Ahn,An optical sensing system for seam tracking and weld pool control in gas metal arc welding of steel pipe,Journal of Materials Processing Technology,2002, Vol.120,458-465.
51. D. Brzakovic, D.T. Khani. Weld Pool Edge Detection for Automated Control of Welding,IEEETransactions on Robotics and Automation, 1991, Vol.7 (3):397-403.
52.娄亚军,基于熔池图像传感的脉冲GTAW动态过程智能控制[博士学位论文],哈尔滨,哈尔滨工业大学,1998.
53.赵冬斌,由单目图像获得表面高度算法的分析和实现,计算机学报, 2000, Vol.23 (2):147-152.
54. Zhang, G. J.; Chen, S. B., Wu, L,Intelligent control of pulsed GTAW with filler metal, Welding Journal, 2005, 84 (1): 9-16.
55. K. Oshima, M. Morita,Observation and Digital Control of the Molten Pool in Pulsed MIG Welding,Welding International. 1988, (3):234-240.
56. Oshima, M. Morita,Sensing and Digital Control of Weld Pool in Pulsed MIG Welding, Transactions of the Japan Welding Society. 1992, Vol. 23(4):36-42.
57.石玗,张得峰,樊丁,梁卫东,用于铝合金MIG焊的机器人熔池视觉传感系统,电焊机, 2006 Vol.36 (3): 27-32.
58.石玗,樊丁,李建军,陈剑虹,视觉传感铝合金脉冲MIG焊熔宽控制系统,焊接学报, 2007,Vol.28 (2): 9-12.
59.周龙早,刘顺洪,付朝杰,CO2焊接熔池直接视觉图像传感研究,电焊机, 2006 Vol.36 (9): 62-65.
60.曹一鹏,陈强,孙振国,CO2短路过渡焊接熔池图像传感器,焊接学报, 2004,Vol.25 (2): 9-12.
61. Zhonghua Liu, Qilong Wang, Bing Zheng,Process control based on double-side image sensing of the keyhole in VPPA welding,Journal of Materials Processing Technology, 2001(115): 373-379.
62.葛景国,何德孚,倪纯珍,PAW视觉传感焊缝跟踪检测系统的设计与实现,上海交通大学学报,2003 Vol.37 No.10: 1529-1531.
63.奥凯尔勃洛姆,焊接变形与应力,雷原译,北京,机械工业出版社,1958.
64. Y.Ueda, H.Murakawa, Applications of computer and numerical analysis techniques in welding research.Transactions of JWRI, 1984, Vol.13 (2):165-174.
65. D.拉达伊,焊接热效应,北京,机械工业出版社,1997.
66. D.H.Kang, K.J.Son, Y.S.Yang.Analysis of laser weldment distortion in the EDFA LD pump packaging,Finite Element in Analysis and Design, 2001, Vol.37:749-760.
67.王国凡,初福民,陈鹭滨,多节框架的焊接工艺及变形控制,焊接技术,2001,Vol.30(4):45-47.
68.李辉,陈卫,集装箱平车中梁焊接变形的工艺分析,焊接,2002(4):41-42.
69.陈丙森,计算机辅助焊接技术,北京,机械工业出版社,1999.
70.谢雷,基于固有应变的大型焊接结构的变形预测的研究[学位论文],上海,上海交通大学,2004.
71.姚君山,张彦华,张崇显,有源强化传热控制薄板焊接压曲变形的研究,机械工程学报,2000,Vol.36(9):55-60.
72. Q.Guan, D.L.Guo, C.Q.Li, Low stress non-distortion (LSND) welding—a new technique for thin materials, Welding in the World, 1994, Vol.33 (3):160-167.
73. Q.Guan, C.X.Zhang, D.L.Guo.Dynamic control of welding distortion by moving spot heat sink, welding in the world, 1994, Vol.33 (4):308-312.
74.李铸国,吴毅雄,林涛,复杂汽车零部件精度焊接成形质量保证系统,焊接学报,2001,Vol.22(5):65-68.
75.汪建华,焊接数值模拟技术及其应用,上海,上海交通大学出版社,2003.10.
76. Zhao P C, Wu C S and Zhang Y M, Numerical simulation of dynamic characteristics of weld pool geometry with step changes of welding parameters, Modeling and Simulation in Materials Science and Engineering, 2004(12): 765-780.
77.陈茂爱,GMAW焊接熔滴过渡动态过程的数值模拟,山东大学, 2003.
78.史清宇,焊接过程三维数值模拟的研究及应用[博士学位论文],北京,清华大学, 2000.
79.曹振宁,TIG/MIG焊接熔透熔池流场与热场的数值分析[博士学位论文],哈尔滨,哈尔滨工业大学工学,1993.
80.李鹤岐,大岛健司,用微机图像法对脉冲MAG焊接熔池进行观察和控制的研究,焊接学报,1988,Vol.9(1):37-43.
81. Jeng-Ywan Jeng, Prediction of Laser Butt Joint Welding Parameters Using Back Propagation and Learning Vector Quantization Networks, Journal of Materials Processing Technology, 2000(99):207-218.
82. Yasuo SUGA, Measurement of Molten Pool Shape and Penetration Control Applying Neural Network in TIG Welding of Thin Steel Plates, ISIJ international,1999, Vol.39 (10):1075-1080.
83.张裕明,TIG焊熔透正面视觉自适应控制的研究[博士学位论文],哈尔滨,哈尔滨工业大学,1990.
84. Y.S. Tarng, Modeling, Optimization and Classification of Weld Quality in Tungsten Inert Gas Welding, International Journal of Machine Tools & Manufacture, 1999, 39:1427-1438.
85. S.C.Juang,Y.S.Tarng, A Comparison between the Back-propagation and Counter-propagation Networks in the Modeling of the TIG Welding Process, Journal of Materials Processing Technology,1998(75):54-62.
86. Billy Chan, Modeling Gas Metal Arc Weld Geometry Using Artificial Neural Network Technology, Canadian Metallurgical Quarterly, 1999, Vol.38 (1): 43-51.
87. George E. Cook, Weld Modeling and Control Using Artificial Neural Networks, IEEE Transactions on Industry Applications, 1995, Vol.31 (6): 1484-1491.
88. R. Kovacevic, Y.M. Zhang, and L. Li, Monitoring of Weld Penetration Based on Weld Pool Geometrical Appearance, Welding Journal, 1996, Vol.75 (10):317-328s.
89.李迪,用人工神经元网络技术对焊接质量的智能控制[博士学位论文],广州,华南理工大学,1993.
90. Zhang, G. J.; Chen, S. B., Wu, L. Intelligent control of pulsed GTAW with filler metal, Welding Journal, 2005, Vol.84 (1): 9-16.
91.杜全营,填丝脉冲GTAW熔池三维特征实时提取与智能控制[博士学位论文],上海,上海交通大学,2006.
92. Wang, B., Chen, S. B., and Wang, J. J., Rough set based knowledge modeling for the aluminum alloy pulsed gtaw process, The International Journal of Advanced Manufacturing Technology, 2005, 25(9), 902-908.
93.黎文航,基于变精度粗糙集理论的焊接动态过程知识建模方法研究[博士学位论文],上海,上海交通大学, 2007.
94. Chu, W.-H. and Tung, P.-C., Development of an automatic arc welding system using a sliding mode control, International Journal of Machine Tools & Manufacture, 2005, 45, 933-939.
95. Zhang, Y. M., Adaptive Control of Full Penetration Gas Tungsten Arc Welding, IEEE Transactions on Control System Technology, 1996, 4(4), 394-403.
96. Chen, S. B., Wu, L., Wang, Q. L., et al., Self-learning fuzzy neural network and computer vision for control of pulsed GTAW, Welding Journal, 1997, 76(5), 201-209s.
97. Chen, S. B., Wu, L., and Wang, Q. L., Self-learning fuzzy neural networks for control of uncertain systems with time delays, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 1997, 27(1), 142-148.
98. Du Quanying, Chen Shanben, and Lin Tao, Inspection on shape of weld based on shape from shading, The international Journal of Advanced Manufacturing Technology, 2006, 27, 667-671.
99. Quan Ying Du, Shan Ben Chen, and Lin, T., An application of shape from shading, in ICARCV 2004-Proceedings of The 8th International Conference on Control, Automation, Robotics and Vision. 2004. p. 184-189.
100.赵伟,李午申,赵春明,焊接产品质量分析及诊断的混合型专家系统的一种开发方法,信息与控制,2002,Vol.29(3):266-271.
101.朱援祥,钟剑,张彦华,基于知识库的焊接裂纹诊断专家系统,焊接学报,2001,Vol.22(3):59-62.
102.格拉德柯夫,控制和预测焊接质量的计算机可视系统,航空工艺技术,1999(2):49-50.
103.付荣华,焊接专家系统的应用现状和发展,热加工工艺,2006,Vol.35(3):53-55.
104.宋永伦,机器人弧焊过程焊缝质量信息的在线判读,焊接技术,2000(29): 15-17.
105.蔡志鹏,大型结构焊接变形数值模拟的研究与应用[博士学位论文],北京,清华大学, 2001.
106.刘玉池,计算机视觉脉冲TIG焊正面实时检测与熔宽控制的研究[硕士学位论文],哈尔滨,哈尔滨工业大学,1994.
107.冶金工业部科技情报产品标准研究所编译,光谱线波长表,北京,中国工业出版社,1971.
108.王惠钧,图像传感变极性等离子弧焊缝稳定成形闭环控制[博士学位论文],哈尔滨,哈尔滨工业大学,1998,19-23.
109. Milan Sonka, Vaclav Hlavac and Roger Boyle, Image processing, analysis, and machine vision, Brooks/Cole, a division of Thomson Asia Pte.Ltd, U.S, 2002.
110.张远鹏,董海,周文灵,计算机图像处理技术基础,北京,北京大学出版社,1996.
111.樊重建,变间隙铝合金脉冲GTAW熔池视觉特征获取及其智能控制研究[博士学位论文],上海,上海交通大学,2008.
112.许传祥,二进小波的构造理论,自然科学进展,1997,Vol.7(3):271-276.
113. MallatS, WangW, SignularitydetectionandProeessingwithwavelets, IEEETrans.Informat, Theory,1992, Vol.38(2):617一643.
114. Levent Sendur, Bivariate Shrinkage with Local Variance Estimation, IEEE SIGNAL PROCESSING LETTERS, 2002, Vol. 9(12):438-441.
115.蔡红苹,基于小波变换的图像去噪方法研究[硕士论文],长沙,国防科学技术大学,2003.
116. Kingsbury, Complex wavelets for shift invariant analysis and filtering of signals, Joumal ofAppliedandComputational HarmonicAnalysis, 2001, Vol.10 (3):234-253.
117.范九伦,赵凤,灰度图像的二维Otsu曲线阈值分割法,电子学报, 2007, Vol.35(4): 751-755.
118.卿粼波,何小海,基于最小二乘准则的模糊估计和图像复原,四川大学学报, 2008, Vol.40(2):129-133.
119.方崇智,萧德云,系统辨识,北京,清华大学出版社,1988.
120.候媛彬,汪梅,王立琦,系统辨识及其Matlab仿真,北京,科学出版社,2004.
121. Van Den, Boom, A.J.W., The Determination of the orders process and noise dynamics, Automatic, 1974, vol.10: 245-256.
122. Riedmiller M, Braun H, A direct adaptive method for faster back-propagation learning: The RPROP algorithm, In Proceedings of the IEEE International Conference on Neural Networks,IEEE,1993.
123.徐丽娜,神经网络控制,哈尔滨,哈尔滨工业大学出版社,1998.
124. Simon Haykin,神经网络原理,北京,机械工业出版社,2003.
125.方崇智,萧德云,系统辨识,北京,清华大学出版社,1988.
126.候媛彬,汪梅,王立琦,系统辨识及其Matlab仿真,北京,科学出版社,2004.
127. Jeremy S. Smith and Chris Balfour. Real-time top-face vision based control of weld pool size. Industrial Robot, 2005, Vol.32 (4): 334-340.
128.谢新民,自适应控制系统,北京,清华大学出版社,2002.