大口径直缝埋弧焊管JCO成形智能化控制技术的研究
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摘要
自河北青县巨龙钢管有限公司引进了我国第一条大口径直缝埋弧焊管JCO生产线后,我国开始对管坯JCO成形工艺及相关技术进行了消化和吸收,由于关键技术和设备从国外引进,对管坯成形质量控制及工艺参数调整只停留在经验层面上,存在着工艺参数调整误差大、人员工作强度大及产品质量波动大等现实情况。为此,本文提出了大口径直缝埋弧焊管JCO成形智能化控制技术,以实现管坯成形质量的实时监测和工艺参数的实时预测。
本文在板材弯曲智能化控制技术的研究成果基础上,分析了管坯JCO成形智能化控制需要解决的关键技术,并对管坯JCO成形过程理论解析、成形过程中的信息监测、材料性能参数识别及工艺参数预测等方面的相关问题展开了系统研究。
从板料成形状态和板料内质点变形状态出发,基于塑性弯曲工程理论,建立了管坯JCO弯曲成形过程的力学模型,推导出了板料任一成形状态板料内任一质点卸载前后的转角、弯曲半径数学表达式,分别给出了描述弯曲力和弯曲行程、弯曲角和弯曲行程之间关系的更为精确的解析表达式。根据卸载理论,建立了精确的回弹计算模型,并利用有限元和实验研究等方法对理论计算结果进行了数值验证,为管坯JCO成形智能化控制技术中识别和预测模型的建立奠定了理论基础。
利用机器视觉和传感器技术,开发了管坯JCO成形过程实时监测系统,实现了弯曲力、弯曲行程和弯曲角等物理参数的非接触、高精度的实时测量。详细研究了管坯端面图像处理和直线检测算法,在试验和结果比较基础上,提出了适合管坯端面的图像处理流程。系统地研究了摄像机标定方法,为克服传统标定方法的标定过程繁琐、需高精度标定参照物的不足,本文提出了适合现场使用的正三角形标定方法,并用实验验证了该方法的正确性和有效性。
通过对管坯JCO成形及弹复过程中的特征变量进行分析和研究,完成了神经网络识别模型的输入、输出层设计,利用MATLAB编程语言,开发了管坯JCO成形过程中的参数识别和预测程序。选择前馈BP网络作为识别和预测模型的神经网络结构、LM算法作为网络优化算法,提高了网络收敛和泛化精度。
在上述研究基础上,利用Visual C++编程语言开发了管坯JCO成形智能化控制系统软件,主要内容包括:管坯弯曲成形过程理论解析计算程序开发,神经网络训练样本自动创建程序开发,弯曲力、弯曲行程数据采集程序开发,管坯端面图像处理及直线检测程序开发,摄像机标定程序开发以及Visual C++与MATLAB接口程序开发。最后利用PCI总线数据采集卡、面阵CCD及相关硬件,完成了管坯JCO成形智能化控制硬件系统开发。
利用管坯JCO成形智能化控制系统进行了物理模拟实验,实验结果表明系统运行稳定,行程工艺参数预测精度可靠,制品椭圆度具有较高的一致性。作为研究工作的最终成果,成功地将自主开发的智能化控制系统移植到了大口径直缝埋弧焊管JCO成形生产线上。
Since the first longitudinal-seam submerged arc welded (LSAW) pipes production line with JCO forming process was established in Julong Steel Pipe Co., Ltd, the unfinished pipe forming process and the correlated technology have been investigated by the researchers. Due to the key technology and the forming press from abroad, the systematical basic research on the forming process has not been conducted yet. And the pipe quality and processing parameters adjustment are conducted according to accumulated experience with the characteristics of large error, high personnel working strength and unstable production quality. As one of the effective method to improve the pipe quality, the intelligent control technology is introduced to real-time monitor the pipe quality and to adjust the processing parameters automatically.
Based on the research achievements of the intelligent control technology for the sheet metal bending, the key technologies of intelligent bending for unfinished pipe forming with JCO process are analyzed, and the relevant issues to the theoretical analysis, the process monitoring, the identified model of material properties and the predicted model of optimal processing parameters have been studied in this paper.
In this investigation, based on the elementary theory of plastic bending, the mechanical model which can describe the bending process and springback behavior adequately has been established. The equations for the rotation angle and radius of each point in neutral surface for any bending process are derived. And the calculated model of springback after removing the punch load is derived according to the uploading theory. The finite element method and experiment were introduced to verify the validation of the mechanical model established before and analyze the dominant factors influenced on the bending and springback process. And the investigation is carried out to provide the theoretic basis for the intelligent control for unfinished pipe forming with JCO process.
Based on machine vision and sensor technology, the system for real-time monitoring the sheet metal bending process is developed, which can measure the bending force, punch displacement and the unbend angle with high precision. The processing algorithms of image pre-processing and line detection, which are simple, efficient and suitable for the pipe ending surface image, are studied in detail. According to the comparison of the image processing results, a series of algorithms suitable for the images are identified. Focused on the live condition, a new camera calibration method has been put forward to transform the angle in image into the actual angle. The experimental data indicates that the calibration method proposed has advantages of simple operation, fast completion and the high precision, which is very suitable for live system calibration.
The identification model of material properties and the prediction model of optimal processing parameters have been constructed employing the neural network technology. In the identification model, the bending force, the punch displacement and the unbend angle are denoted as the input variables, and the material properties are the output variables. The Levenberg-Marquarat is chosen as the optimal algorithm of neural network whose topology structure is feedforward network, and Matlab language is to program.
Employing the research results above, an intelligent control code for unfinished pipe forming with JCO process is developed with Visual C++ language, including the development of calculated code theoretic analysis for sheet metal air bending process, the development of building code of the neural network training sample data, the development of gathering code of bending force and the bending displacement, the development of calibration code, the development of unbend angle identifying code, and the development of the interface code between the signal control and the identification model. At last, employing the portable DAQ card and camera CCD and other hardware, the intelligent control system for the unfinished pipe forming with JCO process is established.
The physical simulated experiments have been conducted with the intelligent control system, which showed that the system operated stably, the punch displacement predicted is reliable, and can obtain high quality pipes. As the final achievement of research work, the intelligent control system has been transplanted to the LSAW pipe production line with JCO process successfully, improving the production efficiency and the product quality and realizing the intelligent control for the unfinished pipe bending process.
本文在板材弯曲智能化控制技术的研究成果基础上,分析了管坯JCO成形智能化控制需要解决的关键技术,并对管坯JCO成形过程理论解析、成形过程中的信息监测、材料性能参数识别及工艺参数预测等方面的相关问题展开了系统研究。
从板料成形状态和板料内质点变形状态出发,基于塑性弯曲工程理论,建立了管坯JCO弯曲成形过程的力学模型,推导出了板料任一成形状态板料内任一质点卸载前后的转角、弯曲半径数学表达式,分别给出了描述弯曲力和弯曲行程、弯曲角和弯曲行程之间关系的更为精确的解析表达式。根据卸载理论,建立了精确的回弹计算模型,并利用有限元和实验研究等方法对理论计算结果进行了数值验证,为管坯JCO成形智能化控制技术中识别和预测模型的建立奠定了理论基础。
利用机器视觉和传感器技术,开发了管坯JCO成形过程实时监测系统,实现了弯曲力、弯曲行程和弯曲角等物理参数的非接触、高精度的实时测量。详细研究了管坯端面图像处理和直线检测算法,在试验和结果比较基础上,提出了适合管坯端面的图像处理流程。系统地研究了摄像机标定方法,为克服传统标定方法的标定过程繁琐、需高精度标定参照物的不足,本文提出了适合现场使用的正三角形标定方法,并用实验验证了该方法的正确性和有效性。
通过对管坯JCO成形及弹复过程中的特征变量进行分析和研究,完成了神经网络识别模型的输入、输出层设计,利用MATLAB编程语言,开发了管坯JCO成形过程中的参数识别和预测程序。选择前馈BP网络作为识别和预测模型的神经网络结构、LM算法作为网络优化算法,提高了网络收敛和泛化精度。
在上述研究基础上,利用Visual C++编程语言开发了管坯JCO成形智能化控制系统软件,主要内容包括:管坯弯曲成形过程理论解析计算程序开发,神经网络训练样本自动创建程序开发,弯曲力、弯曲行程数据采集程序开发,管坯端面图像处理及直线检测程序开发,摄像机标定程序开发以及Visual C++与MATLAB接口程序开发。最后利用PCI总线数据采集卡、面阵CCD及相关硬件,完成了管坯JCO成形智能化控制硬件系统开发。
利用管坯JCO成形智能化控制系统进行了物理模拟实验,实验结果表明系统运行稳定,行程工艺参数预测精度可靠,制品椭圆度具有较高的一致性。作为研究工作的最终成果,成功地将自主开发的智能化控制系统移植到了大口径直缝埋弧焊管JCO成形生产线上。
Since the first longitudinal-seam submerged arc welded (LSAW) pipes production line with JCO forming process was established in Julong Steel Pipe Co., Ltd, the unfinished pipe forming process and the correlated technology have been investigated by the researchers. Due to the key technology and the forming press from abroad, the systematical basic research on the forming process has not been conducted yet. And the pipe quality and processing parameters adjustment are conducted according to accumulated experience with the characteristics of large error, high personnel working strength and unstable production quality. As one of the effective method to improve the pipe quality, the intelligent control technology is introduced to real-time monitor the pipe quality and to adjust the processing parameters automatically.
Based on the research achievements of the intelligent control technology for the sheet metal bending, the key technologies of intelligent bending for unfinished pipe forming with JCO process are analyzed, and the relevant issues to the theoretical analysis, the process monitoring, the identified model of material properties and the predicted model of optimal processing parameters have been studied in this paper.
In this investigation, based on the elementary theory of plastic bending, the mechanical model which can describe the bending process and springback behavior adequately has been established. The equations for the rotation angle and radius of each point in neutral surface for any bending process are derived. And the calculated model of springback after removing the punch load is derived according to the uploading theory. The finite element method and experiment were introduced to verify the validation of the mechanical model established before and analyze the dominant factors influenced on the bending and springback process. And the investigation is carried out to provide the theoretic basis for the intelligent control for unfinished pipe forming with JCO process.
Based on machine vision and sensor technology, the system for real-time monitoring the sheet metal bending process is developed, which can measure the bending force, punch displacement and the unbend angle with high precision. The processing algorithms of image pre-processing and line detection, which are simple, efficient and suitable for the pipe ending surface image, are studied in detail. According to the comparison of the image processing results, a series of algorithms suitable for the images are identified. Focused on the live condition, a new camera calibration method has been put forward to transform the angle in image into the actual angle. The experimental data indicates that the calibration method proposed has advantages of simple operation, fast completion and the high precision, which is very suitable for live system calibration.
The identification model of material properties and the prediction model of optimal processing parameters have been constructed employing the neural network technology. In the identification model, the bending force, the punch displacement and the unbend angle are denoted as the input variables, and the material properties are the output variables. The Levenberg-Marquarat is chosen as the optimal algorithm of neural network whose topology structure is feedforward network, and Matlab language is to program.
Employing the research results above, an intelligent control code for unfinished pipe forming with JCO process is developed with Visual C++ language, including the development of calculated code theoretic analysis for sheet metal air bending process, the development of building code of the neural network training sample data, the development of gathering code of bending force and the bending displacement, the development of calibration code, the development of unbend angle identifying code, and the development of the interface code between the signal control and the identification model. At last, employing the portable DAQ card and camera CCD and other hardware, the intelligent control system for the unfinished pipe forming with JCO process is established.
The physical simulated experiments have been conducted with the intelligent control system, which showed that the system operated stably, the punch displacement predicted is reliable, and can obtain high quality pipes. As the final achievement of research work, the intelligent control system has been transplanted to the LSAW pipe production line with JCO process successfully, improving the production efficiency and the product quality and realizing the intelligent control for the unfinished pipe bending process.
引文
1王茂堂.完善我国油气管道钢管制管体系的几个问题.焊管, 1998, 21(4): 35-40
2李晓红.我国焊管生产现状及发展趋势.钢管, 2008, 37(1): 18-21
3陈宝林.我国建设直缝埋弧焊管机组的前景.钢管, 2000, 29(2): 5-9
4李鹤林.油气输送钢管的发展动向与展望.焊管, 2004, 27(6): 1-11
5张培康.从陕京管道建设看国产埋弧螺旋焊管质量的提高.焊管, 1998, 21(4): 50-51
6尹国耀.库鄯输油管道达到90年代国际先进水平的依据.焊管, 1999, 22(3): 1-3
7陈三强,胡万志.从库鄯输油管线建设特点探讨西部石油管道发展对策.焊管, 1999, 22(1): 1-3
8冯耀荣,李鹤林.管道钢及管道钢管的研究进展与发展方向(上).石油规划设计, 2005, 16(5): 1-7
9张远生,李延丰.大口径直缝埋弧焊钢管生产线简介.焊管, 2001, 24(6): 35-37
10解培成,侯占森,于文光.大直径螺旋埋弧焊管在长输管线上应用前景.鞍山钢铁学院学报, 2002, 25(4): 273-279
11吴坚.轧钢设备及工艺-钢管生产.东北重型机械学院学报, 1984, 6: 180-197
12陈少杰.螺旋焊管成形工艺研究. [燕山大学博士学位论文]. 1997: 5-8
13张绍庆.螺旋焊接钢管的直径控制.焊管, 1995, 18(6): 34-38
14王关荣.压力容器整圆模的设计和应用.压力容器, 1991, 8(6): 40-44
15李长穆等编译.现代钢管生产.冶金工业出版社, 1982: 271-280
16潘家华.我国管道钢管的发展方向.焊管, 1998, 21(4): 16-20
17杨槐安.西气东输工程简介和管道信息.化工施工技术, 2000, 22(5): 6
18李延丰.螺旋缝埋弧焊管与直缝埋弧焊管之特点.焊管, 1998, 21(3): 1-2
19王晓香.建设大口径直缝埋弧焊管生产线可行性研究的几点说明.焊管, 1998, 21(4): 52-54
20丁大宇.大口径直缝焊管生产工艺.钢管, 1997, 26(1): 18-20
21张朝生.国外UOE电焊钢管生产技术发展现状.焊管, 1992, 15(3): 54-61
22兰兴昌.我国建设大直径直缝埋弧焊管机组的机组选型.焊管, 1998, 21(4): 41-45
23杨秀琴.关于我国钢管行业发展战略的思考(上).钢管, 2006, 35(1): 12-18
24李延丰,孙奇. JCO直缝埋弧焊钢管生产线的研发和应用.焊管, 2004, 27(6): 48-53
25郭宝峰,金淼.大口径直缝焊管UOE成形技术及发展策略.锻压技术, 2000, 25(3): 22-25
26李宏.直缝埋弧焊钢管生产线预弯工艺.焊管, 2006, 29(1): 55-57
27孙恒.机械原理.北京:高等教育出版社, 1986: 42-44
28金淼,郭宝峰,任运来,等.大口径管线钢管机械扩径工艺实验研究.锻压技术, 2001, 12(3): 44-46
29王利树,黎剑峰.焊接钢管机械扩径工艺和水压扩径工艺技术分析.焊管, 2006, 29(4): 60-63
30张远生,王占理,王晨,等.大直径焊管生产用扩径机简介.钢管, 2000, 29(6): 40-42
31 W. M. Sing, K. P. Rao. Knowledge-Based Process Layout System for Axisymmetrical Deep Drawing Using Decision Tables. Computer & Industrial Engineering, 1997, 32(2): 299-319
32 G. S. Sekhon R. Singh. An Expert System for Optimal Selection of a Press for a Sheet Metal Operation. Journal of Materials Processing Technology, 1999, 86(1-3): 131-138
33何大钧,王孝培,罗永建.筒形件拉深CAPP中模糊信息优化技术的应用.锻压技术, 2002(1): 11-14
34吕冬,丁柯,何丹农,等.基于神经网络的拉深力智能化预测系统.中国有色金属学报, 2000, 10(3): 420-425
35赵军.圆锥形件拉深成形智能化的研究. [哈尔滨工业大学博士学位论文]. 1997: 128-129
36王凤琴.盒形件拉深智能化控制关键技术的研究. [燕山大学博士学位论文]. 2003: 92-94
37官英平.板材弯曲智能化控制技术的研究. [燕山大学博士学位论文]. 2004: 85-86
38赵军,秦泗吉,曹宏强,等.轴对称曲面件智能化拉深成形过程的解析定量描述.塑性工程学报, 1998, 5(4): 47-48
39赵军,李硕本,吕炎.板材冲压成形的智能化控制技术.塑性工程学报, 1999, 6(4): 10-21
40赵军,罗亚军,官英平,等.基于神经网络的材料性能参数和摩擦系数的实时识别.塑性工程学报, 2001, 8(2): 36-39
41王凤琴,高颖,赵军.基于遗传算法的神经网络优化.燕山大学学报, 2001, 25(3): 235-239
42赵军,郑祖伟,王凤琴.轴对称件拉深成形智能化控制过程中参数实时识别的GA-ENN建模.中国机械工程, 2003, 14(1): 72-74
43王凤琴,赵军,官英平,等.方盒形件拉深破裂预测的研究.塑性工程学报, 2003, 10(6): 19-22
44 Zhao Jun, Cao Hong-qiang, Ma Li-xia. Study on intelligent control technology of deep drawing for an axis-symmetric shell part. Journal of Material Processing Technology, 2004, 151: 98-104
45官英平,赵军,苏春建.宽板V形自由弯曲智能化控制过程材料参数识别及最优工艺参数预测.机械工程学报, 2005, 41(4): 199-202
46官英平,赵军,苏春建.宽板V形自由弯曲智能化控制过程解析定量描述.塑性工程学报, 2005, 12(2): 30-33
47 Zhao Jun, Wang Fengqin. Parameter identification by neural network for intelligent deep drawing of axisymmetric workpieces. Journal of Material Processing Technology, 2005, 166: 387-391
48马丽霞,王凤琴,赵军.盒形件主要变形区的应力近似理论解析.机械工程学报, 2005, 12(7): 32-37
49赵军,苏建春,官英平,等.弯曲智能化控制系统中基于LabVIEW虚拟仪器高速数据采集的实现.制造业自动化, 2007, 29(2): 29-33
50 Ken-ichi Manabe, Kentaro Soeda. Adaptive Control Method of Deep Drawing Using the Variable Blank Holding Force Technique. Journal of the Japan Society for Technology of Plasticity, 1992, 33(375): 423-428
51马瑞.盒形件智能化拉深压边力控制规律的研究和实时预测. [燕山大学博士学位论文]. 2006: 91-92
52楊明.知能化技術.塑性と加工, 1996, 427(37): 859-860
53 Ken-ichi Manabe, M. Yang. Intelligent Technology on Sheet Metal Forming. Journal of the JSTP, 1993, 387(34): 398-404
54 H. J. Park, H. S. Cho. Fuzzy Self-Learning Control Method with Application to Hydroforming Processes. ASME Journal of Engineering for Industry, 1995, 117: 197-303
55苏春建.多角单次弯曲件回弹规律及智能化控制技术的研究. [燕山大学博士学位论文]. 2007: 132-134
56 ZHANG Z T, HU S J. Stress and Residual Stress Distributions in Plane Strain Bending. Int J Mech Sci, 1998, 40(6): 533-543
57 Qzgur Tekaslan, Ulvi Seker, Ahmet Ozdemir. Determining springback amount of steel sheet metal has 0.5mmthickness in bending dies. Materials and Design, 2006, 27: 251-258
58 Dongye Fei, Peter Hodgson. Experimental and numerical studies of springback in air v-bending process for cold rolled TRIP steels. Nuclear Engineering and Design, 2006, 236(18): 1847-1851
59 H.B. Mullan. Improved prediction of springback on final formed components. Journal ofMaterials Processing Technology, 2004, 153-154(1-3): 464-471
60 LI K P, Carden W P, Waqoner R H. Simulation of springback. International Journal of Mechanical Sciences, 2002, 44(1): 103-122
61 Waqoner R H, LI M. Simulation of springback: Through-thickness integration. International Journal of Plasticity, 2007, 23(3): 345-360
62 Meinders T, Burchitz I A, Bonte M H A, Linqbeek R A. Numerical product design: Springback prediction, compensation and optimization. International Journal of Machine Tools and Manufacture, 2008, 48(5): 499-514
63 Burchitz I A, Meinders T. Adaptive through-thickness integration for accurate springback prediction. International Journal for Numerical Methods in Engineering, 2008, 75(5): 533-554
64 Schikorra M, Govindarajan R, Brosius A, Kleiner M. Springback analysis of sheet metals regarding material hardening. Sheet Metal 2005 - Proceedings of the 11th International Conference, SheMet'05, 2005: 721-728
65 Shawn Cheng, Cao Jian. An accelerated springback compensation method. International Journal of Mechanical Sciences, 2007, 49(3): 267-279
66 Wang J F, Waqoner R H, Matlock D K, Barlat F. Anticlastic curvature in draw-bend springback. International Journal of Solids and Structures, 2005, 42(5-6): 1287-1307
67 Garcia Romeu, M Luisa, Ciurana, Joaquim. Springback and geometry prediction - Neural networks applied to the air bending process. International Conference on Intelligent Computing, ICIC 2006, 2006: 470-475
68 Peng CHEN, Koc Muammer, Wenner Michael. Experimental investigation of springback variation in forming of high strength steels. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 2008, 130(4): 0410061-0410069
69 Yi Kiyoung, Choi K K, Kim N H, Botkin Mark E. Design sensitivity analysis and optimization for minimizing springback of sheet-formed part. International Journal for Numerical Methods in Engineering, 2007, 71(12): 1483-1511
70 Liu Wenjuan, Liu Qiang, Ruan Feng, Liang Zhiyong, Qiu Hongyang. Springback prediction for sheet metal forming based on GA-ANN technology. Journal of Materials Processing Technology, 2007, 187-188: 227-231
71 Galileo. Two new sciences 1638. The MacMillan Company, New York, 1933
72余同希,张亮炽.塑性弯曲理论及其应用.北京:科学出版社, 1992, 45-86, 181-187
73 M. Huang, J. C. Gerdeen. Springback of Doubly Curved Developable Sheet Metal Surface. 22 An Overview, SAE Trans. , Section 5, 938, 718-731
74庄茁.有限元软件ABAQUS 6.4版入门指南.北京:清华大学出版社, 2004: 1-3
75 Yoshida F, Uemori T, Abe S. Modeling of large-strain cyclic plasticity for accurate springback simulation. Key Engineering Materials, 2007, (340-341): 811-816
76 Del Prete A, Primo T, De Vitis A A. Non deterministic approach in metal forming springback simulation. Sheet Metal 2007: Proceedings of the 12th International Conference, 2007: 399-410
77 Papeleus Luc, Ponthot Jean-Philippe. Finite element simulation of springback in sheet metal forming. Journal of Materials Processing Technology, 2002, 125-126: 785-791
78王耀南,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: 1
79王新成.高级数字图像处理技术.北京:中国科技出版社, 2000: 4-5
80霍宏涛.数字图像处理.北京:机械工业出版社, 2003: 4-6
81朗锐.数字图像处理学.北京:北京希望出版社, 2003: 12-13
82张兆礼,赵春晖,梅晓丹,等.现代图像处理技术及Matlab实现.北京:人民邮电出版社, 2001: 15-16
83 N.C. Gallagher, G. L. Wise. A theoretical analysis of the properties of median filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, ASSP-29: 1136-1141
84李翠华,郑南宁.图形的正则变换与快速自适应滤波.电子学报, 1999, 27(8): 52-55
85 M Petrou, P Bosdogianni. Image processing: The fundanmentals, John Wiley&Sons, LTD, 1999
86 Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing, Second Edition.北京:电子工业出版社, 2003: 3-8
87何斌,马天予. Visual C++数字图像处理.北京:人民邮电出版社, 2001: 150-152
88 T. Law, H. Itoh, H. Seki. Image filtering, edge detection and edge tracing using fuzzy reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(5): 481-491
89 P. K. Sahoo, et al. A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing, 1988, 41: 233-260
90王润生.图像理解.北京:国防科技大学出版社, 1995: 72-74
91 Yin Peng-yeng and Chen Ling-wei. A fast iterative scheme for multilevel thresholding methods. Signal Processing, 1997, 60(3): 305-313
92 N. R. Kapur and P. K. Sahoo. A new method for gray picture thresholding using the entropy of histogram. CVGIP-29, 1985, 273-285
93 N. R. Pal and S. K. Pal. A review on image segmentation techniques. Computer Vision Graphics Image Processing, 1993, 29: 1277-1294
94 Roberts L. G. Machine Perception of Three-Dimensional Solids. In Optical and Electro-Optical Information Processing, Tippet, J. T. (ed.), MIT Press, Cambridge, Mass, 1965
95 Sobel I. E. Camera Models and Machine Perception. Ph.D. dissertation, Stanford University, Palo Alto, Calif, 1970
96 Prewitt I. M. S.“Object Enhancement and Extraction.”In Picture Processing and Psychopictorics, Lipkin, B. S., and Rosenfeld, A. (eds.), Academic Press, New York. 1970
97 Marr D, Hildreth E. Theory of edge detection. Proceedings of the Royal Society of London (Series B), Biological Sciences, 1980, 207(1167): 187-217
98 Canny J. A Computational Approach for Edge Detection. IEEE Trans. Pattern Anal. Machine Intel., 1986, 8(6): 679-698
99刘奋飞,赵辉,陶卫.改进的直线拟合线阵CCD图像边缘检测方法.光电工程, 2005, 32(3):40-43
100肖晓明,刘佳,蔡自兴.图像中网格直线的检测方法与研究.计算机测量与控制, 2007, 15(10): 1292-1294
101陈震,高满屯,杨声云.基于Hough变换的直线跟踪方法.计算机应用, 2003, 23(10): 30-32
102周云燕,杨坤涛.基于RHT-LSM直线检测方法的研究.光电工程, 2007, 34(1): 55-58
103 Tsai R. Y. A versatile of ckose-range photogrammetry systems: Mathematical formulation. Photogrammetric Eng. Remote Sensing, 1975, 41(12): 1479-1486
104 J.Y. Weng, P. Cohen, M. Herniou. Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(10): 965-980
105 Abdel-Aziz Y. I., Karara H. M.. Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. In U. of Illinois Symposium on Close-Range Photogrammetry. Urbana: University of
106 R. Y. Tsai. An efficient and accurate camera calibration technique for 3D machine vision. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL,1986: 364-374
107 Z Y Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334
108 Z Y Zhang. Flexible camera calibration by viewing a plane from unknown orientations. International Conference on Computer Vision(ICCV’99), Corfu, Greece, 1999: 666-673
109霍同如,徐秉业.板材成形的计算机辅助工程系统.力学进展, 1996, 26(4): 548-557
110 K Osakada, G B Yang.Neural Networks for Process Planning of Cold Forging. Annals of the CIRP, 2005, 120: 105-176
111张大志,程秉祥,李谋渭,等.基于遗传神经网络的冷连轧机轧制压力模型.北京科技大学学报, 2003, 22(4): 284-288
112太勇.神经网络多参数诊断法及其应用研究.机械工程学报, 2002, 34(1): 100-103
113张昊.应用模糊神经网络进行负荷预测的研究.自动化报, 2005, 25(1): 60-67
114王旭东,邵惠鹤. RBF神经元网络在非线性系统建模中的应用,控制理论与应用, 1997, 14(1): 59-66
115贾财潮.一种用人工神经网络重建自由曲面的方法.中国机械工程, 2002, 9(9): 42-45
116胡小平,毛征宇,胡燕平.基于人工神经网络的一种板形反馈控制.制造业自动化, 2001, 23(3): 40-4
117赵军,官英平,郑祖伟,等.基于神经网络的材料性能参数和摩擦系数的实时识别.塑性工程学报, 2001, 8(2): 36-39
118赵军,罗亚军,曹宏强.轴对称拉深成形智能化控制过程中材料参数识别的神经网络模型.燕山大学学报, 2000, 4(2): 95-98
119张立明.人工神经网络的模型及其应用.上海:复旦大学出版社, 1993: 1-6
120徐丽娜.神经元网络控制.哈尔滨:哈尔滨工业大学出版社, 2004: 8-9
121钟诗胜,朴树学,丁刚.改进BP算法在过程神经网络中的应用.哈尔滨工业大学学报, 2006, 38(6): 840-842
122李军,丁萃菁.一种改进的BP算法在实际应用中的研究.计算机仿真, 2004, 21(2): 119-121
123姜立芳,刘泊,施莲辉.一种改进的BP算法及其在模式识别中的应用.哈尔滨理工大学学报, 2003, 8(3): 90-92
124 M. T. Hagen, M. B. Menhaj. Training Feed forward Network with the Leven berg-Marquardt Algorithm. IEEE Trans on Neural Networks, 1994, 5(6): 989-993
125司捷,周贵安,李函,等.基于提督监督学习的理论与应用(I)-基本算法.清华大学学报, 1997, 37(7): 71-73
126姬振豫.正交设计的方法与理论.北京:世界科技出版社, 2001: 21-23
127闻新. MATLAB神经网络应用设计.北京:科学出版社, 2000: 85-87
128张凯. VC++程序设计.大连:大连理工大学出版社, 2002: 1-2
129苏金明,黄国明,刘波. MATLAB与外部程序接口.北京:电子工业出版社, 2004: 174-177
130石波,陈淑珍,沈海鸥. VC与Matlab接口方法的剖析.计算机工程, 2000, 26(3): 98-100
131郭宝峰.管线钢管机械扩径工艺的数值模拟与实验研究. [燕山大学博士学位论文]. 2001: 109-110
2李晓红.我国焊管生产现状及发展趋势.钢管, 2008, 37(1): 18-21
3陈宝林.我国建设直缝埋弧焊管机组的前景.钢管, 2000, 29(2): 5-9
4李鹤林.油气输送钢管的发展动向与展望.焊管, 2004, 27(6): 1-11
5张培康.从陕京管道建设看国产埋弧螺旋焊管质量的提高.焊管, 1998, 21(4): 50-51
6尹国耀.库鄯输油管道达到90年代国际先进水平的依据.焊管, 1999, 22(3): 1-3
7陈三强,胡万志.从库鄯输油管线建设特点探讨西部石油管道发展对策.焊管, 1999, 22(1): 1-3
8冯耀荣,李鹤林.管道钢及管道钢管的研究进展与发展方向(上).石油规划设计, 2005, 16(5): 1-7
9张远生,李延丰.大口径直缝埋弧焊钢管生产线简介.焊管, 2001, 24(6): 35-37
10解培成,侯占森,于文光.大直径螺旋埋弧焊管在长输管线上应用前景.鞍山钢铁学院学报, 2002, 25(4): 273-279
11吴坚.轧钢设备及工艺-钢管生产.东北重型机械学院学报, 1984, 6: 180-197
12陈少杰.螺旋焊管成形工艺研究. [燕山大学博士学位论文]. 1997: 5-8
13张绍庆.螺旋焊接钢管的直径控制.焊管, 1995, 18(6): 34-38
14王关荣.压力容器整圆模的设计和应用.压力容器, 1991, 8(6): 40-44
15李长穆等编译.现代钢管生产.冶金工业出版社, 1982: 271-280
16潘家华.我国管道钢管的发展方向.焊管, 1998, 21(4): 16-20
17杨槐安.西气东输工程简介和管道信息.化工施工技术, 2000, 22(5): 6
18李延丰.螺旋缝埋弧焊管与直缝埋弧焊管之特点.焊管, 1998, 21(3): 1-2
19王晓香.建设大口径直缝埋弧焊管生产线可行性研究的几点说明.焊管, 1998, 21(4): 52-54
20丁大宇.大口径直缝焊管生产工艺.钢管, 1997, 26(1): 18-20
21张朝生.国外UOE电焊钢管生产技术发展现状.焊管, 1992, 15(3): 54-61
22兰兴昌.我国建设大直径直缝埋弧焊管机组的机组选型.焊管, 1998, 21(4): 41-45
23杨秀琴.关于我国钢管行业发展战略的思考(上).钢管, 2006, 35(1): 12-18
24李延丰,孙奇. JCO直缝埋弧焊钢管生产线的研发和应用.焊管, 2004, 27(6): 48-53
25郭宝峰,金淼.大口径直缝焊管UOE成形技术及发展策略.锻压技术, 2000, 25(3): 22-25
26李宏.直缝埋弧焊钢管生产线预弯工艺.焊管, 2006, 29(1): 55-57
27孙恒.机械原理.北京:高等教育出版社, 1986: 42-44
28金淼,郭宝峰,任运来,等.大口径管线钢管机械扩径工艺实验研究.锻压技术, 2001, 12(3): 44-46
29王利树,黎剑峰.焊接钢管机械扩径工艺和水压扩径工艺技术分析.焊管, 2006, 29(4): 60-63
30张远生,王占理,王晨,等.大直径焊管生产用扩径机简介.钢管, 2000, 29(6): 40-42
31 W. M. Sing, K. P. Rao. Knowledge-Based Process Layout System for Axisymmetrical Deep Drawing Using Decision Tables. Computer & Industrial Engineering, 1997, 32(2): 299-319
32 G. S. Sekhon R. Singh. An Expert System for Optimal Selection of a Press for a Sheet Metal Operation. Journal of Materials Processing Technology, 1999, 86(1-3): 131-138
33何大钧,王孝培,罗永建.筒形件拉深CAPP中模糊信息优化技术的应用.锻压技术, 2002(1): 11-14
34吕冬,丁柯,何丹农,等.基于神经网络的拉深力智能化预测系统.中国有色金属学报, 2000, 10(3): 420-425
35赵军.圆锥形件拉深成形智能化的研究. [哈尔滨工业大学博士学位论文]. 1997: 128-129
36王凤琴.盒形件拉深智能化控制关键技术的研究. [燕山大学博士学位论文]. 2003: 92-94
37官英平.板材弯曲智能化控制技术的研究. [燕山大学博士学位论文]. 2004: 85-86
38赵军,秦泗吉,曹宏强,等.轴对称曲面件智能化拉深成形过程的解析定量描述.塑性工程学报, 1998, 5(4): 47-48
39赵军,李硕本,吕炎.板材冲压成形的智能化控制技术.塑性工程学报, 1999, 6(4): 10-21
40赵军,罗亚军,官英平,等.基于神经网络的材料性能参数和摩擦系数的实时识别.塑性工程学报, 2001, 8(2): 36-39
41王凤琴,高颖,赵军.基于遗传算法的神经网络优化.燕山大学学报, 2001, 25(3): 235-239
42赵军,郑祖伟,王凤琴.轴对称件拉深成形智能化控制过程中参数实时识别的GA-ENN建模.中国机械工程, 2003, 14(1): 72-74
43王凤琴,赵军,官英平,等.方盒形件拉深破裂预测的研究.塑性工程学报, 2003, 10(6): 19-22
44 Zhao Jun, Cao Hong-qiang, Ma Li-xia. Study on intelligent control technology of deep drawing for an axis-symmetric shell part. Journal of Material Processing Technology, 2004, 151: 98-104
45官英平,赵军,苏春建.宽板V形自由弯曲智能化控制过程材料参数识别及最优工艺参数预测.机械工程学报, 2005, 41(4): 199-202
46官英平,赵军,苏春建.宽板V形自由弯曲智能化控制过程解析定量描述.塑性工程学报, 2005, 12(2): 30-33
47 Zhao Jun, Wang Fengqin. Parameter identification by neural network for intelligent deep drawing of axisymmetric workpieces. Journal of Material Processing Technology, 2005, 166: 387-391
48马丽霞,王凤琴,赵军.盒形件主要变形区的应力近似理论解析.机械工程学报, 2005, 12(7): 32-37
49赵军,苏建春,官英平,等.弯曲智能化控制系统中基于LabVIEW虚拟仪器高速数据采集的实现.制造业自动化, 2007, 29(2): 29-33
50 Ken-ichi Manabe, Kentaro Soeda. Adaptive Control Method of Deep Drawing Using the Variable Blank Holding Force Technique. Journal of the Japan Society for Technology of Plasticity, 1992, 33(375): 423-428
51马瑞.盒形件智能化拉深压边力控制规律的研究和实时预测. [燕山大学博士学位论文]. 2006: 91-92
52楊明.知能化技術.塑性と加工, 1996, 427(37): 859-860
53 Ken-ichi Manabe, M. Yang. Intelligent Technology on Sheet Metal Forming. Journal of the JSTP, 1993, 387(34): 398-404
54 H. J. Park, H. S. Cho. Fuzzy Self-Learning Control Method with Application to Hydroforming Processes. ASME Journal of Engineering for Industry, 1995, 117: 197-303
55苏春建.多角单次弯曲件回弹规律及智能化控制技术的研究. [燕山大学博士学位论文]. 2007: 132-134
56 ZHANG Z T, HU S J. Stress and Residual Stress Distributions in Plane Strain Bending. Int J Mech Sci, 1998, 40(6): 533-543
57 Qzgur Tekaslan, Ulvi Seker, Ahmet Ozdemir. Determining springback amount of steel sheet metal has 0.5mmthickness in bending dies. Materials and Design, 2006, 27: 251-258
58 Dongye Fei, Peter Hodgson. Experimental and numerical studies of springback in air v-bending process for cold rolled TRIP steels. Nuclear Engineering and Design, 2006, 236(18): 1847-1851
59 H.B. Mullan. Improved prediction of springback on final formed components. Journal ofMaterials Processing Technology, 2004, 153-154(1-3): 464-471
60 LI K P, Carden W P, Waqoner R H. Simulation of springback. International Journal of Mechanical Sciences, 2002, 44(1): 103-122
61 Waqoner R H, LI M. Simulation of springback: Through-thickness integration. International Journal of Plasticity, 2007, 23(3): 345-360
62 Meinders T, Burchitz I A, Bonte M H A, Linqbeek R A. Numerical product design: Springback prediction, compensation and optimization. International Journal of Machine Tools and Manufacture, 2008, 48(5): 499-514
63 Burchitz I A, Meinders T. Adaptive through-thickness integration for accurate springback prediction. International Journal for Numerical Methods in Engineering, 2008, 75(5): 533-554
64 Schikorra M, Govindarajan R, Brosius A, Kleiner M. Springback analysis of sheet metals regarding material hardening. Sheet Metal 2005 - Proceedings of the 11th International Conference, SheMet'05, 2005: 721-728
65 Shawn Cheng, Cao Jian. An accelerated springback compensation method. International Journal of Mechanical Sciences, 2007, 49(3): 267-279
66 Wang J F, Waqoner R H, Matlock D K, Barlat F. Anticlastic curvature in draw-bend springback. International Journal of Solids and Structures, 2005, 42(5-6): 1287-1307
67 Garcia Romeu, M Luisa, Ciurana, Joaquim. Springback and geometry prediction - Neural networks applied to the air bending process. International Conference on Intelligent Computing, ICIC 2006, 2006: 470-475
68 Peng CHEN, Koc Muammer, Wenner Michael. Experimental investigation of springback variation in forming of high strength steels. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 2008, 130(4): 0410061-0410069
69 Yi Kiyoung, Choi K K, Kim N H, Botkin Mark E. Design sensitivity analysis and optimization for minimizing springback of sheet-formed part. International Journal for Numerical Methods in Engineering, 2007, 71(12): 1483-1511
70 Liu Wenjuan, Liu Qiang, Ruan Feng, Liang Zhiyong, Qiu Hongyang. Springback prediction for sheet metal forming based on GA-ANN technology. Journal of Materials Processing Technology, 2007, 187-188: 227-231
71 Galileo. Two new sciences 1638. The MacMillan Company, New York, 1933
72余同希,张亮炽.塑性弯曲理论及其应用.北京:科学出版社, 1992, 45-86, 181-187
73 M. Huang, J. C. Gerdeen. Springback of Doubly Curved Developable Sheet Metal Surface. 22 An Overview, SAE Trans. , Section 5, 938, 718-731
74庄茁.有限元软件ABAQUS 6.4版入门指南.北京:清华大学出版社, 2004: 1-3
75 Yoshida F, Uemori T, Abe S. Modeling of large-strain cyclic plasticity for accurate springback simulation. Key Engineering Materials, 2007, (340-341): 811-816
76 Del Prete A, Primo T, De Vitis A A. Non deterministic approach in metal forming springback simulation. Sheet Metal 2007: Proceedings of the 12th International Conference, 2007: 399-410
77 Papeleus Luc, Ponthot Jean-Philippe. Finite element simulation of springback in sheet metal forming. Journal of Materials Processing Technology, 2002, 125-126: 785-791
78王耀南,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: 1
79王新成.高级数字图像处理技术.北京:中国科技出版社, 2000: 4-5
80霍宏涛.数字图像处理.北京:机械工业出版社, 2003: 4-6
81朗锐.数字图像处理学.北京:北京希望出版社, 2003: 12-13
82张兆礼,赵春晖,梅晓丹,等.现代图像处理技术及Matlab实现.北京:人民邮电出版社, 2001: 15-16
83 N.C. Gallagher, G. L. Wise. A theoretical analysis of the properties of median filters. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, ASSP-29: 1136-1141
84李翠华,郑南宁.图形的正则变换与快速自适应滤波.电子学报, 1999, 27(8): 52-55
85 M Petrou, P Bosdogianni. Image processing: The fundanmentals, John Wiley&Sons, LTD, 1999
86 Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing, Second Edition.北京:电子工业出版社, 2003: 3-8
87何斌,马天予. Visual C++数字图像处理.北京:人民邮电出版社, 2001: 150-152
88 T. Law, H. Itoh, H. Seki. Image filtering, edge detection and edge tracing using fuzzy reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(5): 481-491
89 P. K. Sahoo, et al. A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing, 1988, 41: 233-260
90王润生.图像理解.北京:国防科技大学出版社, 1995: 72-74
91 Yin Peng-yeng and Chen Ling-wei. A fast iterative scheme for multilevel thresholding methods. Signal Processing, 1997, 60(3): 305-313
92 N. R. Kapur and P. K. Sahoo. A new method for gray picture thresholding using the entropy of histogram. CVGIP-29, 1985, 273-285
93 N. R. Pal and S. K. Pal. A review on image segmentation techniques. Computer Vision Graphics Image Processing, 1993, 29: 1277-1294
94 Roberts L. G. Machine Perception of Three-Dimensional Solids. In Optical and Electro-Optical Information Processing, Tippet, J. T. (ed.), MIT Press, Cambridge, Mass, 1965
95 Sobel I. E. Camera Models and Machine Perception. Ph.D. dissertation, Stanford University, Palo Alto, Calif, 1970
96 Prewitt I. M. S.“Object Enhancement and Extraction.”In Picture Processing and Psychopictorics, Lipkin, B. S., and Rosenfeld, A. (eds.), Academic Press, New York. 1970
97 Marr D, Hildreth E. Theory of edge detection. Proceedings of the Royal Society of London (Series B), Biological Sciences, 1980, 207(1167): 187-217
98 Canny J. A Computational Approach for Edge Detection. IEEE Trans. Pattern Anal. Machine Intel., 1986, 8(6): 679-698
99刘奋飞,赵辉,陶卫.改进的直线拟合线阵CCD图像边缘检测方法.光电工程, 2005, 32(3):40-43
100肖晓明,刘佳,蔡自兴.图像中网格直线的检测方法与研究.计算机测量与控制, 2007, 15(10): 1292-1294
101陈震,高满屯,杨声云.基于Hough变换的直线跟踪方法.计算机应用, 2003, 23(10): 30-32
102周云燕,杨坤涛.基于RHT-LSM直线检测方法的研究.光电工程, 2007, 34(1): 55-58
103 Tsai R. Y. A versatile of ckose-range photogrammetry systems: Mathematical formulation. Photogrammetric Eng. Remote Sensing, 1975, 41(12): 1479-1486
104 J.Y. Weng, P. Cohen, M. Herniou. Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(10): 965-980
105 Abdel-Aziz Y. I., Karara H. M.. Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. In U. of Illinois Symposium on Close-Range Photogrammetry. Urbana: University of
106 R. Y. Tsai. An efficient and accurate camera calibration technique for 3D machine vision. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL,1986: 364-374
107 Z Y Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334
108 Z Y Zhang. Flexible camera calibration by viewing a plane from unknown orientations. International Conference on Computer Vision(ICCV’99), Corfu, Greece, 1999: 666-673
109霍同如,徐秉业.板材成形的计算机辅助工程系统.力学进展, 1996, 26(4): 548-557
110 K Osakada, G B Yang.Neural Networks for Process Planning of Cold Forging. Annals of the CIRP, 2005, 120: 105-176
111张大志,程秉祥,李谋渭,等.基于遗传神经网络的冷连轧机轧制压力模型.北京科技大学学报, 2003, 22(4): 284-288
112太勇.神经网络多参数诊断法及其应用研究.机械工程学报, 2002, 34(1): 100-103
113张昊.应用模糊神经网络进行负荷预测的研究.自动化报, 2005, 25(1): 60-67
114王旭东,邵惠鹤. RBF神经元网络在非线性系统建模中的应用,控制理论与应用, 1997, 14(1): 59-66
115贾财潮.一种用人工神经网络重建自由曲面的方法.中国机械工程, 2002, 9(9): 42-45
116胡小平,毛征宇,胡燕平.基于人工神经网络的一种板形反馈控制.制造业自动化, 2001, 23(3): 40-4
117赵军,官英平,郑祖伟,等.基于神经网络的材料性能参数和摩擦系数的实时识别.塑性工程学报, 2001, 8(2): 36-39
118赵军,罗亚军,曹宏强.轴对称拉深成形智能化控制过程中材料参数识别的神经网络模型.燕山大学学报, 2000, 4(2): 95-98
119张立明.人工神经网络的模型及其应用.上海:复旦大学出版社, 1993: 1-6
120徐丽娜.神经元网络控制.哈尔滨:哈尔滨工业大学出版社, 2004: 8-9
121钟诗胜,朴树学,丁刚.改进BP算法在过程神经网络中的应用.哈尔滨工业大学学报, 2006, 38(6): 840-842
122李军,丁萃菁.一种改进的BP算法在实际应用中的研究.计算机仿真, 2004, 21(2): 119-121
123姜立芳,刘泊,施莲辉.一种改进的BP算法及其在模式识别中的应用.哈尔滨理工大学学报, 2003, 8(3): 90-92
124 M. T. Hagen, M. B. Menhaj. Training Feed forward Network with the Leven berg-Marquardt Algorithm. IEEE Trans on Neural Networks, 1994, 5(6): 989-993
125司捷,周贵安,李函,等.基于提督监督学习的理论与应用(I)-基本算法.清华大学学报, 1997, 37(7): 71-73
126姬振豫.正交设计的方法与理论.北京:世界科技出版社, 2001: 21-23
127闻新. MATLAB神经网络应用设计.北京:科学出版社, 2000: 85-87
128张凯. VC++程序设计.大连:大连理工大学出版社, 2002: 1-2
129苏金明,黄国明,刘波. MATLAB与外部程序接口.北京:电子工业出版社, 2004: 174-177
130石波,陈淑珍,沈海鸥. VC与Matlab接口方法的剖析.计算机工程, 2000, 26(3): 98-100
131郭宝峰.管线钢管机械扩径工艺的数值模拟与实验研究. [燕山大学博士学位论文]. 2001: 109-110