攀钢1220mm冷连轧机轧制规程优化及模型自适应研究
|
摘要
冷轧带钢生产是一个复杂的系统,从原料到成品要经过多个生产工序,这些工序之间存在着最优组合的问题。相应各机架间的衔接、匹配也存在着最优化问题。合理地制定轧制规程是保证冷连轧机优质、高产、低耗和环保的重要条件。
本文结合攀钢1220mm四机架冷连轧机冷轧带钢质量改进的项目,开展了轧制规程优化及模型自适应研究,重点进行轧制过程中摩擦系数、变形抗力、轧制力、轧制力矩和轧制功率等参数的计算和各机架压下量的分配。首先,建立冷连轧数学模型,并在结构上对数学模型进行适当的简化,使数学模型具有在线使用的价值。其次,根据BP神经网络的特点,建立BP神经网络摩擦模型,同时应用BP神经网络的在线自适应功能,提高实际生产中摩擦系数的预报精度。第三,在冷轧带钢生产中,变形抗力、轧制力、轧制力矩、轧制功率和连轧张力的预报精度受轧制状态的影响,不易精确确定。为了提高它们的预报精度,分别对变形抗力、轧制力、轧制力矩、轧制功率和连轧张力进行了模型自适应。第四,在保证轧制力能参数精度的基础上,建立合适的目标函数、制定必要约束条件并选择适当的优化方法(动态规划法)对轧制规程进行优化。
研究结果表明,简化的数学模型可以满足在线使用的需要,使机器的运算速度得以提高;BP神经网络模型对摩擦系数进行在线预报提高了摩擦系数的预报精度;对摩擦系数、变形抗力、轧制力等参数进行模型自适应,提高了轧制力能参数的预报精度;以最小轧制能耗为目标函数对轧制规程进行优化,实现了各架轧机的优化的压下量分配,节能百分比为1.22%,优化效果较为满意;以等功率裕量为目标函数对轧制规程进行优化,虽然节能效果不如前者,但各架轧机的负荷系数几近相等,实现了充分发挥电机能力的目的。
在冷连轧生产中,建立合适的数学模型并对其进行模型自适应,利用人工智能技术对轧制力能参数进行预报和模型自适应,采用优化技术对轧制规程进行优化,这对充分利用现有轧机设备和提高冷轧的控制精度具有重要的作用。
The production of cold rolling scrip is a complicated system. There must be passing through many working procedures before raw materials being finished products and it is needed to organize these procedures in an optimized way. Therefore, the connection and the match for all the stands are also an optimization problem. It is an important condition to offer a rational rolling schedule for the high quality, high yield, low energy consumption and environmental protection.
Combined with the project of improving the qualities of cold rolling scrip in four-stand tradem cold rolling mills of Pangang Iron &Steel Company, the researchs on rolling schedule optimization and adaptive model have been developed. The calculation of fiction coefficient, deformance resistance, rolling force, rolling torque and rolling power is focused on, and the distribution of thickness of every stand is also done. First, mathematical models are built up in tandem cold rolling mills and are made simple as far as possible in the structure. Therefore, they may have the value of on-line use. Second, according to neual network's characteristic, BP neural network model of friction coefficient is built up. Meanwhile, combined with the adaptive function of BP neural network model, forecast precision of friction coefficient is improved in the process of actual production. Third, forecast precisions of deformance resistance, rolling force, rolling torque, rolling power and trandem rolling tensile force are influenced by the state of rolling process, so forecast procisions of them can not be ensured easily. In order to improve their forecast procisions, adaptive models are done separately. Fourth, on the basis of ensuring the forecast procision of rolling parameters of force and energy, suitable objective functions are built up and the constraint conditions are made necessarily. Then a suitable method (dynamic programming) is chosen to optimize rolling schedules.
The findings showed that: predigested mathematical models may meet the needs of on-line use and make the speed of computer higher; Forecast precision of friction coefficient can be improved when BP neural network model is used to on-line forecast; Forecast procisions of rolling parameters of force and eneregy are improved when their adaptive models are carried on; As the objective function on minimum rolling power does optimize the rolling schedules, results are satisfied because optimized thickness of every stand is realized and energy about 1.22% is saved; As the other objective function about the abundant quantity of rolling power does do them, results are that the conserved energy is not so good as the former, but the load coefficient of every stand is close to equally, so the purpose of fully playing the electrical machinery ability has been realized.
In the process of trandem cold rolling, establish the rational mathematical models and carry on adaptive model to them; Techniques of artificial intelligence are utilized to forecast the values of rolling parameters and adaptive model are carried on; Optimization techniques are used to optimize rolling schedules. An important role will be played in fully making use of cold rolling mills and improving the cold rolling control precisions.
本文结合攀钢1220mm四机架冷连轧机冷轧带钢质量改进的项目,开展了轧制规程优化及模型自适应研究,重点进行轧制过程中摩擦系数、变形抗力、轧制力、轧制力矩和轧制功率等参数的计算和各机架压下量的分配。首先,建立冷连轧数学模型,并在结构上对数学模型进行适当的简化,使数学模型具有在线使用的价值。其次,根据BP神经网络的特点,建立BP神经网络摩擦模型,同时应用BP神经网络的在线自适应功能,提高实际生产中摩擦系数的预报精度。第三,在冷轧带钢生产中,变形抗力、轧制力、轧制力矩、轧制功率和连轧张力的预报精度受轧制状态的影响,不易精确确定。为了提高它们的预报精度,分别对变形抗力、轧制力、轧制力矩、轧制功率和连轧张力进行了模型自适应。第四,在保证轧制力能参数精度的基础上,建立合适的目标函数、制定必要约束条件并选择适当的优化方法(动态规划法)对轧制规程进行优化。
研究结果表明,简化的数学模型可以满足在线使用的需要,使机器的运算速度得以提高;BP神经网络模型对摩擦系数进行在线预报提高了摩擦系数的预报精度;对摩擦系数、变形抗力、轧制力等参数进行模型自适应,提高了轧制力能参数的预报精度;以最小轧制能耗为目标函数对轧制规程进行优化,实现了各架轧机的优化的压下量分配,节能百分比为1.22%,优化效果较为满意;以等功率裕量为目标函数对轧制规程进行优化,虽然节能效果不如前者,但各架轧机的负荷系数几近相等,实现了充分发挥电机能力的目的。
在冷连轧生产中,建立合适的数学模型并对其进行模型自适应,利用人工智能技术对轧制力能参数进行预报和模型自适应,采用优化技术对轧制规程进行优化,这对充分利用现有轧机设备和提高冷轧的控制精度具有重要的作用。
The production of cold rolling scrip is a complicated system. There must be passing through many working procedures before raw materials being finished products and it is needed to organize these procedures in an optimized way. Therefore, the connection and the match for all the stands are also an optimization problem. It is an important condition to offer a rational rolling schedule for the high quality, high yield, low energy consumption and environmental protection.
Combined with the project of improving the qualities of cold rolling scrip in four-stand tradem cold rolling mills of Pangang Iron &Steel Company, the researchs on rolling schedule optimization and adaptive model have been developed. The calculation of fiction coefficient, deformance resistance, rolling force, rolling torque and rolling power is focused on, and the distribution of thickness of every stand is also done. First, mathematical models are built up in tandem cold rolling mills and are made simple as far as possible in the structure. Therefore, they may have the value of on-line use. Second, according to neual network's characteristic, BP neural network model of friction coefficient is built up. Meanwhile, combined with the adaptive function of BP neural network model, forecast precision of friction coefficient is improved in the process of actual production. Third, forecast precisions of deformance resistance, rolling force, rolling torque, rolling power and trandem rolling tensile force are influenced by the state of rolling process, so forecast procisions of them can not be ensured easily. In order to improve their forecast procisions, adaptive models are done separately. Fourth, on the basis of ensuring the forecast procision of rolling parameters of force and energy, suitable objective functions are built up and the constraint conditions are made necessarily. Then a suitable method (dynamic programming) is chosen to optimize rolling schedules.
The findings showed that: predigested mathematical models may meet the needs of on-line use and make the speed of computer higher; Forecast precision of friction coefficient can be improved when BP neural network model is used to on-line forecast; Forecast procisions of rolling parameters of force and eneregy are improved when their adaptive models are carried on; As the objective function on minimum rolling power does optimize the rolling schedules, results are satisfied because optimized thickness of every stand is realized and energy about 1.22% is saved; As the other objective function about the abundant quantity of rolling power does do them, results are that the conserved energy is not so good as the former, but the load coefficient of every stand is close to equally, so the purpose of fully playing the electrical machinery ability has been realized.
In the process of trandem cold rolling, establish the rational mathematical models and carry on adaptive model to them; Techniques of artificial intelligence are utilized to forecast the values of rolling parameters and adaptive model are carried on; Optimization techniques are used to optimize rolling schedules. An important role will be played in fully making use of cold rolling mills and improving the cold rolling control precisions.
引文
[1]傅作宝.冷轧薄钢板生产(第2版)[M].北京:冶金工业出版社,2005
[2]赵家骏,魏立群.冷轧带钢生产问答[M].北京:冶金工业出版社,2004
[3]杨节.轧制过程数学模型[M].北京:冶金工业出版社,1983
[4]李虎兴,陈贻宏.冷轧窄带钢生产[M].北京:冶金工业出版社,1995
[5]曹鸿德.塑性变形力学基础与轧制原理[M].北京:机械工业出版社,1981
[6]WL.罗伯茨著,王延溥,潘大炜等译.冷轧带钢生产(上册)[M].北京:冶金工业出版社,1991
[7]Mohieddine Jelali.Performance assessment of control systems in rolling millsapplication to strip thickness and flatness control.Journal of Process Control,2007,17(10):805-816
[8]Jiang ZY,Zhu HT,Tieu AK.Mechanics of roll edge contact in cold rolling of thin strip.International Journal of Mechanical Sciences,2006,48(7):697-706
[9]Deltombe R,Dubar M,Duboi A,etal.A new methodology to analyze iron fines during steel cold rolling processes.Wear,2003,254(3-4):211-221
[10]Duk Man Lee,Choi SG.Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill.Engineering Appl.of Artificial Intelligence,2004,17(5):557-565
[11]Zhao HJ,Zhang YH,Hu HT.The optimized design of copper strips rolling rules by dynamic programming method.Journal of Southern Institute of Metallurgy,2001,(22):243-246
[12]华建新,王贞祥.全连续式冷连轧机过程控制[M].北京:冶金工业出版社,2000
[13]戚向东.宝钢新建板带轧机轧制规程及机型选择的研究:[博士学位论文].秦皇岛:燕山大学,2002
[14]徐雅洁.1370冷连轧机轧制规程优化设计及模型自学习研究:[硕士学位论文].秦皇岛:燕山大学,2006
[15]王延溥,齐克敏.金属塑性加工学——轧制理论与工艺(第2版)[M].北京: 冶金工业出版社,2001
[16]杨西荣,赵西成,王快社.冷轧板带规程设定方法的研究现状及发展[J].西安建筑科技大学学报,2005,37(3):403-406
[17]Larkiola J,Myllykoski P,Korhonen AS,etal.The role of neural networks in the optimisation of rolling processes.Journal of Materials Processing Technology,1998,80-81:16-23
[18]Forouzan S,Akbarzadeh A.Prediction of effect of thermo-mechanical parameters on mechanical properties and anisotropy of aluminum alloy AA3004 using artificial neural network.Materials & Design,2007,28(5):1678-1684
[19]Luis E.Zarate,Fabricio R.Bittencout.Representation and control of the cold rolling process through artificial neural networks via sensitivity factors.Journal of Materials Processing Technology,In Press,Corrected Proof,Available online 30 June 2007
[20]Jong-Yeob Jung,Yong-Taek Im,Hyung Lee-Kwang.Development of fuzzy control algorithm for shape control in cold rolling.Journal of Materials Processing Technology,1995,48(1-4):187-195
[21]Chun-yu JIA,Hong-min LIU.Fuzzy Shape Control Based on Elman Dynamic Recursion Network Prediction Model.Journal of Iron and Steel Research,2006,13(1):31-35
[22]Jong-Yeob Jung,Yong-Taek Ira,Hyung Lee-Kwang.Fuzzy-control simulation of cross-sectional shape in six-high cold-rolling mills.Journal of Materials Processing Technology,1996,62(1-3):61-69
[23]Manohar PA,Shivathaya SS,Ferry M.Design of an expert system for the optimization of steel compositions and process route.Expert Systems with Applications,1999,17(2):129-134
[24]Wang DD,Tieu AK,F.G.de Boer etal.Toward a heuristic optimum design of rolling schedules for tandem cold rolling mills.Engineering Applications of Artificial Intelligence,2000,13(4):397-406
[25]刘战英.轧制变形规程优化设计[M].北京:冶金工业出版社,1996
[26]Wang DD,Tieu AK,Lu C et al.Modeling and optimization of threading process for shape control in tandem cold rolling.Journal of Materials Processing Technology,2003,140(1-3):562-568
[27]N.Venkata Reddy,G.Suryanarayana.A set-up model for tandem cold rolling mills.Journal of Materials Processing Technology,2001,116(2-3):269-277
[28]Pires CTA,Ferreira HC,Sales RM,et al.Set-up optimization for tandem cold mills:A case study.Journal of Materials Processing Technology,2006,173(3):368-375
[29]《运筹学》教材编写组.运筹学(修订版)[M].北京:清华大学出版社,1990
[30]王国栋,刘相华.金属轧制规程过程人工智能优化[M].北京:冶金工业出版社,2000
[31]Matin T.Hagan,Howard B.Demuth,Mark H,etal,戴葵等译.神经网络设计[M].北京:机械工业出版社,2002
[32]刘利锋.多轧机轧制流程轧辊负载均衡优化:[硕士学位论文].天津:河北工业大学,2005
[33]吕立华,刘维广,马靳江等.基于EXCEL规划求解器带钢压下规程制定[J].钢铁钒钛,2005,26(3):48-52
[34]白振华,连家创,王俊飞.冷连轧机以预防打滑为目标的压下规程优化研究[J].钢铁,2003,38(3):35-38
[35]徐俊,邱格君,白振华等.冷连轧机高速轧制过程中划痕问题的研究[J].上海金属,2006,28(3):24-27
[36]李慧超,蔡庆伍,余伟等.双机架可逆冷连轧机组厚度分配途径[J].北京科技大学学报,2004,26(4):424-428
[37]胡贤磊,矫志杰,邱红雷等.综合等负荷函数法在中厚板规程分配中的应用[J].钢铁研究学报,2003,15(2):24-26
[38]张扬,余弛斌,韩双华.五机架冷连轧的轧制规程优化[J].甘肃冶金,2006,28(1):1-3
[39]Wang DD,Tieu AK.冷连轧机轧制规程调整的最佳化[C].第7届国际轧钢会议论文集.北京:冶金工业出版社,1999
[40]刘晓星.冷连轧机轧制规程的多目标模糊优化设计[J].钢铁,2000,35(8):34-36
[41]窦富萍.五机架冷连轧机轧制规程优化设计及模型自学习研究:[硕士学位论文].秦皇岛:燕山大学,2007
[42]徐秉铮,张百灵,韦岗.神经网络理论与应用[M].广州:华南理工大学出版社,1994
[43]王永庆.人工智能原理与方法[M].西安:西安交通大学出版社,1998
[44]Francisco Garcia-Cordova.A cortical network for control of voluntary movements in a robot finger.Neurocomputing,2007,71(1-3):374-391
[45]Ginoris YP,Amaral AL,Nicolau A,etal.Development of an image analysis procedure for identifying protozoa and metazoa typical of activated sludge system.Water Research,2007,41(12):2581-2589
[46]Ovunc Polat,Tulay Yildirim.Hand geometry identification without feature extraction by general regression neural network.Expert Systems with Applications,2008,34(2):845-849
[47]James N.K.Liu,Raymond W.M.Kwong.Automatic extraction and identification of chart patterns towards financial forecast.Applied Soft Computing,2007,7(4):1197-1208
[48]赵振宇,徐用懋.模糊理论和神经网络的基础与应用[M].北京:清华大学出版社,1996
[49]Lipo Wang,Yakov Frayman.A dynamically generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles.Engineering Applications of Artificial Intelligence,2002,15(6):541-550
[50]Aistleitner K,Mattersdorfer LG,Haas W,etal.Neural network for identification of roll eccentricity in rolling mills.Journal of Materials Processing Technology,1996,60(1-4):387-392
[51]Capdevila C,Garcia-Mateo C,Caballero FG,etal.Neural network analysis of the influence of processing on strength and ductility of automotive low carbon sheet steels.Computational Materials Science,2006,38(1):192-201
[52]Yan Peng,Hongmin Liu,Du R.A neural network-based shape control system for cold rolling operations.Journal of Materials Processing Technology,In Press,Corrected Proof,Available online 18 October 2007
[53]Gunasekera JS,Zhengjie Jia,Malas JC,etal.Development of a neural network model for a cold rolling process.Engineering Applications of Artificial Intelligence,1998,11(5):597-603
[54]Yang YY,Linkens DA,Talamantes-Silva J.Roll load prediction—data collection,analysis and neural network modeling.Journal of Materials Processing Technology,2004,152(3):304-315
[55]Larkiola J,Myllykoski P,Nylander J,etal.Prediction of rolling force in cold rolling by using physical models and neural computing.Journal of Materials Processing Technology,1996,60(1-4):381-386
[56]杨景明,徐雅洁,韩宗刚.神经网络预报在冷连轧规程设计中的应用[J].冶金设备,2006,155(1):7-11
[57]赵启林,刘相华,王国栋.基于BP神经网络的带钢冷轧摩擦模型[J].轧钢,2003,20(6):7-10
[58]谈芬芳.基于BP神经网络的冷轧轧制压力预报:[硕士学位论文].武汉:武汉科技大学,2005
[59]McCulloch WS,Pitts W.A logical calculus of the ideas immanent in nervous activity.Bull.Math.Biophys,1943,(5):115-133
[60]Hebb DO.The Organization of Bechavior.New York:Willey,1949
[61]Rosenblatt F.Principles of Neurodynamics.New York:Spartan,1962
[62]Widrow B,Hoff ME.Adaptive switching circuits.In:1960 IRE WESCON convention record,1960:96-104
[63]Hopfield JJ.Neural networks and physical systems with emergent collective computerational abilities.Proc.Natl.Acad.Sci,1982,(79):2554-2558
[64]Hinton GE,Sejnowski TJ,Ackley DH.Boltzmann machine:constraint satisfaction networks that learn.CMU-CS-84-119,Carnegie-Mellon Uni,1984
[65]Rumelhart DE,McClelland JL.Parallel Distributed Processing.Cambridge:MIT Press,1986
[66]Albus JS.Data storage in the cerebellar model articulation controller(CMAC).Journal of Dynamic System Measurement and Control,1975,97:228-233
[67]徐丽娜.神经网络控制[M].哈尔滨:哈尔滨工业大学出版社,1999
[68]Gudur PP,Dixit US.A neural network-assisted finite element analysis of cold flat rolling.Engineering Applications of Artificial Intelligence,2008,21(1):43-52
[69]Larkioa J,Myllykoski P,Nylandder J etal.Prediction of rolling force in cold by physical models and neural computing.Journal of Materials Processing Technology,1996,60:381-386
[70]王焱,刘景录,孙一康.基于IGA—BP网络混合模型的冷连轧机组负荷分配优化法[J].钢铁研究学报,2002,14(3):64-67
[71]黄庆学,梁爱生.高精度轧制技术[M].北京:冶金工业出版社,2002
[72]唐谋风.现代带钢冷连轧机的自动化[M].北京:冶金工业出版社,1995
[73]董德元,贺毓辛.试论轧钢理论的发展[C].轧钢理论文集.北京:冶金工业出版社,1982
[74]张进之.板带轧制工艺控制理论概要[J].中国工程科学,2001,(34):46-55
[75]Ginzburg VB.板带轧制工艺学[M].北京:冶金工业出版社,1998
[76]Freshwater IJ.Simplified theories of flat rolling——Ⅰ.The calculation of roll pressure,roll force and roll torque.International Journal of Mechanical Sciences,1996,38(6):633-648
[77]Freshwater IJ.Simplified theories of flat rolling——Ⅱ.Comparison of calculated and experimental results.International Journal of Mechanical Sciences,1996,38(6):649-660
[78]孙一康.带钢冷连轧计算机控制[M].北京:冶金工业出版社,2002
[79]焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社,1995
[80]张立明.人工神经网络的模型及其应用[M].上海:复旦大学出版社,1992
[81]施鸿宝.神经网络及其应用[M].西安:西安交通大学出版社,1993
[82]宿吉延.神经网络理论及应用[M].吉林:东北林业大学出版社,1993
[83]王伟,连家创.采用混合摩擦模型预报冷轧薄板轧制力[J].钢铁研究学报,2000,12(1):10-13
[84]Lee WH,Kwak JH,Park CJ.Study on the Adaptation Method of Control Model for Tandem Cold Rolling Mill.Proceedings of the Japan/USA symposium on Flexible Automation,1996,2(3):1035-1038
[85]Venkata RN,Suryanarayana G.A Set-Up Model for Tandem Cold Rolling Mills. Journal of Materials Processing Technology,2001,116(10):269-277
[86]WL.罗伯茨著,王延溥,潘大炜等译.冷轧带钢生产(下册)[M].北京:冶金工业出版社,1991
[87]赵解扬.冷轧板带规程设计方法与实践[J].铝加工,2002,25(3):18-23
[88]Wang JS,Jiang ZY,Tieu AK,etal.Adaptive calculation of deformation resistance model of online process control in tandem cold mill.Journal of Materials Processing Technology,2005,162-163(15):585-590
[89]Byon SM,Kim SI,Lee Y.A numerical Approach to Determine Flow StressStrain Curve of Strip and Friction Coefficient in Actual Cold Rolling mill.Journal of Materials Processing Technology,In Press,Accepted Manuscript,Available online 29 November 2007
[90]宋冀生,王曼星等.轧制时摩擦系数的研究[C].轧钢理论文集.北京:冶金工业出版社,1982
[91]Campbell McConnell,Lenard JG.Friction in cold rolling of a low carbon steel with lubricants.Journal of Materials Processing Technology,2000,99(1-3):86-93
[92]孙建林.轧制工艺润滑原理技术与应用[M].北京:冶金工业出版社,2004
[93]盛骤,谢式千,潘承毅.概率论与数理统计(第三版)[M].北京:高等教育出版社,2001
[94]李庆扬,王能超,易大义.数值分析[M].武汉:华中科技大学出版社,2001
[95]张进之,张自诚,杨美顺.冷连轧过程变形抗力和摩擦系数的非线性估算[C].轧钢理论文集.北京:冶金工业出版社,1982
[96]吴隆华,赵亚忠.冷轧变形抗力模型[C].轧钢理论文集.北京:冶金工业出版社,1982
[2]赵家骏,魏立群.冷轧带钢生产问答[M].北京:冶金工业出版社,2004
[3]杨节.轧制过程数学模型[M].北京:冶金工业出版社,1983
[4]李虎兴,陈贻宏.冷轧窄带钢生产[M].北京:冶金工业出版社,1995
[5]曹鸿德.塑性变形力学基础与轧制原理[M].北京:机械工业出版社,1981
[6]WL.罗伯茨著,王延溥,潘大炜等译.冷轧带钢生产(上册)[M].北京:冶金工业出版社,1991
[7]Mohieddine Jelali.Performance assessment of control systems in rolling millsapplication to strip thickness and flatness control.Journal of Process Control,2007,17(10):805-816
[8]Jiang ZY,Zhu HT,Tieu AK.Mechanics of roll edge contact in cold rolling of thin strip.International Journal of Mechanical Sciences,2006,48(7):697-706
[9]Deltombe R,Dubar M,Duboi A,etal.A new methodology to analyze iron fines during steel cold rolling processes.Wear,2003,254(3-4):211-221
[10]Duk Man Lee,Choi SG.Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill.Engineering Appl.of Artificial Intelligence,2004,17(5):557-565
[11]Zhao HJ,Zhang YH,Hu HT.The optimized design of copper strips rolling rules by dynamic programming method.Journal of Southern Institute of Metallurgy,2001,(22):243-246
[12]华建新,王贞祥.全连续式冷连轧机过程控制[M].北京:冶金工业出版社,2000
[13]戚向东.宝钢新建板带轧机轧制规程及机型选择的研究:[博士学位论文].秦皇岛:燕山大学,2002
[14]徐雅洁.1370冷连轧机轧制规程优化设计及模型自学习研究:[硕士学位论文].秦皇岛:燕山大学,2006
[15]王延溥,齐克敏.金属塑性加工学——轧制理论与工艺(第2版)[M].北京: 冶金工业出版社,2001
[16]杨西荣,赵西成,王快社.冷轧板带规程设定方法的研究现状及发展[J].西安建筑科技大学学报,2005,37(3):403-406
[17]Larkiola J,Myllykoski P,Korhonen AS,etal.The role of neural networks in the optimisation of rolling processes.Journal of Materials Processing Technology,1998,80-81:16-23
[18]Forouzan S,Akbarzadeh A.Prediction of effect of thermo-mechanical parameters on mechanical properties and anisotropy of aluminum alloy AA3004 using artificial neural network.Materials & Design,2007,28(5):1678-1684
[19]Luis E.Zarate,Fabricio R.Bittencout.Representation and control of the cold rolling process through artificial neural networks via sensitivity factors.Journal of Materials Processing Technology,In Press,Corrected Proof,Available online 30 June 2007
[20]Jong-Yeob Jung,Yong-Taek Im,Hyung Lee-Kwang.Development of fuzzy control algorithm for shape control in cold rolling.Journal of Materials Processing Technology,1995,48(1-4):187-195
[21]Chun-yu JIA,Hong-min LIU.Fuzzy Shape Control Based on Elman Dynamic Recursion Network Prediction Model.Journal of Iron and Steel Research,2006,13(1):31-35
[22]Jong-Yeob Jung,Yong-Taek Ira,Hyung Lee-Kwang.Fuzzy-control simulation of cross-sectional shape in six-high cold-rolling mills.Journal of Materials Processing Technology,1996,62(1-3):61-69
[23]Manohar PA,Shivathaya SS,Ferry M.Design of an expert system for the optimization of steel compositions and process route.Expert Systems with Applications,1999,17(2):129-134
[24]Wang DD,Tieu AK,F.G.de Boer etal.Toward a heuristic optimum design of rolling schedules for tandem cold rolling mills.Engineering Applications of Artificial Intelligence,2000,13(4):397-406
[25]刘战英.轧制变形规程优化设计[M].北京:冶金工业出版社,1996
[26]Wang DD,Tieu AK,Lu C et al.Modeling and optimization of threading process for shape control in tandem cold rolling.Journal of Materials Processing Technology,2003,140(1-3):562-568
[27]N.Venkata Reddy,G.Suryanarayana.A set-up model for tandem cold rolling mills.Journal of Materials Processing Technology,2001,116(2-3):269-277
[28]Pires CTA,Ferreira HC,Sales RM,et al.Set-up optimization for tandem cold mills:A case study.Journal of Materials Processing Technology,2006,173(3):368-375
[29]《运筹学》教材编写组.运筹学(修订版)[M].北京:清华大学出版社,1990
[30]王国栋,刘相华.金属轧制规程过程人工智能优化[M].北京:冶金工业出版社,2000
[31]Matin T.Hagan,Howard B.Demuth,Mark H,etal,戴葵等译.神经网络设计[M].北京:机械工业出版社,2002
[32]刘利锋.多轧机轧制流程轧辊负载均衡优化:[硕士学位论文].天津:河北工业大学,2005
[33]吕立华,刘维广,马靳江等.基于EXCEL规划求解器带钢压下规程制定[J].钢铁钒钛,2005,26(3):48-52
[34]白振华,连家创,王俊飞.冷连轧机以预防打滑为目标的压下规程优化研究[J].钢铁,2003,38(3):35-38
[35]徐俊,邱格君,白振华等.冷连轧机高速轧制过程中划痕问题的研究[J].上海金属,2006,28(3):24-27
[36]李慧超,蔡庆伍,余伟等.双机架可逆冷连轧机组厚度分配途径[J].北京科技大学学报,2004,26(4):424-428
[37]胡贤磊,矫志杰,邱红雷等.综合等负荷函数法在中厚板规程分配中的应用[J].钢铁研究学报,2003,15(2):24-26
[38]张扬,余弛斌,韩双华.五机架冷连轧的轧制规程优化[J].甘肃冶金,2006,28(1):1-3
[39]Wang DD,Tieu AK.冷连轧机轧制规程调整的最佳化[C].第7届国际轧钢会议论文集.北京:冶金工业出版社,1999
[40]刘晓星.冷连轧机轧制规程的多目标模糊优化设计[J].钢铁,2000,35(8):34-36
[41]窦富萍.五机架冷连轧机轧制规程优化设计及模型自学习研究:[硕士学位论文].秦皇岛:燕山大学,2007
[42]徐秉铮,张百灵,韦岗.神经网络理论与应用[M].广州:华南理工大学出版社,1994
[43]王永庆.人工智能原理与方法[M].西安:西安交通大学出版社,1998
[44]Francisco Garcia-Cordova.A cortical network for control of voluntary movements in a robot finger.Neurocomputing,2007,71(1-3):374-391
[45]Ginoris YP,Amaral AL,Nicolau A,etal.Development of an image analysis procedure for identifying protozoa and metazoa typical of activated sludge system.Water Research,2007,41(12):2581-2589
[46]Ovunc Polat,Tulay Yildirim.Hand geometry identification without feature extraction by general regression neural network.Expert Systems with Applications,2008,34(2):845-849
[47]James N.K.Liu,Raymond W.M.Kwong.Automatic extraction and identification of chart patterns towards financial forecast.Applied Soft Computing,2007,7(4):1197-1208
[48]赵振宇,徐用懋.模糊理论和神经网络的基础与应用[M].北京:清华大学出版社,1996
[49]Lipo Wang,Yakov Frayman.A dynamically generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles.Engineering Applications of Artificial Intelligence,2002,15(6):541-550
[50]Aistleitner K,Mattersdorfer LG,Haas W,etal.Neural network for identification of roll eccentricity in rolling mills.Journal of Materials Processing Technology,1996,60(1-4):387-392
[51]Capdevila C,Garcia-Mateo C,Caballero FG,etal.Neural network analysis of the influence of processing on strength and ductility of automotive low carbon sheet steels.Computational Materials Science,2006,38(1):192-201
[52]Yan Peng,Hongmin Liu,Du R.A neural network-based shape control system for cold rolling operations.Journal of Materials Processing Technology,In Press,Corrected Proof,Available online 18 October 2007
[53]Gunasekera JS,Zhengjie Jia,Malas JC,etal.Development of a neural network model for a cold rolling process.Engineering Applications of Artificial Intelligence,1998,11(5):597-603
[54]Yang YY,Linkens DA,Talamantes-Silva J.Roll load prediction—data collection,analysis and neural network modeling.Journal of Materials Processing Technology,2004,152(3):304-315
[55]Larkiola J,Myllykoski P,Nylander J,etal.Prediction of rolling force in cold rolling by using physical models and neural computing.Journal of Materials Processing Technology,1996,60(1-4):381-386
[56]杨景明,徐雅洁,韩宗刚.神经网络预报在冷连轧规程设计中的应用[J].冶金设备,2006,155(1):7-11
[57]赵启林,刘相华,王国栋.基于BP神经网络的带钢冷轧摩擦模型[J].轧钢,2003,20(6):7-10
[58]谈芬芳.基于BP神经网络的冷轧轧制压力预报:[硕士学位论文].武汉:武汉科技大学,2005
[59]McCulloch WS,Pitts W.A logical calculus of the ideas immanent in nervous activity.Bull.Math.Biophys,1943,(5):115-133
[60]Hebb DO.The Organization of Bechavior.New York:Willey,1949
[61]Rosenblatt F.Principles of Neurodynamics.New York:Spartan,1962
[62]Widrow B,Hoff ME.Adaptive switching circuits.In:1960 IRE WESCON convention record,1960:96-104
[63]Hopfield JJ.Neural networks and physical systems with emergent collective computerational abilities.Proc.Natl.Acad.Sci,1982,(79):2554-2558
[64]Hinton GE,Sejnowski TJ,Ackley DH.Boltzmann machine:constraint satisfaction networks that learn.CMU-CS-84-119,Carnegie-Mellon Uni,1984
[65]Rumelhart DE,McClelland JL.Parallel Distributed Processing.Cambridge:MIT Press,1986
[66]Albus JS.Data storage in the cerebellar model articulation controller(CMAC).Journal of Dynamic System Measurement and Control,1975,97:228-233
[67]徐丽娜.神经网络控制[M].哈尔滨:哈尔滨工业大学出版社,1999
[68]Gudur PP,Dixit US.A neural network-assisted finite element analysis of cold flat rolling.Engineering Applications of Artificial Intelligence,2008,21(1):43-52
[69]Larkioa J,Myllykoski P,Nylandder J etal.Prediction of rolling force in cold by physical models and neural computing.Journal of Materials Processing Technology,1996,60:381-386
[70]王焱,刘景录,孙一康.基于IGA—BP网络混合模型的冷连轧机组负荷分配优化法[J].钢铁研究学报,2002,14(3):64-67
[71]黄庆学,梁爱生.高精度轧制技术[M].北京:冶金工业出版社,2002
[72]唐谋风.现代带钢冷连轧机的自动化[M].北京:冶金工业出版社,1995
[73]董德元,贺毓辛.试论轧钢理论的发展[C].轧钢理论文集.北京:冶金工业出版社,1982
[74]张进之.板带轧制工艺控制理论概要[J].中国工程科学,2001,(34):46-55
[75]Ginzburg VB.板带轧制工艺学[M].北京:冶金工业出版社,1998
[76]Freshwater IJ.Simplified theories of flat rolling——Ⅰ.The calculation of roll pressure,roll force and roll torque.International Journal of Mechanical Sciences,1996,38(6):633-648
[77]Freshwater IJ.Simplified theories of flat rolling——Ⅱ.Comparison of calculated and experimental results.International Journal of Mechanical Sciences,1996,38(6):649-660
[78]孙一康.带钢冷连轧计算机控制[M].北京:冶金工业出版社,2002
[79]焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社,1995
[80]张立明.人工神经网络的模型及其应用[M].上海:复旦大学出版社,1992
[81]施鸿宝.神经网络及其应用[M].西安:西安交通大学出版社,1993
[82]宿吉延.神经网络理论及应用[M].吉林:东北林业大学出版社,1993
[83]王伟,连家创.采用混合摩擦模型预报冷轧薄板轧制力[J].钢铁研究学报,2000,12(1):10-13
[84]Lee WH,Kwak JH,Park CJ.Study on the Adaptation Method of Control Model for Tandem Cold Rolling Mill.Proceedings of the Japan/USA symposium on Flexible Automation,1996,2(3):1035-1038
[85]Venkata RN,Suryanarayana G.A Set-Up Model for Tandem Cold Rolling Mills. Journal of Materials Processing Technology,2001,116(10):269-277
[86]WL.罗伯茨著,王延溥,潘大炜等译.冷轧带钢生产(下册)[M].北京:冶金工业出版社,1991
[87]赵解扬.冷轧板带规程设计方法与实践[J].铝加工,2002,25(3):18-23
[88]Wang JS,Jiang ZY,Tieu AK,etal.Adaptive calculation of deformation resistance model of online process control in tandem cold mill.Journal of Materials Processing Technology,2005,162-163(15):585-590
[89]Byon SM,Kim SI,Lee Y.A numerical Approach to Determine Flow StressStrain Curve of Strip and Friction Coefficient in Actual Cold Rolling mill.Journal of Materials Processing Technology,In Press,Accepted Manuscript,Available online 29 November 2007
[90]宋冀生,王曼星等.轧制时摩擦系数的研究[C].轧钢理论文集.北京:冶金工业出版社,1982
[91]Campbell McConnell,Lenard JG.Friction in cold rolling of a low carbon steel with lubricants.Journal of Materials Processing Technology,2000,99(1-3):86-93
[92]孙建林.轧制工艺润滑原理技术与应用[M].北京:冶金工业出版社,2004
[93]盛骤,谢式千,潘承毅.概率论与数理统计(第三版)[M].北京:高等教育出版社,2001
[94]李庆扬,王能超,易大义.数值分析[M].武汉:华中科技大学出版社,2001
[95]张进之,张自诚,杨美顺.冷连轧过程变形抗力和摩擦系数的非线性估算[C].轧钢理论文集.北京:冶金工业出版社,1982
[96]吴隆华,赵亚忠.冷轧变形抗力模型[C].轧钢理论文集.北京:冶金工业出版社,1982