平面铣削加工过程虚拟仿真系统的开发及其应用研究
|
摘要
铣削加工作为机械制造中一种常用的切削加工工艺,被广泛应用于汽车、航空及模具制造业中机械零件的粗、精加工。由于铣削加工过程中的多刃断续切削、半封闭加工以及切削厚度随时间改变等特性,使得铣削加工机理较为复杂,加工过程容易出现颤振现象。颤振是影响零件加工质量和限制铣削加工效率的主要因素,还会降低刀具的使用寿命,损害机床的安全性。
由于缺乏较为合理和实用的虚拟仿真系统对铣削加工过程动态特性的预测和振动预报,目前大多数企业在常规铣削加工(包括使用计算机数控加工系统)中,往往采用经验数据或是参考切削用量手册来选择铣削加工参数。为避免加工过程中颤振的出现及其不良影响,加工中常被迫强制改变切削加工参数,如降低切削深度或进给速度,但这却妨碍充分利用机床额定功率,导致加工工时,也即加工成本上升。在寻找解决该问题的办法中,提出一个对动态铣削加工过程进行深入、系统的理论和实验研究,开发一套实用的铣削加工过程虚拟仿真系统应是较为有效的方法。这样,在实际铣削加工之前,能够根据加工条件的变化准确地反映出铣削加工参数和刀具几何参数与铣削加工动态特性的关系,并在工艺设计阶段尽量优化加工条件,包括选择合理的刀具几何参数以及主轴转速、切削深度、切削宽度、进给量等参数。
本文正是基于上述思想,并借助于Matlab/Simulink环境,建立和研制了平面铣削加工过程虚拟仿真系统(VSS),采用离线振动控制策略对建立虚拟仿真系统各个模块的关键技术进行了深入研究。研究内容与主要成果为:
(1)以平面铣削加工动力学为研究对象,考虑铣削加工过程的再生振动效应,分析了瞬态切削厚度、刀具有效前角以及刀具偏心模型对动态切削力的影响,对传统的铣削加工动力学解析模型提出改进算法,建立实用有效的铣削加工动力学数学模型和物理模型。
(2)在改进的铣削加工动力学数学模型和物理模型的基础上,综合分析和研究仿真建模过程中的常见算法精度及其效率,确定采用变步长数值积分算法(四阶Runge—Kutta算法)和递推算法建立动态铣
Peripheral milling is broadly used for the manufacture of profiled components in aerospace, automotive and mould/die industries. The mechanism of milling process is very complex due to occurrence of the periodical and intermittent cutting process, semi-closed-form machining and chip thickness variation. The periodical cutting force excites vibration between the cutter and workpiece. Under certain conditions, the vibration with significant amplitude, or the so-called self-excited chatter, occurs due to the interaction between the cutter and workpiece. Unless avoided, machining with the presence of chatter leads to poor surface finish, low productivity, excessive tool wear even breakage or damage of machine.Cutting parameters in peripheral milling are determined usually based on either experience or reference handbooks rather than a mature virtual simulation system of milling process. As a result, sometimes it leads to low metal removal rate, low productivity and even high cost for the avoidance of chatter and its influence. To solve the problem, it is recommendable to develop an effective and practical virtual simulation system for peripheral milling on the basis of a systemic investigation on the machining dynamics. The system should be capable to demonstrate the machining dynamics of the milling process and predict the vibration between the cutter and workpiece under different cutting parameters and cutter dimensions, and to be used for optimization of the above parameters, such as spindle speed, radial and axial depth of cut, feedrate and cutter dimensions etc.Based on machining dynamics analysis, a virtual simulation system for peripheral milling(VSSPM) is hereby designed and developed using Matlab/Simulink. The main contents and conclusions of this thesis include:1. An improved practical mathematical machining dynamics model is developed with consideration of regenerative chatter theory. In particular, the size effect of uncut chip thickness, the influence of the effective rake angle and cutter runout that are usually missed in most existing models of the same sort, are included in the proposed model.2. Based on the proposed machining dynamics model, a computer simulation model for peripheral milling in time domain using Matlab/Simulink is developed by
applying variable-step numerical integral algorithm (fourth-order Runge-Kutta formula) and recurrence algorithm.3. Integrating experimental modal analysis and ARMAX model, research on system identification and modal analysis theory are conducted on establishment of transfer function models of the relevant milling vibration systems. The transfer function models of the milling vibration systems are presented consequently based on ARMAX identification model. Verified by many milling dynamics experiments and modal analysis tests, accuracy on predicting the vibration of VSSPM based on ARMAX identification model that reflects the dynamic characters of milling process is far better than that of the model based on traditional modal analysis tests.4. A series of computer simulation on the dynamics of milling process, including the dynamic forces and the vibration between the cutter and workpiece are conducted. The visual simulation results that exhibit the same trends as those obtained in actual machining, including dynamic cutting force and vibratory displacement between the cutter and workpiece and their power spectral density, demonstrate the accurate estimating capability of the proposed VSSPM and can be further used to analyze characters of milling process in time and frequency domain.5. Dynamics and phase characters of milling process are discussed on the basis of the analysis of the proposed VSSPM and energy consumed during milling. The optimal process parameters of milling and cutter geometric parameters are designed according to the minimum of amplitude of relative vibratory displacement in cutter-workpiece system.6. An optimal artificial neural networks (ANN) model with lower prediction errors and better performance function is proposed by adopting cu
由于缺乏较为合理和实用的虚拟仿真系统对铣削加工过程动态特性的预测和振动预报,目前大多数企业在常规铣削加工(包括使用计算机数控加工系统)中,往往采用经验数据或是参考切削用量手册来选择铣削加工参数。为避免加工过程中颤振的出现及其不良影响,加工中常被迫强制改变切削加工参数,如降低切削深度或进给速度,但这却妨碍充分利用机床额定功率,导致加工工时,也即加工成本上升。在寻找解决该问题的办法中,提出一个对动态铣削加工过程进行深入、系统的理论和实验研究,开发一套实用的铣削加工过程虚拟仿真系统应是较为有效的方法。这样,在实际铣削加工之前,能够根据加工条件的变化准确地反映出铣削加工参数和刀具几何参数与铣削加工动态特性的关系,并在工艺设计阶段尽量优化加工条件,包括选择合理的刀具几何参数以及主轴转速、切削深度、切削宽度、进给量等参数。
本文正是基于上述思想,并借助于Matlab/Simulink环境,建立和研制了平面铣削加工过程虚拟仿真系统(VSS),采用离线振动控制策略对建立虚拟仿真系统各个模块的关键技术进行了深入研究。研究内容与主要成果为:
(1)以平面铣削加工动力学为研究对象,考虑铣削加工过程的再生振动效应,分析了瞬态切削厚度、刀具有效前角以及刀具偏心模型对动态切削力的影响,对传统的铣削加工动力学解析模型提出改进算法,建立实用有效的铣削加工动力学数学模型和物理模型。
(2)在改进的铣削加工动力学数学模型和物理模型的基础上,综合分析和研究仿真建模过程中的常见算法精度及其效率,确定采用变步长数值积分算法(四阶Runge—Kutta算法)和递推算法建立动态铣
Peripheral milling is broadly used for the manufacture of profiled components in aerospace, automotive and mould/die industries. The mechanism of milling process is very complex due to occurrence of the periodical and intermittent cutting process, semi-closed-form machining and chip thickness variation. The periodical cutting force excites vibration between the cutter and workpiece. Under certain conditions, the vibration with significant amplitude, or the so-called self-excited chatter, occurs due to the interaction between the cutter and workpiece. Unless avoided, machining with the presence of chatter leads to poor surface finish, low productivity, excessive tool wear even breakage or damage of machine.Cutting parameters in peripheral milling are determined usually based on either experience or reference handbooks rather than a mature virtual simulation system of milling process. As a result, sometimes it leads to low metal removal rate, low productivity and even high cost for the avoidance of chatter and its influence. To solve the problem, it is recommendable to develop an effective and practical virtual simulation system for peripheral milling on the basis of a systemic investigation on the machining dynamics. The system should be capable to demonstrate the machining dynamics of the milling process and predict the vibration between the cutter and workpiece under different cutting parameters and cutter dimensions, and to be used for optimization of the above parameters, such as spindle speed, radial and axial depth of cut, feedrate and cutter dimensions etc.Based on machining dynamics analysis, a virtual simulation system for peripheral milling(VSSPM) is hereby designed and developed using Matlab/Simulink. The main contents and conclusions of this thesis include:1. An improved practical mathematical machining dynamics model is developed with consideration of regenerative chatter theory. In particular, the size effect of uncut chip thickness, the influence of the effective rake angle and cutter runout that are usually missed in most existing models of the same sort, are included in the proposed model.2. Based on the proposed machining dynamics model, a computer simulation model for peripheral milling in time domain using Matlab/Simulink is developed by
applying variable-step numerical integral algorithm (fourth-order Runge-Kutta formula) and recurrence algorithm.3. Integrating experimental modal analysis and ARMAX model, research on system identification and modal analysis theory are conducted on establishment of transfer function models of the relevant milling vibration systems. The transfer function models of the milling vibration systems are presented consequently based on ARMAX identification model. Verified by many milling dynamics experiments and modal analysis tests, accuracy on predicting the vibration of VSSPM based on ARMAX identification model that reflects the dynamic characters of milling process is far better than that of the model based on traditional modal analysis tests.4. A series of computer simulation on the dynamics of milling process, including the dynamic forces and the vibration between the cutter and workpiece are conducted. The visual simulation results that exhibit the same trends as those obtained in actual machining, including dynamic cutting force and vibratory displacement between the cutter and workpiece and their power spectral density, demonstrate the accurate estimating capability of the proposed VSSPM and can be further used to analyze characters of milling process in time and frequency domain.5. Dynamics and phase characters of milling process are discussed on the basis of the analysis of the proposed VSSPM and energy consumed during milling. The optimal process parameters of milling and cutter geometric parameters are designed according to the minimum of amplitude of relative vibratory displacement in cutter-workpiece system.6. An optimal artificial neural networks (ANN) model with lower prediction errors and better performance function is proposed by adopting cu
引文
[1] 吴雅著.机床切削系统的颤振及其控制[M].北京:科学出版社.1993年12月.
[2] F. W. Taylor. On the Art of Cutting Metals. Transaction of ASME[C]. 1907, 12: 87-93.
[3] R. N. Arnold. The Mechanism of Tool Vibration in Cutting of Steel. Proc. I. Mech. E[C]. 1946, (154): 261-284.
[4] 1944, 1-30.
[5] F. Koenigsberger, T. Tlusty, et al. Machine Tool Structure[M]. Oxford: Pergamon Press. 1970.
[6] T. R. Sisson, R. L. Kegg. An Explanation of Low Speed Chatter Effects[C]. Trans. ASME. 1969, (91): 951-958.
[7] J. Tlusty, E Ismial. Basic Non-linearity in Machining Chatter[C]. Annals of the CIRP, 1981, 30 (1): 299-304.
[8] R. S. Hahn. On the Theory of Regenerative Chatter in Precision Grinding Operations[C]. Trans. ASME. 1954, 76(1): 593-597.
[9] S. A. Tobias, W. Fishwick. Theory of Regenerative Machine Tool Chatter[J]. Engineering. London. 1958, (205): 315-321.
[10] R. Soneys, D. Brown. Dominating Parameters in Grinding Wheel and Workpiece Regenerative Chatter[C]. Process of 9th MTDR. 1968.
[11] R. A. Thompson. On the Doubly Regenerative Stability of A Grinder[C]. Trans. ASME. 1996, (1): 24-30.
[12] 星铁太郎.机械加工现象—解析对策[R].日本工业调查会.1984,1-34.
[13] 星铁太郎.机械加工振动分析[R].日本工业调查会.1990,10-20.
[14] 杨绪光,陈继武.切削过程动力学特性和机床工作稳定性分析[J].机床振动与噪声.1984,(1):17-29.
[15] 杨肃,唐恒龄,廖伯瑜著.机床动力学[M].北京:机械工业出版社.1983年6月.
[16] C. J. Hook, S. A. Tobias. Finite Amplitude Instability—A New Type of Chatter[C]. Proc. 4th MTDR. 1963, 97-109.
[17] J. Ylusty, L. Spacek. Self-Excited Vibration in Machine Tools[M]. Prague. 1954, 1-23.
[18] 土井静雄,加藤仁.旋盤主轴生(第3報)[C].日本机械学会论文集.1954,20:61-65.
[19] 周晓勤,于骏一,王文才.人工神经网络在切削颤振类别诊断中的应用[J].农业机械学报.1998,29(2):156-160.
[20] 于骏一,勾治践.滞后型切削颤振诊断技术的研究[J].振动工程学报.1995,8(2):137-143.
[21] 李沪曾,G.Spur,郭大津.机床振动学研究的历史回顾与展望[J].同济大学学报.1994,22(3):378-383.
[22] C. Andrew. Chatter in Horizontal Milling[C]. Proc Int Mech Engrs. 1965, 179(28): 877-906.
[23] H. M. Shi, Tobias. Theory of Finite Amplitude Machine Tool Instability[J]. Int J Machine Tool Design. 1984, 24(1): 45-69.
[24] S. A. Tobias. The Vibrations of Vertical Milling Machines under Test and Working Conditions[C]. Proc Int Mech Engrs. 1959, 173: 474-510.
[25] I. Minis, R. Yanushevsky, A. Tembo. Analysis of Linear and Nonlinear Chatter in Milling[C]. CIRP Annals. 1990, 39(1): 459-462.
[26] 李沪曾,G.Spur.机床切削振动仿真计算的结构动力学模型[J].同济大学学报.1995,23(5):541-546.
[27] E. Budak, Y. Altintas. Analytical Prediction of Chatter Stability in Milling. Journal of Dynamic System[J]. Measurement and Control. 1998, (120): 24-25.
[28] G. J. Lai, J. Y. Chang. Stability Analysis of Chatter Vibration for a Thin-Wall Cylindrical Workpiece[J]. International Journal of MTM. 1995, 35(3): 431-444.
[29] H. Hanna, S. A. Tobias. A Theory of Nonlinear Regenerative Chatter[J]. Transaction of the ASME. Journal of Engineer for Industry. 1974, (96): 247-255.
[30] D. W. Wu. Comprehensive Dynamic Cutting Force Model and Its Application to Wave-Removing Process[J]. Transaction of ASME. Journal of Engineer for Industry. 1988, 110 (5): 153-161.
[31] 陈花玲,戴德沛.机床切削颤振的非线性研究[J].振动工程学报.1992,5(4):24-26.
[32] X. W. Liu, K. Cheng. Prediction of Cutting Force Distribution and Its Influence on Dimensional Accuracy in Peripheral Milling[J]. International Journal of Machine Tools and Manufacture, 2002, 42: 791-800.
[33] A. Lee. Analysis of Chatter Vibration in a Cutter-Workpiece System[J]. Int. J. of MTM. 1991, 31 (2): 221-234.
[34] I. Minis, T. Yanushevsky. A New Theoretical Approach for the Predication of Machine Tool Chatter in Milling[J]. Transaction of the ASME. Journal of Engineering for Industry. 1993, 115 (1): 1-8.
[35] M. A. Baradie. Statistical Analysis of the Dynamic Cutting Coefficients and Machine Tool Stability[J]. Transaction of ASME, Journal of Engineering for Industry. 1993, 115 (5): 205-215.
[36] Y. Altintas, E. Shamoto, et al. Analytical Prediction of Stability Lobes in Ball End Milling[J]. Transaction of ASME. Journal of Manufacturing Science and Engineering. 1999, 121: 586-592.
[37] B. Y. Lee. Modeling of the Process Damping Force in Chatter Vibration[J]. Int. J. of MTM. 1995, 35(7): 951-962.
[38] M. Hashimoto, E. Marui, S. Kato. Experiment Research on Cutting Force Vibration during Primary Chatter Vibration Occurring in Plain Milling Operation[J]. Int. J. of MTM. 1996, 36 (2): 183-201.
[39] 彭泽民,徐燕申,肖友谊.机床再生颤振的频率筛分机理及其新的监控策略[J].机械工程学报.1993,29(3):98-103.
[40] M. K. Khraisheh, et al. Time Series Based Analysis for Primary Chatter in Metal Cutting[J]. Journal of Sound & Vibration. 1995, 180(1): 67-87.
[41] T. L. Subramanian, M. F. Devries, et al. An Investigation of Computer Control of Machining Chatter[J]. Trans of the ASME, Journal of Engineering for Industry. 1976, 96(4): 112-120.
[42] K. F. Eman, S. M. Wu. A Feasibility Study of On-line Identification of Chatter in Turning Operations[J]. Trans of the ASME, Journal of Engineering for Industry. 1980, (102): 315-321.
[43] 梅志坚.金属切削机床颤振的非线性理论及其计算机在线监控的研究[D].华中理工大学博士论文.1989,20-53.
[44] 姜澄宇,徐鸿钧等.切削颤振在线监测的实验研究[J].航空学报.1989,10(10):516-520.
[45] M. A. Elbestawi, et al. Process Monitoring in Milling by Pattern Recognition Mechanical System[J]. Signal Processing. 1989, 3(3): 305-315.
[46] I. N. Tansel. Modeling 3-D Cutting Dynamic with Neural Networks[J]. Int. J. of MTM. 1992, 32 (6): 829-853.
[47] R. X. Du, M. A. Elbestawi, S. Li. Tool Condition Monitoring in Turning Using Fuzzy Set Theory[J]. Int. J. of MTM. 1992, 32(6): 781-796.
[48] M. Mitsuish. Real-time Machining State Detection Using Multi-axis Force Sensing[C]. Annals of the CIRP. 1992, 41(1): 505-508.
[49] S. Smith, J. Tlusty. Stabilizing Chatter by Automatic Spindle Speed Regulation[C]. Annals of the CIRP. 1992, 41(1): 433-436.
[50] Y. Altintas, P. K. Chart. In-process Detection & Suppression of Chatter in Milling[J]. Int. J. of MTM. 1992, 32(3): 329-347.
[51] Y. S. Tarng, et al. On-line Drilling Chatter Recognition & Avoidance Using a ART2—A Neural Networks[J]. Int. J. of MTM. 1994, 34(7): 949-957.
[52] F. Ismail, E. Solimnan. A New Method for Identification of Stability Lobes in Machining[J]. Int. J. Machine Tools Manufacturing. 1997, 37(6): 763-774.
[53] 刘晓胜.面向质量控制的铣削过程状态监控与检测方法研究[D].哈尔滨工业大学博士论文.1999,12-42.
[54] S. M. Kuo, D. Vigayan. Adaptive Algorithms and Experimental Verification of Feedback Active Control Systems[J]. Noise Control Engineering. 1994, 42(2): 37-46.
[55] W. Weck, E. Verhaag, M. Gather. The Adaptive Chatter for Face-Milling Operations with Strategies for Avoiding Chatter Vibrations and for Automatic Cut Distribution[C]. Annals of the CIRP. 1975, 24(1): 405-409.
[56] 星铁太郎著,顾崇衔译.机械加工的对策与分析[M].上海:上海科技出版社,1984年11月.
[57] S. Smith, J. Tlusty. Stabilizing Chatter by Automatic Speed Regulation[C]. Annals of the CIRP. 1992, (41): 436-443.
[58] S. C. Lin, R. E. Devor, S. G. Kapoor. The Effects of Variable Speed Cutting on Vibration Control in Face Milling[J]. Trans. Of ASME. Journal of Engineering for Industry. 1990, 112 (1): 1～12.
[59] 于骏一,韩相吉,吴博达.变速切削的研究[J].机械工程学报.1998,24(4):59-62.
[60] 于骏一,杨辅伦,吴博达.变速切削的减振机理[J].机械工程学报.1995,31(6):11-16.
[61] 于骏一,吴博达,孟祥龙.变速切削过程中电机电流的变化特征[J].吉林工业大学学报.1993,23(2):15-22.
[62] L. Subramaian. An Investigation of Computer Control of Machine Chatter[C]. Journal of ASME. 1976, (4): 51-58.
[63] 杨辅伦.时变切削参数机械加工系统稳定性的研究[D].吉林工业大学博士学位论文.1994,36-50.
[64] Y. S. Liao, Y. C. Young. A New On-line Spindle Speed Regulation Strategy for Chatter Control[J]. Int. J. of MTM. 1996, 36(5): 651-660.
[65] 魏宸官,张少华.电流变技术及其工程应用[J].中国机械工程.1996,7(1):46-53.
[66] 顾仲权等编著.振动的主动控制[M].北京:国防工业出版社.1998,65-86.
[67] 师汉民,陈吉红.人工神经网络在机械工程领域的应用[J].中国机械工程.1997,8 (2):5-10.
[68] 屈梁生.人工神经网络与机械工程中的智能化问题[J].中国机械工程.1997,8(2):1-4.
[69] 王卫平.金属切削加工过程优化及控制的智能化研究[D].华南理工大学博士学位论文.1994.
[70] 李旭东.神经网络技术在切削用量选择中的应用[D].山东工业大学硕士学位论文.1998.
[71] 刘艳明,黄一夫,杨叔子.机械加工过程的神经网络最优自适应控制[J].机械工业自动化.1994,16(2):4-6.
[72] L. Monostor, et al. A Step to Intelligent Manufacturing: Modeling and Monitoring of Processes Through Artificial Neural Network[C]. Annals of the CIRP. 1993, 42(1): 485-488.
[73] V. B. Jammu, K. Danai. Unsupervised Neural Network for Tool Breakage Detection in Turning[C]. Annals of the CIRP. 1993, 42.
[74] 梁积中,雷鸣,吴雅等.加工中心镗削刀具破损检测研究[J].组合机床与自动化加工技术.1995,(2):2-5.
[75] 唐英等.刀具切削状态智能检测系统研究[J].组合机床与自动化加工技术.1995,(8):23-26.
[76] 揭景耀.智能刀具状态检测系统研究与进展[J].中国机械工程.1997,8(6):60-63.
[77] 牟建强.基于刀具可靠性切削过程优化及智能监测技术的研究[M].济南:山东工业大学出版社.1994.
[78] 柳庆,李斌,吴雅.应用人工神经网络监测切削颤振[J].制造技术与机床.1995,(12):17-19.
[79] 王民,费仁元等.镗削颤振快速预报技术研究[J].机械科学与技术.2002,21(4):520-523.
[80] 周晓勤,于骏一,王文才.人工神经网络在切削颤振类别诊断中的应用[J].农业机械学报.1998,29(2):156-160.
[81] 李旭东,孙萍等.基于人工神经网络的切削加工模拟系统[J].潍坊高等专科学校学报.2000,(1):32-36.
[82] 左力,程涛,刘艳明等.基于神经网络模糊控制器的铣削过程智能控制[J].华中理工大学学报.1998,26(2):41-44.
[83] 王新晴,唐建宁等.基于神经网络的混沌控制原理对切削颤振的模拟控制研究[J].解放军理工大学学报.2000,6(1):63-65.
[84] 王涛.基于神经网络的加工误差智能预测技术[J].航天工艺.1999,(3):8-11.
[85] 刘廷章,卢秉恒等.基于神经网络的复杂曲面加工误差控制[J].中国机械工程.1996,7(6):21-23.
[86] 倪其民,林建平,李从心等.基于人工神经网络和遗传算法的平面铣削加工参数自适应优化[J].组合机床与自动化加工技术.2000,(2):5-8.
[87] 彭观,陈统坚,张俊.切削加工参数多目标优化的神经网络方法[J].机械工艺师.1999,(2):11-12.
[88] 刘艳明,程涛,左力等.机械加工中切削用量的K—L优化研究[J].华中理工大学学报.1996,24(5):50-52.
[89] 陈统坚,彭观,张俊.铣削加工过程多目标优化专家系统[J].机械工程师.1998,1:5-5.
[90] 郭卫,王昀睿.切削参数智能仿真优化集成方法研究[J].制造业自动化.2001,23(8):25-28.
[91] M. E. Martellotti. An Analysis of the Milling Process[J]. ASME, Journal of Engineering for Industry. 1941, 63:677-700.
[92] M. E. Martellotti. An Analysis of the Milling Process, Part Ⅱ- Down Milling[J]. ASME, Journal of Engineering for Industry, 1945, 67:233-251.
[93] F. Koenigsberger, A. J. Sabberwal. Chip Section and Cutting Force during the Milling Operation[C]. Annals of the CIRP, 1961, 10:197-203.
[94] W. A. Kline, R. E. Devor. The Effect of Runout on Cutting Geometry and Forces in End Milling[J]. International Joumal of Machine Tool Design and Research. 1983, 23 (1): 123-140.
[95] W. A. Kline, R. E. Devor, J. R. Lindberg. The Prediction of Cutting Forces in End Milling with Application to Cornering Cuts[J]. International Journal of Machine Tool Design and Research. 1982, 22(1):7-22.
[96] X. W. Liu, K. Cheng, et al. Improved Dynamic Cutting Force Model in Peripheral Milling- Part Ⅰ: Theoretical Model and Simulation[J]. International Journal of Advanced Manufacturing Technology, 2002, 20:631-638.
[97] 李沪曾,于信汇等.铣削振动的计算机仿真[J].振动工程学报.2001,14(3):292-297.
[98] 陈勇,刘雄伟.在Matlab/Simulink环境下的动态铣削力仿真[J].华侨大学学报.2003,4(2):168-173.
[99] 黄田,弓占勇等.考虑几何偏心的螺旋铣刀铣削力建模方法[J].天津大学学报.1995,28(2):155-162.
[100] T. C. Ramaraj , E. Eleftheriou, Analysis of the Mechanics of Machining with Tapered End Milling Cutters[J]. Transactions ASME, Journal of Engineering for Industry, 1994,116: 398-404.
[101] R.N. Arnold. The Mechanism of Tool Vibration in the Cutting of Steel[C]. Proc Int Mech Engrs. 1946, 154:261-284.
[102] S. Doi, S. Kato. Chatter Vibration of Lathe Tools[C]. Trans ASME. 1956, 78:1127-1134.
[103] S. Smith, J. Tlusty. An Overview of Modeling and Simulation of the Milling Process[C]. ASME, Journal of Engineering for Industry. 1991, 113(2): 169-175.
[104] Y. Altintas, E. Budak. Analytical Prediction of Stability Lobes in Milling[C]. Annals of the CIRP, 1995, 44(1), 357-362.
[105] Y. Altintas. Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design[M]. Cambridge University Press. 2000.
[106] M. A. Elbestawi, F. Ismail, et al. Modeling Machining Dynamics Including Damping in the Tool-Workpiece Interface[C]. ASME, Journal of Engineering for Industry. 1994, 116(4), 435-439.
[107] F. Ismail, M. A. Elbestawi, et al. Generation of Milled Surface Including Tool Dynamics and Wear[C]. ASME, Journal of Engineering for Industry. 1993, 115(3):245-252.
[108] 刘晓胜、吴乐男.基于电流信号的铣削颤振识别技术研究[J].机械工程学报.2000.36(4):25-29.
[109]Y.Altintas著,罗学科译.数控技术与制造自动化[M].北京:化学工业出版社.2002年11月.
[110] 徐昕,李涛等编著.Matlab工具箱应用指南—控制工程篇[M].北京:电子工业出版社.2000年5月.
[111] 周露,闻新.一种改进的推广卡尔曼滤波收敛特性研究[J].系统工程与电子技术.1999,21(10):51-54.
[112] G. U. Yule. On a Method of Investigating Periodicities in Disturbed Series with Special Reference to Wotfers Spot Numbers[C]. Philo. Trans. 1927.
[113] G. M. Jenkins, D. G. Watts. Spectral Analysis and Its Applications[M]. Holden-Day. 1968.
[114] G.E.P.Box,G M.Jenkins著,顾岚译.时间序列分析、预测与控制[M].北京:中国统计出版社.1997年9月.
[115] S.M. Pandit, Wu S M. Time Series and System Analysis with Applications[M]. New York: John Wiley, 1983.50-80.
[116] 唐照千,黄文虎主编.振动与冲击手册(第一卷)[M].北京:国防工业出版社.1988年4月.
[117] 张景绘.多项式非线形系统的频率响应特性[J].力学学报.1995,27(3):316-325.
[118] 杨叔子,吴雅著.时间序列分析的工程应用[M].武汉:华中理工大学出版社.1992年4月.
[119] 傅志方,施勤忠.多维时序模型分析及其在模态分析中的应用[c].第三届全国振动理论及应用学术会议论文集.中册.1987,1220—1225.
[120] 韩光文著.辨识与参数估计[M].北京:国防工业出版社.1980年2月.
[121] 傅志方,华宏星著.模态分析理论与应用[M].上海:上海交通大学出版社.2000年7月.
[122] H.A. Akaike, H.A Bayesian. Extension of the Minimum AIC Procedure of Autoregressive Model Fitting[M]. Biometrika. 1979.
[123] 吴旭光,王新民著.计算机仿真技术与应用[M].西安:西北工业大学出版社.1998年2月.
[124] R. Sridhar, R. E. Hohn, G. W. Long. A Stability Algorithm for the General Milling Process[J]. Trans, ASME Journal of Engineer for Industry. 1968.90:330-334.
[125] 李沪曾,张国红,魏衡.多齿端铣切削振动的计算机仿真[J].同济大学学报.2000,28(1):55-59.
[126] 李沪曾,G Spur.平面端铣切削振动的实验观察与计算机仿真[J].同济大学学报.1997,25(2):224-229.
[127] 徐安平,张大卫,黄田.基于再生颤振的端铣动态铣削过程建模与仿真[J].河北工业大学学报.1998,27(1):59-66.
[128] 李锡文,杜润生,杨叔子.铣削力模型的频域特性研究[J].工具技术.2000,34(7):3-6.
[129] 徐安平,曲云霞等.带再生反馈的柔性立铣刀铣削过程模型[J].机械工程学报.1999,35(5):31-36.
[130] 徐安平,张大卫,黄田等.柔性螺旋立铣刀数控铣削表面形貌物理仿真模型[J].计算机辅助设计与图形学学报.2000.12(4):262-266.
[131] 阎兵,张大卫,徐安平等.球头铣刀铣削表面形貌建模与仿真[J].计算机辅助设计与图形学学报.2001.13(2):135-140.
[132] 张智海,郑力等 基于铣削力/力矩模型的铣削表面几何误差模型[J].机械工程学报.2001,37(1):6-10.
[133] 郑力,Steven Y, LIANG等.考虑刀具—工件—机床系统柔性的铣削表面误差模型[J]. 清华大学学报(自然科学版),1998,38(2):76-79.
[134] 施吉林,刘淑珍等编著.计算机数值方法[M].北京:高等教育出版社.1999年6月.
[135] 余得浩,汤华中编著.微分方程数值解法[M].北京:科学出版社.2003年10月.
[136] Chen T, Chen H. Approximation of Continuous Function by Neural Networks with Application to Dynamic Systems[J]. IEEE Trans. Neural Network. 1993, 4(6):910-918.
[137] 鲍晓红,贾英民.用神经网络辨识非线性系统中的网络结构选择[J].控制理论与应用.1997,14(4):489-493.
[138] 鲍晓红,贾英民.用神经网络辨识非线性系统中的模型误差分析(Ⅰ)[J].控制与决策.1997,12(5):536-541.
[139] 张邦礼,李银国,曹长修.非线性系统神经网络辨识的鲁棒BP算法[J].控制与决策.1996,11(1):22-27.
[140] 鲁宏伟,刁柏青,吴雅等.一种非线性系统参数辨识算法[J].信息与控制.1995,24(5):277-282.
[141] 王永骥,涂健编著.神经元网络控制[M].北京:机械工业出版社.1998年2月.
[142] 杨行俊,郑君里编著.人工神经网络与盲信号处N[M].北京:清华大学出版社.2003年1月.
[143] 许东,吴铮编著.基于MATLAB6.X的系统分析与设计—神经网络(第二版)[M].西安:西安电子科技大学出版社.2002年9月.
[144] D. E. Rumelhart, G. E. Hinton, R. J. Williams. Parallel Distributed Processing[M]. Cambridge, MIT Press. 1986, 1:318-362.
[145] 谢庆生,尹健等编著.机械工程中的神经网络方法[M].北京:机械工业出版社.2003年1月.
[146] R. C. Eberhart, R. W. Donbbins. Neural Network PC Tools : A Practical Guide[M]. New York: Academic Press, 1990.47-78.
[147] S. Grossberg. Neural Networks and National Intelligence MIT[M]. New York: Massachussets Press. 1988.432 - 437.
[2] F. W. Taylor. On the Art of Cutting Metals. Transaction of ASME[C]. 1907, 12: 87-93.
[3] R. N. Arnold. The Mechanism of Tool Vibration in Cutting of Steel. Proc. I. Mech. E[C]. 1946, (154): 261-284.
[4] 1944, 1-30.
[5] F. Koenigsberger, T. Tlusty, et al. Machine Tool Structure[M]. Oxford: Pergamon Press. 1970.
[6] T. R. Sisson, R. L. Kegg. An Explanation of Low Speed Chatter Effects[C]. Trans. ASME. 1969, (91): 951-958.
[7] J. Tlusty, E Ismial. Basic Non-linearity in Machining Chatter[C]. Annals of the CIRP, 1981, 30 (1): 299-304.
[8] R. S. Hahn. On the Theory of Regenerative Chatter in Precision Grinding Operations[C]. Trans. ASME. 1954, 76(1): 593-597.
[9] S. A. Tobias, W. Fishwick. Theory of Regenerative Machine Tool Chatter[J]. Engineering. London. 1958, (205): 315-321.
[10] R. Soneys, D. Brown. Dominating Parameters in Grinding Wheel and Workpiece Regenerative Chatter[C]. Process of 9th MTDR. 1968.
[11] R. A. Thompson. On the Doubly Regenerative Stability of A Grinder[C]. Trans. ASME. 1996, (1): 24-30.
[12] 星铁太郎.机械加工现象—解析对策[R].日本工业调查会.1984,1-34.
[13] 星铁太郎.机械加工振动分析[R].日本工业调查会.1990,10-20.
[14] 杨绪光,陈继武.切削过程动力学特性和机床工作稳定性分析[J].机床振动与噪声.1984,(1):17-29.
[15] 杨肃,唐恒龄,廖伯瑜著.机床动力学[M].北京:机械工业出版社.1983年6月.
[16] C. J. Hook, S. A. Tobias. Finite Amplitude Instability—A New Type of Chatter[C]. Proc. 4th MTDR. 1963, 97-109.
[17] J. Ylusty, L. Spacek. Self-Excited Vibration in Machine Tools[M]. Prague. 1954, 1-23.
[18] 土井静雄,加藤仁.旋盤主轴生(第3報)[C].日本机械学会论文集.1954,20:61-65.
[19] 周晓勤,于骏一,王文才.人工神经网络在切削颤振类别诊断中的应用[J].农业机械学报.1998,29(2):156-160.
[20] 于骏一,勾治践.滞后型切削颤振诊断技术的研究[J].振动工程学报.1995,8(2):137-143.
[21] 李沪曾,G.Spur,郭大津.机床振动学研究的历史回顾与展望[J].同济大学学报.1994,22(3):378-383.
[22] C. Andrew. Chatter in Horizontal Milling[C]. Proc Int Mech Engrs. 1965, 179(28): 877-906.
[23] H. M. Shi, Tobias. Theory of Finite Amplitude Machine Tool Instability[J]. Int J Machine Tool Design. 1984, 24(1): 45-69.
[24] S. A. Tobias. The Vibrations of Vertical Milling Machines under Test and Working Conditions[C]. Proc Int Mech Engrs. 1959, 173: 474-510.
[25] I. Minis, R. Yanushevsky, A. Tembo. Analysis of Linear and Nonlinear Chatter in Milling[C]. CIRP Annals. 1990, 39(1): 459-462.
[26] 李沪曾,G.Spur.机床切削振动仿真计算的结构动力学模型[J].同济大学学报.1995,23(5):541-546.
[27] E. Budak, Y. Altintas. Analytical Prediction of Chatter Stability in Milling. Journal of Dynamic System[J]. Measurement and Control. 1998, (120): 24-25.
[28] G. J. Lai, J. Y. Chang. Stability Analysis of Chatter Vibration for a Thin-Wall Cylindrical Workpiece[J]. International Journal of MTM. 1995, 35(3): 431-444.
[29] H. Hanna, S. A. Tobias. A Theory of Nonlinear Regenerative Chatter[J]. Transaction of the ASME. Journal of Engineer for Industry. 1974, (96): 247-255.
[30] D. W. Wu. Comprehensive Dynamic Cutting Force Model and Its Application to Wave-Removing Process[J]. Transaction of ASME. Journal of Engineer for Industry. 1988, 110 (5): 153-161.
[31] 陈花玲,戴德沛.机床切削颤振的非线性研究[J].振动工程学报.1992,5(4):24-26.
[32] X. W. Liu, K. Cheng. Prediction of Cutting Force Distribution and Its Influence on Dimensional Accuracy in Peripheral Milling[J]. International Journal of Machine Tools and Manufacture, 2002, 42: 791-800.
[33] A. Lee. Analysis of Chatter Vibration in a Cutter-Workpiece System[J]. Int. J. of MTM. 1991, 31 (2): 221-234.
[34] I. Minis, T. Yanushevsky. A New Theoretical Approach for the Predication of Machine Tool Chatter in Milling[J]. Transaction of the ASME. Journal of Engineering for Industry. 1993, 115 (1): 1-8.
[35] M. A. Baradie. Statistical Analysis of the Dynamic Cutting Coefficients and Machine Tool Stability[J]. Transaction of ASME, Journal of Engineering for Industry. 1993, 115 (5): 205-215.
[36] Y. Altintas, E. Shamoto, et al. Analytical Prediction of Stability Lobes in Ball End Milling[J]. Transaction of ASME. Journal of Manufacturing Science and Engineering. 1999, 121: 586-592.
[37] B. Y. Lee. Modeling of the Process Damping Force in Chatter Vibration[J]. Int. J. of MTM. 1995, 35(7): 951-962.
[38] M. Hashimoto, E. Marui, S. Kato. Experiment Research on Cutting Force Vibration during Primary Chatter Vibration Occurring in Plain Milling Operation[J]. Int. J. of MTM. 1996, 36 (2): 183-201.
[39] 彭泽民,徐燕申,肖友谊.机床再生颤振的频率筛分机理及其新的监控策略[J].机械工程学报.1993,29(3):98-103.
[40] M. K. Khraisheh, et al. Time Series Based Analysis for Primary Chatter in Metal Cutting[J]. Journal of Sound & Vibration. 1995, 180(1): 67-87.
[41] T. L. Subramanian, M. F. Devries, et al. An Investigation of Computer Control of Machining Chatter[J]. Trans of the ASME, Journal of Engineering for Industry. 1976, 96(4): 112-120.
[42] K. F. Eman, S. M. Wu. A Feasibility Study of On-line Identification of Chatter in Turning Operations[J]. Trans of the ASME, Journal of Engineering for Industry. 1980, (102): 315-321.
[43] 梅志坚.金属切削机床颤振的非线性理论及其计算机在线监控的研究[D].华中理工大学博士论文.1989,20-53.
[44] 姜澄宇,徐鸿钧等.切削颤振在线监测的实验研究[J].航空学报.1989,10(10):516-520.
[45] M. A. Elbestawi, et al. Process Monitoring in Milling by Pattern Recognition Mechanical System[J]. Signal Processing. 1989, 3(3): 305-315.
[46] I. N. Tansel. Modeling 3-D Cutting Dynamic with Neural Networks[J]. Int. J. of MTM. 1992, 32 (6): 829-853.
[47] R. X. Du, M. A. Elbestawi, S. Li. Tool Condition Monitoring in Turning Using Fuzzy Set Theory[J]. Int. J. of MTM. 1992, 32(6): 781-796.
[48] M. Mitsuish. Real-time Machining State Detection Using Multi-axis Force Sensing[C]. Annals of the CIRP. 1992, 41(1): 505-508.
[49] S. Smith, J. Tlusty. Stabilizing Chatter by Automatic Spindle Speed Regulation[C]. Annals of the CIRP. 1992, 41(1): 433-436.
[50] Y. Altintas, P. K. Chart. In-process Detection & Suppression of Chatter in Milling[J]. Int. J. of MTM. 1992, 32(3): 329-347.
[51] Y. S. Tarng, et al. On-line Drilling Chatter Recognition & Avoidance Using a ART2—A Neural Networks[J]. Int. J. of MTM. 1994, 34(7): 949-957.
[52] F. Ismail, E. Solimnan. A New Method for Identification of Stability Lobes in Machining[J]. Int. J. Machine Tools Manufacturing. 1997, 37(6): 763-774.
[53] 刘晓胜.面向质量控制的铣削过程状态监控与检测方法研究[D].哈尔滨工业大学博士论文.1999,12-42.
[54] S. M. Kuo, D. Vigayan. Adaptive Algorithms and Experimental Verification of Feedback Active Control Systems[J]. Noise Control Engineering. 1994, 42(2): 37-46.
[55] W. Weck, E. Verhaag, M. Gather. The Adaptive Chatter for Face-Milling Operations with Strategies for Avoiding Chatter Vibrations and for Automatic Cut Distribution[C]. Annals of the CIRP. 1975, 24(1): 405-409.
[56] 星铁太郎著,顾崇衔译.机械加工的对策与分析[M].上海:上海科技出版社,1984年11月.
[57] S. Smith, J. Tlusty. Stabilizing Chatter by Automatic Speed Regulation[C]. Annals of the CIRP. 1992, (41): 436-443.
[58] S. C. Lin, R. E. Devor, S. G. Kapoor. The Effects of Variable Speed Cutting on Vibration Control in Face Milling[J]. Trans. Of ASME. Journal of Engineering for Industry. 1990, 112 (1): 1～12.
[59] 于骏一,韩相吉,吴博达.变速切削的研究[J].机械工程学报.1998,24(4):59-62.
[60] 于骏一,杨辅伦,吴博达.变速切削的减振机理[J].机械工程学报.1995,31(6):11-16.
[61] 于骏一,吴博达,孟祥龙.变速切削过程中电机电流的变化特征[J].吉林工业大学学报.1993,23(2):15-22.
[62] L. Subramaian. An Investigation of Computer Control of Machine Chatter[C]. Journal of ASME. 1976, (4): 51-58.
[63] 杨辅伦.时变切削参数机械加工系统稳定性的研究[D].吉林工业大学博士学位论文.1994,36-50.
[64] Y. S. Liao, Y. C. Young. A New On-line Spindle Speed Regulation Strategy for Chatter Control[J]. Int. J. of MTM. 1996, 36(5): 651-660.
[65] 魏宸官,张少华.电流变技术及其工程应用[J].中国机械工程.1996,7(1):46-53.
[66] 顾仲权等编著.振动的主动控制[M].北京:国防工业出版社.1998,65-86.
[67] 师汉民,陈吉红.人工神经网络在机械工程领域的应用[J].中国机械工程.1997,8 (2):5-10.
[68] 屈梁生.人工神经网络与机械工程中的智能化问题[J].中国机械工程.1997,8(2):1-4.
[69] 王卫平.金属切削加工过程优化及控制的智能化研究[D].华南理工大学博士学位论文.1994.
[70] 李旭东.神经网络技术在切削用量选择中的应用[D].山东工业大学硕士学位论文.1998.
[71] 刘艳明,黄一夫,杨叔子.机械加工过程的神经网络最优自适应控制[J].机械工业自动化.1994,16(2):4-6.
[72] L. Monostor, et al. A Step to Intelligent Manufacturing: Modeling and Monitoring of Processes Through Artificial Neural Network[C]. Annals of the CIRP. 1993, 42(1): 485-488.
[73] V. B. Jammu, K. Danai. Unsupervised Neural Network for Tool Breakage Detection in Turning[C]. Annals of the CIRP. 1993, 42.
[74] 梁积中,雷鸣,吴雅等.加工中心镗削刀具破损检测研究[J].组合机床与自动化加工技术.1995,(2):2-5.
[75] 唐英等.刀具切削状态智能检测系统研究[J].组合机床与自动化加工技术.1995,(8):23-26.
[76] 揭景耀.智能刀具状态检测系统研究与进展[J].中国机械工程.1997,8(6):60-63.
[77] 牟建强.基于刀具可靠性切削过程优化及智能监测技术的研究[M].济南:山东工业大学出版社.1994.
[78] 柳庆,李斌,吴雅.应用人工神经网络监测切削颤振[J].制造技术与机床.1995,(12):17-19.
[79] 王民,费仁元等.镗削颤振快速预报技术研究[J].机械科学与技术.2002,21(4):520-523.
[80] 周晓勤,于骏一,王文才.人工神经网络在切削颤振类别诊断中的应用[J].农业机械学报.1998,29(2):156-160.
[81] 李旭东,孙萍等.基于人工神经网络的切削加工模拟系统[J].潍坊高等专科学校学报.2000,(1):32-36.
[82] 左力,程涛,刘艳明等.基于神经网络模糊控制器的铣削过程智能控制[J].华中理工大学学报.1998,26(2):41-44.
[83] 王新晴,唐建宁等.基于神经网络的混沌控制原理对切削颤振的模拟控制研究[J].解放军理工大学学报.2000,6(1):63-65.
[84] 王涛.基于神经网络的加工误差智能预测技术[J].航天工艺.1999,(3):8-11.
[85] 刘廷章,卢秉恒等.基于神经网络的复杂曲面加工误差控制[J].中国机械工程.1996,7(6):21-23.
[86] 倪其民,林建平,李从心等.基于人工神经网络和遗传算法的平面铣削加工参数自适应优化[J].组合机床与自动化加工技术.2000,(2):5-8.
[87] 彭观,陈统坚,张俊.切削加工参数多目标优化的神经网络方法[J].机械工艺师.1999,(2):11-12.
[88] 刘艳明,程涛,左力等.机械加工中切削用量的K—L优化研究[J].华中理工大学学报.1996,24(5):50-52.
[89] 陈统坚,彭观,张俊.铣削加工过程多目标优化专家系统[J].机械工程师.1998,1:5-5.
[90] 郭卫,王昀睿.切削参数智能仿真优化集成方法研究[J].制造业自动化.2001,23(8):25-28.
[91] M. E. Martellotti. An Analysis of the Milling Process[J]. ASME, Journal of Engineering for Industry. 1941, 63:677-700.
[92] M. E. Martellotti. An Analysis of the Milling Process, Part Ⅱ- Down Milling[J]. ASME, Journal of Engineering for Industry, 1945, 67:233-251.
[93] F. Koenigsberger, A. J. Sabberwal. Chip Section and Cutting Force during the Milling Operation[C]. Annals of the CIRP, 1961, 10:197-203.
[94] W. A. Kline, R. E. Devor. The Effect of Runout on Cutting Geometry and Forces in End Milling[J]. International Joumal of Machine Tool Design and Research. 1983, 23 (1): 123-140.
[95] W. A. Kline, R. E. Devor, J. R. Lindberg. The Prediction of Cutting Forces in End Milling with Application to Cornering Cuts[J]. International Journal of Machine Tool Design and Research. 1982, 22(1):7-22.
[96] X. W. Liu, K. Cheng, et al. Improved Dynamic Cutting Force Model in Peripheral Milling- Part Ⅰ: Theoretical Model and Simulation[J]. International Journal of Advanced Manufacturing Technology, 2002, 20:631-638.
[97] 李沪曾,于信汇等.铣削振动的计算机仿真[J].振动工程学报.2001,14(3):292-297.
[98] 陈勇,刘雄伟.在Matlab/Simulink环境下的动态铣削力仿真[J].华侨大学学报.2003,4(2):168-173.
[99] 黄田,弓占勇等.考虑几何偏心的螺旋铣刀铣削力建模方法[J].天津大学学报.1995,28(2):155-162.
[100] T. C. Ramaraj , E. Eleftheriou, Analysis of the Mechanics of Machining with Tapered End Milling Cutters[J]. Transactions ASME, Journal of Engineering for Industry, 1994,116: 398-404.
[101] R.N. Arnold. The Mechanism of Tool Vibration in the Cutting of Steel[C]. Proc Int Mech Engrs. 1946, 154:261-284.
[102] S. Doi, S. Kato. Chatter Vibration of Lathe Tools[C]. Trans ASME. 1956, 78:1127-1134.
[103] S. Smith, J. Tlusty. An Overview of Modeling and Simulation of the Milling Process[C]. ASME, Journal of Engineering for Industry. 1991, 113(2): 169-175.
[104] Y. Altintas, E. Budak. Analytical Prediction of Stability Lobes in Milling[C]. Annals of the CIRP, 1995, 44(1), 357-362.
[105] Y. Altintas. Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design[M]. Cambridge University Press. 2000.
[106] M. A. Elbestawi, F. Ismail, et al. Modeling Machining Dynamics Including Damping in the Tool-Workpiece Interface[C]. ASME, Journal of Engineering for Industry. 1994, 116(4), 435-439.
[107] F. Ismail, M. A. Elbestawi, et al. Generation of Milled Surface Including Tool Dynamics and Wear[C]. ASME, Journal of Engineering for Industry. 1993, 115(3):245-252.
[108] 刘晓胜、吴乐男.基于电流信号的铣削颤振识别技术研究[J].机械工程学报.2000.36(4):25-29.
[109]Y.Altintas著,罗学科译.数控技术与制造自动化[M].北京:化学工业出版社.2002年11月.
[110] 徐昕,李涛等编著.Matlab工具箱应用指南—控制工程篇[M].北京:电子工业出版社.2000年5月.
[111] 周露,闻新.一种改进的推广卡尔曼滤波收敛特性研究[J].系统工程与电子技术.1999,21(10):51-54.
[112] G. U. Yule. On a Method of Investigating Periodicities in Disturbed Series with Special Reference to Wotfers Spot Numbers[C]. Philo. Trans. 1927.
[113] G. M. Jenkins, D. G. Watts. Spectral Analysis and Its Applications[M]. Holden-Day. 1968.
[114] G.E.P.Box,G M.Jenkins著,顾岚译.时间序列分析、预测与控制[M].北京:中国统计出版社.1997年9月.
[115] S.M. Pandit, Wu S M. Time Series and System Analysis with Applications[M]. New York: John Wiley, 1983.50-80.
[116] 唐照千,黄文虎主编.振动与冲击手册(第一卷)[M].北京:国防工业出版社.1988年4月.
[117] 张景绘.多项式非线形系统的频率响应特性[J].力学学报.1995,27(3):316-325.
[118] 杨叔子,吴雅著.时间序列分析的工程应用[M].武汉:华中理工大学出版社.1992年4月.
[119] 傅志方,施勤忠.多维时序模型分析及其在模态分析中的应用[c].第三届全国振动理论及应用学术会议论文集.中册.1987,1220—1225.
[120] 韩光文著.辨识与参数估计[M].北京:国防工业出版社.1980年2月.
[121] 傅志方,华宏星著.模态分析理论与应用[M].上海:上海交通大学出版社.2000年7月.
[122] H.A. Akaike, H.A Bayesian. Extension of the Minimum AIC Procedure of Autoregressive Model Fitting[M]. Biometrika. 1979.
[123] 吴旭光,王新民著.计算机仿真技术与应用[M].西安:西北工业大学出版社.1998年2月.
[124] R. Sridhar, R. E. Hohn, G. W. Long. A Stability Algorithm for the General Milling Process[J]. Trans, ASME Journal of Engineer for Industry. 1968.90:330-334.
[125] 李沪曾,张国红,魏衡.多齿端铣切削振动的计算机仿真[J].同济大学学报.2000,28(1):55-59.
[126] 李沪曾,G Spur.平面端铣切削振动的实验观察与计算机仿真[J].同济大学学报.1997,25(2):224-229.
[127] 徐安平,张大卫,黄田.基于再生颤振的端铣动态铣削过程建模与仿真[J].河北工业大学学报.1998,27(1):59-66.
[128] 李锡文,杜润生,杨叔子.铣削力模型的频域特性研究[J].工具技术.2000,34(7):3-6.
[129] 徐安平,曲云霞等.带再生反馈的柔性立铣刀铣削过程模型[J].机械工程学报.1999,35(5):31-36.
[130] 徐安平,张大卫,黄田等.柔性螺旋立铣刀数控铣削表面形貌物理仿真模型[J].计算机辅助设计与图形学学报.2000.12(4):262-266.
[131] 阎兵,张大卫,徐安平等.球头铣刀铣削表面形貌建模与仿真[J].计算机辅助设计与图形学学报.2001.13(2):135-140.
[132] 张智海,郑力等 基于铣削力/力矩模型的铣削表面几何误差模型[J].机械工程学报.2001,37(1):6-10.
[133] 郑力,Steven Y, LIANG等.考虑刀具—工件—机床系统柔性的铣削表面误差模型[J]. 清华大学学报(自然科学版),1998,38(2):76-79.
[134] 施吉林,刘淑珍等编著.计算机数值方法[M].北京:高等教育出版社.1999年6月.
[135] 余得浩,汤华中编著.微分方程数值解法[M].北京:科学出版社.2003年10月.
[136] Chen T, Chen H. Approximation of Continuous Function by Neural Networks with Application to Dynamic Systems[J]. IEEE Trans. Neural Network. 1993, 4(6):910-918.
[137] 鲍晓红,贾英民.用神经网络辨识非线性系统中的网络结构选择[J].控制理论与应用.1997,14(4):489-493.
[138] 鲍晓红,贾英民.用神经网络辨识非线性系统中的模型误差分析(Ⅰ)[J].控制与决策.1997,12(5):536-541.
[139] 张邦礼,李银国,曹长修.非线性系统神经网络辨识的鲁棒BP算法[J].控制与决策.1996,11(1):22-27.
[140] 鲁宏伟,刁柏青,吴雅等.一种非线性系统参数辨识算法[J].信息与控制.1995,24(5):277-282.
[141] 王永骥,涂健编著.神经元网络控制[M].北京:机械工业出版社.1998年2月.
[142] 杨行俊,郑君里编著.人工神经网络与盲信号处N[M].北京:清华大学出版社.2003年1月.
[143] 许东,吴铮编著.基于MATLAB6.X的系统分析与设计—神经网络(第二版)[M].西安:西安电子科技大学出版社.2002年9月.
[144] D. E. Rumelhart, G. E. Hinton, R. J. Williams. Parallel Distributed Processing[M]. Cambridge, MIT Press. 1986, 1:318-362.
[145] 谢庆生,尹健等编著.机械工程中的神经网络方法[M].北京:机械工业出版社.2003年1月.
[146] R. C. Eberhart, R. W. Donbbins. Neural Network PC Tools : A Practical Guide[M]. New York: Academic Press, 1990.47-78.
[147] S. Grossberg. Neural Networks and National Intelligence MIT[M]. New York: Massachussets Press. 1988.432 - 437.