基于切削过程物理模型的参数优化及其数据库实现
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摘要
随着制造技术的发展,金属切削已呈现出高速高效、高加工要求和工件材料高性能等特点。为满足新形势下的加工需求,对刀具的性能也提出越来越苛刻的要求,目前刀具的发展方向为高精、高效、高可靠性和专用化。在工件材料性能和刀具技术日新月异的今天,切削加工工艺面临着前所未有的机遇和挑战,如何充分发挥机床和刀具的最大潜能,如何有效合理地选择切削参数已经成为制约切削技术发展的瓶颈。因此,新形势下的切削工艺数据比以往更具有较强的时效性,但切削过程中的力热本质是不变的,因此提出基于切削过程物理模型的参数优化问题研究。
首先,分析了切削参数优化的三个基本要素:优化变量、目标函数和约束条件。优化变量是指切削用量:切削速度和进给量,任何形式的切削参数优化最终都是以切削用量为其自变量的;目标函数则主要指表面质量、切除率和刀具寿命,以适应不同的加工要求;约束条件主要是指切削过程中的力约束、热约束和振动约束三方面,这样才能使刀具在约束许可的范围内进行高效切削。本文将针对几种典型的工况分别从三个方面展开对参数优化问题的研究:力热约束、智能方法和颤振稳定性。
切削力和切削热是反映金属切削本质最基本的物理量,同时也是难加工材料切削的突出特征。首先分析总结了斜角切削中切削力和切削热的形成。通过JC本构模型分析了金属在第一变形区内高温高应变率条件下的Mises流动应力特性;分析了刀刃钝圆所承受的应力特点及其计算方法,考虑了铣削交变应力对刀刃强度的影响,指出了每齿进给量的优化应满足刀刃的疲劳强度;为降低工件的加工硬化和刀具的高温磨损,以第二变形区的切削温度作为热约束条件,提出以材料的再结晶温度和刀具/涂层的扩散或氧化温度作为约束条件,将切削速度限制在一可行域内;另外,考查了表面粗糙度、机床功率和刀具寿命对参数优化的影响,指出参数优化的结果应是一个包含多种次优方案在内的具有一定选择范围的切削用量域,而非某个特定值。基于上述方法,以钛合金为代表的典型难加工材料为例对铣削切削速度和每齿进给量进行了优化。
对于普通金属材料的加工,此时的切削力热特征不再突出,如何针对现有的切削数据进行多目标优化提出了采用神经模糊的方法。针对预拉伸铝合金铣削表面残余应力的预测优化进行了ANFIS建模,指出:当小样本用于网络训练时,可通过采用输入选择方法以降低输入数据的维数,从而提高预测效果。应用模糊综合评判方法对不同权重的多目标参数优化进行了探讨,通过采用不同的算子对多种方案进行不同的评判及排序,得出了在现有权重向量条件下的优化值,达到了预期的切削效果。
对于弱刚性工艺系统如薄壁件加工或使用加长刀杆时,把机床、刀具、工件和夹具作为一个系统来考虑,需要对切削参数进行颤振稳定性校核。基于经典再生型切削颤振理论,首次提出了基于颤振稳定度的模糊化颤振理论。阐明了切削颤振稳定度的概念,它表明了切削系统自身保持其稳定性的能力和程度,指出切削系统从非稳定态到稳定态的过渡事实上是一个渐进的过程,即稳定度GOS属于[0,1]闭区间,提出了不同阶Lobe曲线由于曲线斜率的不同而应具有不同的过渡带宽度,并利用Sigmoid函数定义了各阶过渡带宽度的求解方法。由此,根据改进的模糊化稳定性Lobes图,以模具钢铣削为例进行了切削参数的稳定性校核。
最后,针对具体的工程应用对刀具选用及切削数据库和基于物理模型的切削参数优化决策系统进行了基于Web的系统开发。这样,才能使之能够直接为生产实践服务。
With the developing of modern manufacturing technology, high speed cutting, high efficiency cutting and high performance of work material have been the characteristics of metal cutting. In order to satisfy the situation, it puts forward rigorous demands for cutting performance of tool. So, the developing of tool are high precision, high efficiency, high reliability and specification. Thus, Cutting technology has faced much unprecedented opportunities and challenges nowdays. By now, cutting parameters optimization has been a bottleneck of cutting technology in order to exploit the maximal potentials of machine tool and cutting tool. Under this new situation, cutting parameters have shorter timeliness than ever before, but the essences of cutting process are invariable such as cutting force and cutting temperature. On the basis of this, parameter optimization based on physical models is performed here.
Firstly, three essential elements of cutting parameters optimization have been analyzed: optimization variables, objective functions and constrain conditions. Optimization variables include cutting speed and feed rate. Objective functions refer to surface quality, material removal rate and tool life. Constraint conditions include forces, heat and vibration constraint. With the constraint conditions, cutting tool would be performed efficiency in a certain range of cutting parameters. In this dissertation, according to several typical working conditions the study of cutting parameters optimization would be expanded as three respects as follows: force and heat constraint, intelligent methods and chatter stability.
Cutting forces and heat, the basic physical qualities in cutting process, is also the prominent features when difficult-to-cut material is machined. The formation of cutting forces and heat in oblique cutting is analyzed firstly. Then Mises flow stress in the primary deformation zone is studied by JC constitutive model. Stress characters and its computational methods on tool edge are studied. At the same time, the influence of alternate milling forces on cutting edge strength is discussed here. It indicates that the optimization of feed per tooth is depended on fatigue strength of tool edge. In order to reduce work hardening of workpiece and tool wear in high temperature, the cutting temperature in the secondary deformation zone is used as the thermal constraint. It indicates that cutting temperature is controlled by the range of recrystallized temperature of work material and service temperature of tool or coating. That is to say, cutting speed is limited by cutting temperature in a feasible zone. Besides, the impacts of surface quality, power consumption, and tool life to cutting parameters optimization are also studied. It shows that the result of cutting parameters optimization is not a specified value, but a range that includes several suboptimum solutions. Based on the above research, taking the difficult-to-cut material TC4 for example, the milling speed and feed per tooth are optimized in a certain range.
As for common material, neuro-fuzzy optimization method is put forward based on multiple objectives optimization when cutting force and heat are not serious. Surface residual stress in pre-stretched aluminum milling is forecasted by an established ANFIS model. When small sample is used in ANFIS training, its forecasting effect can be improved by input selection in order to reduce the dimensions of input data. Besides, multiple objective functions are optimized by fuzzy synthetic evaluation based on different weight vectors. It shows that different sorting results can be obtained by use of different operators. An optimized result is obtained under the specified weight vector. Thus the corresponding cutting parameters can be solved by BP network with Bayesian regularization.
When there are weak rigidity components in cutting process such as thin wall part, chatter stability verification to the whole system is absolutely necessary. The cutting system consists of machine tool, cutting tool, workpiece and chucks. According to conventional regenerative chatter theory, fuzzy chatter stability theory is put forward based on grade of stability (GOS). Cutting chatter stability grade represents the ability and grade of stability. In fact, the transition of cutting chatter from unstable to stable condition actually is a gradual process, that is to say, the grade of stability is in a range of a closed interval [0,1] not 0 or 1. Deeper studies on Lobes curves show that there should be different width of transition belt in different order Lobe curve because of the different Lobe slope. Thus, the method to calculate the transition belt width is defined according to Sigmoid function. Therefore, stability verification to milling parameters of die steel has been done based on the modified fuzzy stability Lobes.
At last, according to specific engineering applications, a tool selection database and cutting parameters optimization decision-making system have been developed based on Web browser. In this way, it could service practical application directly.
首先,分析了切削参数优化的三个基本要素:优化变量、目标函数和约束条件。优化变量是指切削用量:切削速度和进给量,任何形式的切削参数优化最终都是以切削用量为其自变量的;目标函数则主要指表面质量、切除率和刀具寿命,以适应不同的加工要求;约束条件主要是指切削过程中的力约束、热约束和振动约束三方面,这样才能使刀具在约束许可的范围内进行高效切削。本文将针对几种典型的工况分别从三个方面展开对参数优化问题的研究:力热约束、智能方法和颤振稳定性。
切削力和切削热是反映金属切削本质最基本的物理量,同时也是难加工材料切削的突出特征。首先分析总结了斜角切削中切削力和切削热的形成。通过JC本构模型分析了金属在第一变形区内高温高应变率条件下的Mises流动应力特性;分析了刀刃钝圆所承受的应力特点及其计算方法,考虑了铣削交变应力对刀刃强度的影响,指出了每齿进给量的优化应满足刀刃的疲劳强度;为降低工件的加工硬化和刀具的高温磨损,以第二变形区的切削温度作为热约束条件,提出以材料的再结晶温度和刀具/涂层的扩散或氧化温度作为约束条件,将切削速度限制在一可行域内;另外,考查了表面粗糙度、机床功率和刀具寿命对参数优化的影响,指出参数优化的结果应是一个包含多种次优方案在内的具有一定选择范围的切削用量域,而非某个特定值。基于上述方法,以钛合金为代表的典型难加工材料为例对铣削切削速度和每齿进给量进行了优化。
对于普通金属材料的加工,此时的切削力热特征不再突出,如何针对现有的切削数据进行多目标优化提出了采用神经模糊的方法。针对预拉伸铝合金铣削表面残余应力的预测优化进行了ANFIS建模,指出:当小样本用于网络训练时,可通过采用输入选择方法以降低输入数据的维数,从而提高预测效果。应用模糊综合评判方法对不同权重的多目标参数优化进行了探讨,通过采用不同的算子对多种方案进行不同的评判及排序,得出了在现有权重向量条件下的优化值,达到了预期的切削效果。
对于弱刚性工艺系统如薄壁件加工或使用加长刀杆时,把机床、刀具、工件和夹具作为一个系统来考虑,需要对切削参数进行颤振稳定性校核。基于经典再生型切削颤振理论,首次提出了基于颤振稳定度的模糊化颤振理论。阐明了切削颤振稳定度的概念,它表明了切削系统自身保持其稳定性的能力和程度,指出切削系统从非稳定态到稳定态的过渡事实上是一个渐进的过程,即稳定度GOS属于[0,1]闭区间,提出了不同阶Lobe曲线由于曲线斜率的不同而应具有不同的过渡带宽度,并利用Sigmoid函数定义了各阶过渡带宽度的求解方法。由此,根据改进的模糊化稳定性Lobes图,以模具钢铣削为例进行了切削参数的稳定性校核。
最后,针对具体的工程应用对刀具选用及切削数据库和基于物理模型的切削参数优化决策系统进行了基于Web的系统开发。这样,才能使之能够直接为生产实践服务。
With the developing of modern manufacturing technology, high speed cutting, high efficiency cutting and high performance of work material have been the characteristics of metal cutting. In order to satisfy the situation, it puts forward rigorous demands for cutting performance of tool. So, the developing of tool are high precision, high efficiency, high reliability and specification. Thus, Cutting technology has faced much unprecedented opportunities and challenges nowdays. By now, cutting parameters optimization has been a bottleneck of cutting technology in order to exploit the maximal potentials of machine tool and cutting tool. Under this new situation, cutting parameters have shorter timeliness than ever before, but the essences of cutting process are invariable such as cutting force and cutting temperature. On the basis of this, parameter optimization based on physical models is performed here.
Firstly, three essential elements of cutting parameters optimization have been analyzed: optimization variables, objective functions and constrain conditions. Optimization variables include cutting speed and feed rate. Objective functions refer to surface quality, material removal rate and tool life. Constraint conditions include forces, heat and vibration constraint. With the constraint conditions, cutting tool would be performed efficiency in a certain range of cutting parameters. In this dissertation, according to several typical working conditions the study of cutting parameters optimization would be expanded as three respects as follows: force and heat constraint, intelligent methods and chatter stability.
Cutting forces and heat, the basic physical qualities in cutting process, is also the prominent features when difficult-to-cut material is machined. The formation of cutting forces and heat in oblique cutting is analyzed firstly. Then Mises flow stress in the primary deformation zone is studied by JC constitutive model. Stress characters and its computational methods on tool edge are studied. At the same time, the influence of alternate milling forces on cutting edge strength is discussed here. It indicates that the optimization of feed per tooth is depended on fatigue strength of tool edge. In order to reduce work hardening of workpiece and tool wear in high temperature, the cutting temperature in the secondary deformation zone is used as the thermal constraint. It indicates that cutting temperature is controlled by the range of recrystallized temperature of work material and service temperature of tool or coating. That is to say, cutting speed is limited by cutting temperature in a feasible zone. Besides, the impacts of surface quality, power consumption, and tool life to cutting parameters optimization are also studied. It shows that the result of cutting parameters optimization is not a specified value, but a range that includes several suboptimum solutions. Based on the above research, taking the difficult-to-cut material TC4 for example, the milling speed and feed per tooth are optimized in a certain range.
As for common material, neuro-fuzzy optimization method is put forward based on multiple objectives optimization when cutting force and heat are not serious. Surface residual stress in pre-stretched aluminum milling is forecasted by an established ANFIS model. When small sample is used in ANFIS training, its forecasting effect can be improved by input selection in order to reduce the dimensions of input data. Besides, multiple objective functions are optimized by fuzzy synthetic evaluation based on different weight vectors. It shows that different sorting results can be obtained by use of different operators. An optimized result is obtained under the specified weight vector. Thus the corresponding cutting parameters can be solved by BP network with Bayesian regularization.
When there are weak rigidity components in cutting process such as thin wall part, chatter stability verification to the whole system is absolutely necessary. The cutting system consists of machine tool, cutting tool, workpiece and chucks. According to conventional regenerative chatter theory, fuzzy chatter stability theory is put forward based on grade of stability (GOS). Cutting chatter stability grade represents the ability and grade of stability. In fact, the transition of cutting chatter from unstable to stable condition actually is a gradual process, that is to say, the grade of stability is in a range of a closed interval [0,1] not 0 or 1. Deeper studies on Lobes curves show that there should be different width of transition belt in different order Lobe curve because of the different Lobe slope. Thus, the method to calculate the transition belt width is defined according to Sigmoid function. Therefore, stability verification to milling parameters of die steel has been done based on the modified fuzzy stability Lobes.
At last, according to specific engineering applications, a tool selection database and cutting parameters optimization decision-making system have been developed based on Web browser. In this way, it could service practical application directly.
引文
[1]严隽琪.制造系统信息集成技术.上海:上海交通大学出版社,2001.
[2]杨叔子,吴波.先进制造技术及其发展趋势.机械工程学报. 2003(10):73-78.
[3]杨叔子,吴波,李斌.再论先进制造技术及其发展趋势.机械工程学报. 2006(1):1-5.
[4]朱高峰.新世纪如何提高和发展我国制造业.中国制造业信息化. 2003(4):4-8.
[5]路甬祥.团结奋斗开拓创新建设制造强国.制造技术与机床. 2003(1):6-13.
[6] KAHLES J F. Machinability data requirements for advanced machining system[J]. Annals of the CIRP. 1987, 36(2): 523-529.
[7]刘战强,黄传真,万熠,艾兴.切削数据库的研究现状与发展.计算机集成制造系统—CIMS. 2003(11): 938-943.
[8]蔡建国,吴祖育.现代制造技术导论.上海:上海交通大学出版社,2000.
[9]刘钢.金属切削过程优化中多约束描述方法与应用[博士学位论文].上海:上海交通大学,2007.
[10] Franci Cus, Uros Zuperl. Approach to optimization of cutting conditions by using artificial neural networks. Journal of Materials Processing Technology. 2006, 173: 281-290.
[11]艾兴.高速切削加工技术.北京:国防工业出版社, 2003.
[12] T.W. Wright. The physics and mathematics of adiabatic shear bands. London: Cambridge University Press, 2002.
[13]叶文华,张幼桢. CIMS中切削数据优化方法的研究.组合机床与自动化技术. 1991(5): 24-27.
[14]金问林,张幼桢,严丽丽.切削过程优化问题边界极值解法.南京航空学院学报. 1992, 24(4): 370-376.
[15]李兆前,邓建新,艾兴.基于刀具可靠性的切削用量优化.机械工程学报. 1995,31(6): 6-10.
[16]郑华林,吴胜萍.切削用量的模糊优化设计.机械. 1994(6): 24-28.
[17]姜培刚.铣削用量的模糊优化设计.现代机械. 1994(3): 42-45.
[18]李旭东,黄克正,艾兴等.切削用量智能化选择的神经网络建模.组合机床与自动化加工技术. 1999(10): 13-17.
[19] B.Y., Lee, Y.S., Tarng, H.R., Lii. An Investigation of Modeling of the Machining Database in Turning Operations, Journal of Materials Processing Technology, 2000, Vol.105: 1-6.
[20] S.S., Rao, Li, Chen. Determination of Optimal Machining Conditions: a Coupled Uncertainty Model, Transactions of the ASME, Journal of Manufacturing Science and Engineering. 2000, Vol.122: 207-214.
[21] B.K., Subhas, Ramaraja, Bhat, K., Ramachandra. Simultaneous Optimization of Machining Parameters for Dimensional Instability Control in Aero Gas Turbine Components Made of Inconel 718 Alloy. Transactions of the ASME, Journal of Manufacturing Science and Engineering. 2000, Vol.122: 586-589.
[22] Q.,Meng, J.A., Arseculatatne, P., Mathew. Calculation of Optimum Cutting Conditions for Turning Operations Using a Machining Theory. International Journal of Machine Tools & Manufacture. 2000, Vol.40: 1709-1733.
[23]张颖,刘艳秋.软计算方法.北京:科学出版社,2002.
[24]师汉民,陈日曜.基因遗传算法用于切削用量优化.机械工艺师. 1992(9): 3-6.
[25]袁人炜.难加工材料高速铣削机理及工艺[博士学位论文].上海:上海交通大学,2001.
[26]陈吉红,师汉民,陈日曜.基因遗传算法用于人工神经网络的训练.华中理工大学学报. 1992(12): 215-222.
[27]靳蕃.神经计算智能基础原理方法.成都;西南交通大学出版社, 2000.
[28]仇启源.现代金属切削技术.北京:机械工业出版社,1992.
[29] M.E. Merchant. Basic mechanics of the metal cutting process. Trans. Am. Soc. Mech. Engrs. 1944, 66: A168-A175.
[30] M.E.Merchant. Mechanics of the metal cutting process. J. Appl. Phys., Engrs. 1945, 16: 267a-318b.
[31] K.J.Trigger, B.T.Chao. An analytical evaluation of metal cutting temperature. Trans. ASME, 1953, 73: 57-68.
[32] W.B.Palmer, P.L.B.Oxley. Mechanics of orthogonal mahining. Proc. Inst. Mech. Engrs. 1959, 173: 623-638.
[33] P.L.B.Oxley, A.G. Humphreys, A. Larizadeh. The influence of strain-hardening in machining. Proc. Inst. Mech. Engrs. 1961, 175: 881-891.
[34] F. Koenigsberger and A. J. P. Sabberwal. Chip section and cutting force during themilling operation Annals of CIRP. 1961, 10: 197-203.
[35] K.H., Fuh and R.M., Hwang. A predicted milling force model for high-speed end milling operation. International Journal of Machine Tool and Manufacturing. 1997, Vol.37: 969-979.
[36] G. M., Kim, P.J., Cho, and C.N., Chu. Cutting force prediction of sculptured surface ball-end milling using Z-map. International Journal of Machine Tool and Manufacturing. 2000, Vol.40: 277-291.
[37] T.I.Elwardany, M.A.Elebestawi. Cutting Temperature of Ceramic tools in HSM of Difficult-to-Cut Materials. Int. J. Mach. Tools Manufact. 1996, Vol.36(5): 611-634.
[38] Jehnming Lin, Shinn-Liang Lee, Cheng-I Weng. Estimation of Cutting Temperature in High Speed Machining. Journal of Engineering Materials and Technology. Trans. ASME. 1992, Vol.114: 289-296.
[39] Jehnming Lin. Inverse Estimation of the Tool-Work Interface Temperature in End Milling. Int. J. Mach. Tools Manufact. 1995, Vol.35(5): 751-760.
[40] Yogesh K.Potdar, Alan T.Zehnder. Measurements and simulations of temperature and deformation fields in transient metal cutting. Journal of Manufacturing Science and Engineering. 2003, Vol.125: 645-655.
[41]陈明,袁人炜.三维有限元分析在高速铣削温度研究中应用.机械工程学报. 2002, 38(7): 76-79.
[42] Iwata K, Osakada K, Terasaka T. Process modeling of orthogonal cutting by the rigid-plastic finite method. Trans ASME J Eng Master Tecnol. 1984, 106: 132-138.
[43] E.Usui, K.Maekawa, T.Shirakashi. Simulation analysis of the build-up edge formation in machining of low carbon steel. Bull. Jan. Soc. Proc. Eng. 1981, 15(4): 237-242.
[44] E.Usui, T.Shirakashi. Mechanics of machining from descriptive to predictive theory. on the Art of Cutting Metals-75 years later, ASME PED. 1982, 7: 13-35.
[45] Marc Andre Meyers著,张庆明译. Dynamic Behavior of Materials材料的动力学行为.北京:国防工业出版社,2006.
[46] G.R.Johnson and W.H.Cook. A constitutive model and data for metals subjected to large strains, high rates and high temperatures. Proceedings of the Seventh International Symposium on Ballistics. The Netherlands, The Hague. 1983: 541-547.
[47] G.R.Johnson, T.J.Holmquist. Evaluation of cylinder-impact test data for constitutive model constants. J. Appl. Phys. 1988, 64(8): 3901-3910.
[48] G.R.Johnson, W.H.Cook. Fracture characteristics of three metals subjected to various strains, strain rates, temperatures and pressures. Eng. Fract. Mech. 1985, 21(1): 31-48.
[49] F.J.Zerilli, R.W.Armstrong. Dislocation-mechanics-based constitutive relations for material dynamics calculations. J. Appl. Phys. 1987, 61(5): 1816-1825.
[50] F.J.Zerilli, R.W.Armstrong. Description of tantalum deformation behavior by dislocation mechanics based constitutive equations. J. Appl. Phys. 1990, 68(4): 1580-1591.
[51] F.J.Zerilli, R.W.Armstrong. The effect of dislocation drag on the stress-strain behavior of FCC metals. Acta. Metall. Mater. 1992, 40(8): 1803-1808.
[52] S.R.Bodner. Constitutive equations for dynamic material behavior in mechanical behavior of materials under dynamic loads. New York : Springer-Verlag, 1968: 176-190.
[53] S.R.Bodner, Y.Partom. A large deformation elastic-visco-plastic analysis of thick-walled spherical shell. ASME J. Appl. Mech. 1972, 39: 751-757.
[54] S.R.Bodner, and Y.Partom. Constitutive equations for elastic-visco-plastic strain-harding materials. ASME J. Appl. Mech. 1975, 42: 385-389.
[55] P.S.Follansbee, U.F.Kocks. A constitutive description of the deformation of copper based on the use of the mechanical threshold stress as an internal state variable. Acta. Metall. 1988, 36: 81-93.
[56]刘旭红,黄西成,陈裕泽,苏先樾,朱建士.强动载荷下金属材料塑性变形本构模型评述.力学进展. 2007,37(3): 361-374.
[57] Tobias SA, Fishwick W. A theory of regenerative chatter. Engineer, London, 1958.
[58] Tlusty J, Polacek M. The stability of machine tools against self excited vibrations in machining. Int Res Prod Eng. 1963: 465-474.
[59] Merritt HE. Theory of self-excited machine tool chatter. ASME J Eng Ind. 1965, 87: 447-454.
[60] F.Koenigsberger, J.Tlusty. Machine Tool Structures-Vol.I: Stability Against Chatter. Pergamon Press, 1967.
[61] H.Opitz, F.Bernardi. Inverstigation and Calculation of the Chatter Behavior of Lathes and Milling Machines. Annals of the CIRP, 1970, Vol. 18: 335-343.
[62] J.Tlusty, F.Ismail. Basic Nonlinearity in Machining Chatter. Annals of the CIRP. 1981, Vol.30: 21-25.
[63] J.Tlusty, Dynamics of High-Speed Milling. ASME Journal of Engineering for Industry. 1986, Vol.108(2): 59-67.
[64] S.Smith, J.Tlusty. Efficient Simulation Programs for Chatter in Milling. Annals of the CIRP. 1993, Vol.42(1): 463-466.
[65] Y.Altintas, D.Montgomery, and E.Budak. Dynamic Peripheral Milling of Flexible Structures. ASME, Journal of Engineering for Industry. 1992, Vol.114(2): 137-145.
[66] Y. Altintas and E. Budak. Analytical Prediction of Stability Lobes in Milling. Annals of the CIRP. 1995, 44(1): 357-362.
[67] Yusuf Altintas. Manufacturing Automation– Metal Cutting Mechanics, Machine Tool Vibrations and CNC Design. Cambridge: Cambridge University Press, 2002.
[68] Y.Altintas, E.Shamoto, P.Lee, etc., Analytical Prediction of Stability Lobes in Ball End Milling , Transactions of the ASME, Journal of Manufacturing Science and Engineering, 1999, Vol.121: 586~592.
[69] Y. Altintas, M. Weck. Chatter Stability of Metal Cutting and Grinding. Annals of CIRP. 2004, 53(2): 619-642.
[70] E. Budak and Y. Altintas. Analytical Prediction of Chatter Stability in Milling - Part I General Formulation. Trans. ASME Journal of Dynamics System, Measurement and Control. 1998, Vol.120: 22-30.
[71] E. Budak and Y. Altintas. Analytical Prediction of Chatter Stability in Milling - Part II Application of the General Formulation to Common Milling Systems. Trans. ASME Journal of Dynamics System, Measurement and Control, 1998, Vol.120: 31-36.
[72]彭翀,刘强,李忠群.基于Web的铣削过程颤振稳定域远程仿真系统研究.计算机工程与应用. 2006(20): 213-216.
[73]刘强,尹力.一种面向数控工艺参数优化的铣削过程动力学仿真系统研究.中国机械工程. 2005 (13) : 1146～1149.
[74]李忠群,刘强.基于MATLAB的铣削加工颤振稳定域仿真算法及实现.机械设计与制造. 2007(7): 109-111.
[75]宋清华,艾兴.高速铣削稳定性与表面加工精度研究.制造技术与机床. 2008(4): 40-43.
[76] Martin T. Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design. Boston, MA: PWS Publishing, 1996.
[77] Z. Guo, W. Sha. Modelling the correlation between processing parameters andproperties in titanium alloys using artificial neural network. Computational Materials Science. 2001, 21(3): 375-394.
[78] S. Malinov, W. Sha. Software products for modelling and simulation in materials science. Computational Materials Science. 2003, 28(2): 179-198.
[79] S. Malinov, W. Sha. Application of artificial neural networks for modelling correlations in titanium alloys. Materials Science & Engineering. 2004, 365(1-2): 202-211.
[80] Z. Guo, W. Sha. Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network. Computational Materials Science. 2004, 29(4): 12-28.
[81] J. McBride, S. Malinov, W. Sha. Modelling tensile properties of gamma-based titanium aluminides using artificial neural network. Materials Science & Engineering. 2004, 384(1-2): 129-137.
[82] D. Umbrello, G. Ambrogio, L. Filice. An ANN approach for predicting subsurface residual stresses and the desired cutting conditions during hard turning. International Journal of Materials Processing Technology. 2007, 189(1-3): 143-152.
[83] C. Chungchoo, D. Saini. On-line tool wear estimation in CNC turning operations using fuzzy neural network model. International Journal of Machine Tools & Manufacture. 2002, 42(1): 29-40.
[84] S.K. Choudhury, V.K. Jain, Ch.V.V. Rama Rao. On-line monitoring of tool wear in turning using a neural network. International Journal of Machine Tools & Manufacture. 1999, 39: 489–504.
[85] Qiang Liu, Yusuf Altintas. On-line monitoring of flank wear in turning with multilayered feed-forward neural network. International Journal of Machine Tools & Manufacture 1999, 39: 1945–1959.
[86] D.E. Dimla Sr, P.M. Lister. On-line metal cutting tool condition monitoring. II tool-state classification using multi-layer perceptron neural networks. International Journal of Machine Tools & Manufacture. 2000, 40: 769–781.
[87]陈超,徐建林,黄建龙.基于人工神经网络的刀具状态监控系统.机械工程学报. 2002(8): 135-138.
[88] Tugrul Ozel, Yigit Karpat. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools & Manufacture. 2005, 45(4-5): 467-479.
[89] C. Sanjay, M.L. Neema, C.W. Chin. Modeling of tool wear in drilling by statistical analysis and artificial neural network. International Journal of Materials Processing Technology. 2005, 170(3): 494-500.
[90]郭俊锋.神经网络及仿真在切削加工中的应用研究[硕士学位论文].兰州:兰州理工大学, 2005.
[91]周泽华.金属切削原理(第二版).上海:上海科学技术出版社, 2000.
[92]孟令锋.磨削加工切削参数的智能选择系统研究[硕士学位论文].成都:四川大学,2006.
[93]方昆凡.工程材料手册黑色金属材料卷.北京:北京出版社,2002.
[94] L.A. Zadeh. Fuzzy sets. Information and Control. 1965, Vol.8: 338-353.
[95]朱训生.工程管理的模糊分析.上海:上海交通大学出版社,2004.
[96]王锋. CIMS环境下车、铣削数据库专家系统的研究开发[博士学位论文].北京:北京理工大学,1995.
[97]庞思勤,于启勋,刘胜杨等. CIMS环境下的切削数据库和专家系统.工具技术. 2000, 34 (增刊): 98-100.
[98] WONG S V , HAMOUDA A M S. Development of genetic algorithm-based fuzzy rules design for metal cutting data selection. Robotics and Computer Integrated Manufacturing. 2002, 18: 1-12.
[99] ROWE WB , L I Y, CHEN X , et al. Case - based reasoning for selection of grinding conditions. Computer Integrated Manufacturing Systems. 1996, 9(4): 197-205.
[100]刘战强,王遵彤,万熠.基于实例推理的刀具材料选择系统的研究.中国机械工程学会2002年会.北京:机械工业出版社,2002.
[101]王遵彤,刘战强,万熠.高速切削数据库技术的研究.工具技术. 2002, 34(6):6-9.
[102]王遵彤.基于实例推理的高速切削数据库系统HISCUT的研究[博士学位论文].济南:山东大学,2003.
[103] Roger Jang. Neuro-Fuzzy and Soft Computing. New Jersey: Prentice Hall, 1997.
[104]苏宇,何宁,武凯,李亮.基于ANFIS的铝合金铣削加工表面粗糙度预测模型研究.中国机械工程. 2005(6): 475-478.
[105]孙艳红,杨兆军,李雪,张立新.基于ANFIS模糊神经网络的微钻头破损监测.润滑与密封. 2006(11): 66-70.
[106]张浩炯,余岳峰,王强.应用自适应神经模糊推理系统(ANFIS)进行建模与仿真.计算机仿真. 2002(7): 47-49.
[107]孟令锋.磨削加工切削参数的智能选择系统研究[硕士学位论文].成都:四川大学,2006.
[108] GIUSTI F, SANTOCHI M. COATS: an expert module for optimal tool selection. Annals of the CIRP. 1986 , 35(1): 337-340.
[112]王庆林,李莉敏,韦纪祥. UG铣制造过程实用指导.北京:清华大学出版社,2002.
[110] VAN LUTTERVEL T C A ,CHILDS T HC,JAWAHIR I S, et al. Present situation and future trends in modeling of machining operations. Annals of the CIRP. 1998, 47(2): 587-626.
[111] EVERSHEIM W, KALS H J J , KONIG W, et al. Tool management:the present and the future. Annals of the CIRP. 1991, 40(2): 631-639.
[112]梁吉文,张玉,郁鼎文.面向CIMS的计算机辅助刀具管理系统.工具技术. 2000. 34(2): 16-18.
[113]王涛,陈三松,刘小梅.计算机刀具管理系统的开发.工具技术. 2000, 34(9): 15-17.
[114]周文亚,边月明,张明贤.航空常用铝合金NC铣削数据库的研究与开发.航空制造技术. 1994(5): 14-16.
[115]任红丽,孙伟岩,刘献礼,等.聚晶立方氮化硼刀具切削数据库管理系统.哈尔滨理工大学学报. 2001, 6(5): 71-73,74.
[116]凡孝勇.旋转刀具/切削数据库应用研究[硕士学位论文].上海:上海交通大学, 2001.
[117]杨贵军.回转体刀具切削数据库的研究与应用[硕士学位论文].上海:上海交通大学,2001.
[118]张兴模,彭贝.大型多用户车削数据库软件CTRN90的开发及其应用.工具技术. 1992, 26(1): 21-28.
[119]叶文华.金属切削数据库系统及磨削模拟的理论与应用研究[博士学位论文].南京:南京航空航天大学,1991.
[120]周渠.涂层硬质合金刀具切削数据库[博士学位论文].北京:北京理工大学,1990.
[121]周渠,于启勋.涂层硬质合金切削数据库.硬质合金. 1992, 9(4): 223-226.
[122]赵文祥.硬质合金刀具材料切削数据库的建立与研究[博士学位论文].北京:北京理工大学,1994.
[123] OZBAYRAK M , De SOUZA R B R , BELL R. Design of a toolmanagement system for a flexible machining facility. Proceedings of the Institution of Mechanical Engineers Part B. Journal of Engineering Manufacture. 2001, 215: 353-370.
[124] ADAMCZYK Z, MAL EK H. Internet tools supporting creation and management of technological of CAD/ CAM systems. Journal of Materials Processing Technology. 1998, 76(1-3): 102-108.
[125]刘忠和.基于B/S网络的切削数据库与专家系统的开发和应用研究[博士学位论文].北京:北京理工大学,2003.
[126]刘忠和.基于Web环境的刀具切削数据库与专家系统[硕士学位论文].北京:北京理工大学,2000.
[127]王怀明.基于Web切削刀具信息系统的开发研究[硕士学位论文].北京:北京航空航天大学,2004.
[128]陈文亮.基于Web的数控切削参数管理及优化系统的研究与实现[博士学位论文].南京:南京航空航天大学,2001.
[129]刘培德.切削力学新篇.大连:大连理工大学出版社, 1991.
[130]臼井英治.切削磨削加工学.北京:机械工业出版社, 1982.
[131]陈明,袁人炜,薛秉源等.铝合金高速铣削中切削温度动态变化规律的试验研究.工具技术. 2000, 34(5): 7-10.
[132] Chen Ming, Sun Fanghong. Experimental research on the dynamic characteristics of the cutting temperature in the process of high-speed milling. Journal of Materials Processing Technology. 2003, 138: 468–471.
[133] P. Lezanski, M.C. Shaw. Tool Face Temperatures in High Speed Milling. Transactions of ASME. 1990, Vol.112: 132-135.
[134]史兴宽,杨巧凤,张明贤,陈明.钛合金TC4高速铣削表面的温度场研究.航空制造技术. 2002(1): 34-37.
[135] Milton C. Shaw. Metal Cutting Principles. New York: Oxford University Press, 2005.
[136]乐兑谦.金属切削刀具(第二版).北京:机械工业出版社,2005.
[137]周泽华.金属切削理论.北京:机械工业出版社, 1992.
[138] Stephenson, D.A. Assessment of Steady-State metal cutting Temperature Models based on simultaneous infrared and thermocouple data. Journal of Engineering for Industry. 1991, Vol.113: 121-128.
[139]王金鹏,曾攀,雷丽萍. 2024Al高温高应变率下动态塑性本构关系的实验研究.塑性工程学报. 2008, 15(3): 101-104,118.
[140]杨桂通.弹塑性力学引论.北京:清华大学出版社,2004.
[141]李同林.弹塑性力学.武汉:中国地质大学出版社,2006.
[142]张幼桢.金属切削理论.南京:航空工业出版社,1988.
[143]王桂生.钛的应用技术.长沙:中南大学出版社,2007.
[144]杨勇.钛合金航空整体结构件铣削加工变形的预测理论及方法研究[博士学位论文].杭州:浙江大学, 2007.
[145]陈日耀.金属切削原理(第二版).北京:机械工业出版社,2004.
[146]张颖,刘艳秋.软计算方法.北京:科学出版社,2002.
[147] S.P. Lo. An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology, 2003, Vol. 142: 665-675.
[148] A. Iqbal, N. He, L. Li and N.U. Dar. A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process. Expert Systems with Applications, 2007, Vol.32: 1020-1027.
[149] S. Kumanan, C.P. Jesuthanam and R.A. Kumar. Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness. The International Journal of AdvancedManufacturing Technology. 2008, Vol.35(7-8): 778-788.
[150]谢季坚,刘承平.模糊数学方法及其应用(第三版).武汉:华中科技大学出版社,2006.
[151] Liang Tian, Dynamic learning with neural networks and support vector machines [Ph.D. Dissertation]. West Virginia University, 2005.
[152] Howard B. Demuth, Mark H. Beale, Martin T. Hagan. Neural Network Toolbox User’s Guide. The MathWorks, Inc. 2007.
[153]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计.北京:清华大学出版社,2005.
[154] David J.C. MacKay. Bayesian Interpolation. Neural Computation. 1992, Vol.4: 415-447.
[155] F. Dan Foresee, Martin T. Hagan. Gauss-Newton approximation to Bayesian learning. Proceedings of the 1997 International Joint Conference on Neural Networks. 1997: 1930-1935.
[156] F. Dan Foresee. Generalization an Neural Networks [Ph.D. Dissertation]. Oklahoma State University, 1996.
[157]黄渝祥,邢爱芳.工程经济学(第三版).上海:同济大学出版社,2005.
[158]曹承志,王楠.智能技术.北京:清华大学出版社, 2004.
[159]徐孝凯.数据库技术基础教程.北京:机械工业出版社,2004.
[160]王珊,萨师暄.数据库系统概论(第四版).北京:高等教育出版社,2006.
[161] C.J. Date著,孟小峰,王珊译. An Introduction to Database System (Seventh Edition).北京:机械工业出版社,2000.
[2]杨叔子,吴波.先进制造技术及其发展趋势.机械工程学报. 2003(10):73-78.
[3]杨叔子,吴波,李斌.再论先进制造技术及其发展趋势.机械工程学报. 2006(1):1-5.
[4]朱高峰.新世纪如何提高和发展我国制造业.中国制造业信息化. 2003(4):4-8.
[5]路甬祥.团结奋斗开拓创新建设制造强国.制造技术与机床. 2003(1):6-13.
[6] KAHLES J F. Machinability data requirements for advanced machining system[J]. Annals of the CIRP. 1987, 36(2): 523-529.
[7]刘战强,黄传真,万熠,艾兴.切削数据库的研究现状与发展.计算机集成制造系统—CIMS. 2003(11): 938-943.
[8]蔡建国,吴祖育.现代制造技术导论.上海:上海交通大学出版社,2000.
[9]刘钢.金属切削过程优化中多约束描述方法与应用[博士学位论文].上海:上海交通大学,2007.
[10] Franci Cus, Uros Zuperl. Approach to optimization of cutting conditions by using artificial neural networks. Journal of Materials Processing Technology. 2006, 173: 281-290.
[11]艾兴.高速切削加工技术.北京:国防工业出版社, 2003.
[12] T.W. Wright. The physics and mathematics of adiabatic shear bands. London: Cambridge University Press, 2002.
[13]叶文华,张幼桢. CIMS中切削数据优化方法的研究.组合机床与自动化技术. 1991(5): 24-27.
[14]金问林,张幼桢,严丽丽.切削过程优化问题边界极值解法.南京航空学院学报. 1992, 24(4): 370-376.
[15]李兆前,邓建新,艾兴.基于刀具可靠性的切削用量优化.机械工程学报. 1995,31(6): 6-10.
[16]郑华林,吴胜萍.切削用量的模糊优化设计.机械. 1994(6): 24-28.
[17]姜培刚.铣削用量的模糊优化设计.现代机械. 1994(3): 42-45.
[18]李旭东,黄克正,艾兴等.切削用量智能化选择的神经网络建模.组合机床与自动化加工技术. 1999(10): 13-17.
[19] B.Y., Lee, Y.S., Tarng, H.R., Lii. An Investigation of Modeling of the Machining Database in Turning Operations, Journal of Materials Processing Technology, 2000, Vol.105: 1-6.
[20] S.S., Rao, Li, Chen. Determination of Optimal Machining Conditions: a Coupled Uncertainty Model, Transactions of the ASME, Journal of Manufacturing Science and Engineering. 2000, Vol.122: 207-214.
[21] B.K., Subhas, Ramaraja, Bhat, K., Ramachandra. Simultaneous Optimization of Machining Parameters for Dimensional Instability Control in Aero Gas Turbine Components Made of Inconel 718 Alloy. Transactions of the ASME, Journal of Manufacturing Science and Engineering. 2000, Vol.122: 586-589.
[22] Q.,Meng, J.A., Arseculatatne, P., Mathew. Calculation of Optimum Cutting Conditions for Turning Operations Using a Machining Theory. International Journal of Machine Tools & Manufacture. 2000, Vol.40: 1709-1733.
[23]张颖,刘艳秋.软计算方法.北京:科学出版社,2002.
[24]师汉民,陈日曜.基因遗传算法用于切削用量优化.机械工艺师. 1992(9): 3-6.
[25]袁人炜.难加工材料高速铣削机理及工艺[博士学位论文].上海:上海交通大学,2001.
[26]陈吉红,师汉民,陈日曜.基因遗传算法用于人工神经网络的训练.华中理工大学学报. 1992(12): 215-222.
[27]靳蕃.神经计算智能基础原理方法.成都;西南交通大学出版社, 2000.
[28]仇启源.现代金属切削技术.北京:机械工业出版社,1992.
[29] M.E. Merchant. Basic mechanics of the metal cutting process. Trans. Am. Soc. Mech. Engrs. 1944, 66: A168-A175.
[30] M.E.Merchant. Mechanics of the metal cutting process. J. Appl. Phys., Engrs. 1945, 16: 267a-318b.
[31] K.J.Trigger, B.T.Chao. An analytical evaluation of metal cutting temperature. Trans. ASME, 1953, 73: 57-68.
[32] W.B.Palmer, P.L.B.Oxley. Mechanics of orthogonal mahining. Proc. Inst. Mech. Engrs. 1959, 173: 623-638.
[33] P.L.B.Oxley, A.G. Humphreys, A. Larizadeh. The influence of strain-hardening in machining. Proc. Inst. Mech. Engrs. 1961, 175: 881-891.
[34] F. Koenigsberger and A. J. P. Sabberwal. Chip section and cutting force during themilling operation Annals of CIRP. 1961, 10: 197-203.
[35] K.H., Fuh and R.M., Hwang. A predicted milling force model for high-speed end milling operation. International Journal of Machine Tool and Manufacturing. 1997, Vol.37: 969-979.
[36] G. M., Kim, P.J., Cho, and C.N., Chu. Cutting force prediction of sculptured surface ball-end milling using Z-map. International Journal of Machine Tool and Manufacturing. 2000, Vol.40: 277-291.
[37] T.I.Elwardany, M.A.Elebestawi. Cutting Temperature of Ceramic tools in HSM of Difficult-to-Cut Materials. Int. J. Mach. Tools Manufact. 1996, Vol.36(5): 611-634.
[38] Jehnming Lin, Shinn-Liang Lee, Cheng-I Weng. Estimation of Cutting Temperature in High Speed Machining. Journal of Engineering Materials and Technology. Trans. ASME. 1992, Vol.114: 289-296.
[39] Jehnming Lin. Inverse Estimation of the Tool-Work Interface Temperature in End Milling. Int. J. Mach. Tools Manufact. 1995, Vol.35(5): 751-760.
[40] Yogesh K.Potdar, Alan T.Zehnder. Measurements and simulations of temperature and deformation fields in transient metal cutting. Journal of Manufacturing Science and Engineering. 2003, Vol.125: 645-655.
[41]陈明,袁人炜.三维有限元分析在高速铣削温度研究中应用.机械工程学报. 2002, 38(7): 76-79.
[42] Iwata K, Osakada K, Terasaka T. Process modeling of orthogonal cutting by the rigid-plastic finite method. Trans ASME J Eng Master Tecnol. 1984, 106: 132-138.
[43] E.Usui, K.Maekawa, T.Shirakashi. Simulation analysis of the build-up edge formation in machining of low carbon steel. Bull. Jan. Soc. Proc. Eng. 1981, 15(4): 237-242.
[44] E.Usui, T.Shirakashi. Mechanics of machining from descriptive to predictive theory. on the Art of Cutting Metals-75 years later, ASME PED. 1982, 7: 13-35.
[45] Marc Andre Meyers著,张庆明译. Dynamic Behavior of Materials材料的动力学行为.北京:国防工业出版社,2006.
[46] G.R.Johnson and W.H.Cook. A constitutive model and data for metals subjected to large strains, high rates and high temperatures. Proceedings of the Seventh International Symposium on Ballistics. The Netherlands, The Hague. 1983: 541-547.
[47] G.R.Johnson, T.J.Holmquist. Evaluation of cylinder-impact test data for constitutive model constants. J. Appl. Phys. 1988, 64(8): 3901-3910.
[48] G.R.Johnson, W.H.Cook. Fracture characteristics of three metals subjected to various strains, strain rates, temperatures and pressures. Eng. Fract. Mech. 1985, 21(1): 31-48.
[49] F.J.Zerilli, R.W.Armstrong. Dislocation-mechanics-based constitutive relations for material dynamics calculations. J. Appl. Phys. 1987, 61(5): 1816-1825.
[50] F.J.Zerilli, R.W.Armstrong. Description of tantalum deformation behavior by dislocation mechanics based constitutive equations. J. Appl. Phys. 1990, 68(4): 1580-1591.
[51] F.J.Zerilli, R.W.Armstrong. The effect of dislocation drag on the stress-strain behavior of FCC metals. Acta. Metall. Mater. 1992, 40(8): 1803-1808.
[52] S.R.Bodner. Constitutive equations for dynamic material behavior in mechanical behavior of materials under dynamic loads. New York : Springer-Verlag, 1968: 176-190.
[53] S.R.Bodner, Y.Partom. A large deformation elastic-visco-plastic analysis of thick-walled spherical shell. ASME J. Appl. Mech. 1972, 39: 751-757.
[54] S.R.Bodner, and Y.Partom. Constitutive equations for elastic-visco-plastic strain-harding materials. ASME J. Appl. Mech. 1975, 42: 385-389.
[55] P.S.Follansbee, U.F.Kocks. A constitutive description of the deformation of copper based on the use of the mechanical threshold stress as an internal state variable. Acta. Metall. 1988, 36: 81-93.
[56]刘旭红,黄西成,陈裕泽,苏先樾,朱建士.强动载荷下金属材料塑性变形本构模型评述.力学进展. 2007,37(3): 361-374.
[57] Tobias SA, Fishwick W. A theory of regenerative chatter. Engineer, London, 1958.
[58] Tlusty J, Polacek M. The stability of machine tools against self excited vibrations in machining. Int Res Prod Eng. 1963: 465-474.
[59] Merritt HE. Theory of self-excited machine tool chatter. ASME J Eng Ind. 1965, 87: 447-454.
[60] F.Koenigsberger, J.Tlusty. Machine Tool Structures-Vol.I: Stability Against Chatter. Pergamon Press, 1967.
[61] H.Opitz, F.Bernardi. Inverstigation and Calculation of the Chatter Behavior of Lathes and Milling Machines. Annals of the CIRP, 1970, Vol. 18: 335-343.
[62] J.Tlusty, F.Ismail. Basic Nonlinearity in Machining Chatter. Annals of the CIRP. 1981, Vol.30: 21-25.
[63] J.Tlusty, Dynamics of High-Speed Milling. ASME Journal of Engineering for Industry. 1986, Vol.108(2): 59-67.
[64] S.Smith, J.Tlusty. Efficient Simulation Programs for Chatter in Milling. Annals of the CIRP. 1993, Vol.42(1): 463-466.
[65] Y.Altintas, D.Montgomery, and E.Budak. Dynamic Peripheral Milling of Flexible Structures. ASME, Journal of Engineering for Industry. 1992, Vol.114(2): 137-145.
[66] Y. Altintas and E. Budak. Analytical Prediction of Stability Lobes in Milling. Annals of the CIRP. 1995, 44(1): 357-362.
[67] Yusuf Altintas. Manufacturing Automation– Metal Cutting Mechanics, Machine Tool Vibrations and CNC Design. Cambridge: Cambridge University Press, 2002.
[68] Y.Altintas, E.Shamoto, P.Lee, etc., Analytical Prediction of Stability Lobes in Ball End Milling , Transactions of the ASME, Journal of Manufacturing Science and Engineering, 1999, Vol.121: 586~592.
[69] Y. Altintas, M. Weck. Chatter Stability of Metal Cutting and Grinding. Annals of CIRP. 2004, 53(2): 619-642.
[70] E. Budak and Y. Altintas. Analytical Prediction of Chatter Stability in Milling - Part I General Formulation. Trans. ASME Journal of Dynamics System, Measurement and Control. 1998, Vol.120: 22-30.
[71] E. Budak and Y. Altintas. Analytical Prediction of Chatter Stability in Milling - Part II Application of the General Formulation to Common Milling Systems. Trans. ASME Journal of Dynamics System, Measurement and Control, 1998, Vol.120: 31-36.
[72]彭翀,刘强,李忠群.基于Web的铣削过程颤振稳定域远程仿真系统研究.计算机工程与应用. 2006(20): 213-216.
[73]刘强,尹力.一种面向数控工艺参数优化的铣削过程动力学仿真系统研究.中国机械工程. 2005 (13) : 1146～1149.
[74]李忠群,刘强.基于MATLAB的铣削加工颤振稳定域仿真算法及实现.机械设计与制造. 2007(7): 109-111.
[75]宋清华,艾兴.高速铣削稳定性与表面加工精度研究.制造技术与机床. 2008(4): 40-43.
[76] Martin T. Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design. Boston, MA: PWS Publishing, 1996.
[77] Z. Guo, W. Sha. Modelling the correlation between processing parameters andproperties in titanium alloys using artificial neural network. Computational Materials Science. 2001, 21(3): 375-394.
[78] S. Malinov, W. Sha. Software products for modelling and simulation in materials science. Computational Materials Science. 2003, 28(2): 179-198.
[79] S. Malinov, W. Sha. Application of artificial neural networks for modelling correlations in titanium alloys. Materials Science & Engineering. 2004, 365(1-2): 202-211.
[80] Z. Guo, W. Sha. Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network. Computational Materials Science. 2004, 29(4): 12-28.
[81] J. McBride, S. Malinov, W. Sha. Modelling tensile properties of gamma-based titanium aluminides using artificial neural network. Materials Science & Engineering. 2004, 384(1-2): 129-137.
[82] D. Umbrello, G. Ambrogio, L. Filice. An ANN approach for predicting subsurface residual stresses and the desired cutting conditions during hard turning. International Journal of Materials Processing Technology. 2007, 189(1-3): 143-152.
[83] C. Chungchoo, D. Saini. On-line tool wear estimation in CNC turning operations using fuzzy neural network model. International Journal of Machine Tools & Manufacture. 2002, 42(1): 29-40.
[84] S.K. Choudhury, V.K. Jain, Ch.V.V. Rama Rao. On-line monitoring of tool wear in turning using a neural network. International Journal of Machine Tools & Manufacture. 1999, 39: 489–504.
[85] Qiang Liu, Yusuf Altintas. On-line monitoring of flank wear in turning with multilayered feed-forward neural network. International Journal of Machine Tools & Manufacture 1999, 39: 1945–1959.
[86] D.E. Dimla Sr, P.M. Lister. On-line metal cutting tool condition monitoring. II tool-state classification using multi-layer perceptron neural networks. International Journal of Machine Tools & Manufacture. 2000, 40: 769–781.
[87]陈超,徐建林,黄建龙.基于人工神经网络的刀具状态监控系统.机械工程学报. 2002(8): 135-138.
[88] Tugrul Ozel, Yigit Karpat. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools & Manufacture. 2005, 45(4-5): 467-479.
[89] C. Sanjay, M.L. Neema, C.W. Chin. Modeling of tool wear in drilling by statistical analysis and artificial neural network. International Journal of Materials Processing Technology. 2005, 170(3): 494-500.
[90]郭俊锋.神经网络及仿真在切削加工中的应用研究[硕士学位论文].兰州:兰州理工大学, 2005.
[91]周泽华.金属切削原理(第二版).上海:上海科学技术出版社, 2000.
[92]孟令锋.磨削加工切削参数的智能选择系统研究[硕士学位论文].成都:四川大学,2006.
[93]方昆凡.工程材料手册黑色金属材料卷.北京:北京出版社,2002.
[94] L.A. Zadeh. Fuzzy sets. Information and Control. 1965, Vol.8: 338-353.
[95]朱训生.工程管理的模糊分析.上海:上海交通大学出版社,2004.
[96]王锋. CIMS环境下车、铣削数据库专家系统的研究开发[博士学位论文].北京:北京理工大学,1995.
[97]庞思勤,于启勋,刘胜杨等. CIMS环境下的切削数据库和专家系统.工具技术. 2000, 34 (增刊): 98-100.
[98] WONG S V , HAMOUDA A M S. Development of genetic algorithm-based fuzzy rules design for metal cutting data selection. Robotics and Computer Integrated Manufacturing. 2002, 18: 1-12.
[99] ROWE WB , L I Y, CHEN X , et al. Case - based reasoning for selection of grinding conditions. Computer Integrated Manufacturing Systems. 1996, 9(4): 197-205.
[100]刘战强,王遵彤,万熠.基于实例推理的刀具材料选择系统的研究.中国机械工程学会2002年会.北京:机械工业出版社,2002.
[101]王遵彤,刘战强,万熠.高速切削数据库技术的研究.工具技术. 2002, 34(6):6-9.
[102]王遵彤.基于实例推理的高速切削数据库系统HISCUT的研究[博士学位论文].济南:山东大学,2003.
[103] Roger Jang. Neuro-Fuzzy and Soft Computing. New Jersey: Prentice Hall, 1997.
[104]苏宇,何宁,武凯,李亮.基于ANFIS的铝合金铣削加工表面粗糙度预测模型研究.中国机械工程. 2005(6): 475-478.
[105]孙艳红,杨兆军,李雪,张立新.基于ANFIS模糊神经网络的微钻头破损监测.润滑与密封. 2006(11): 66-70.
[106]张浩炯,余岳峰,王强.应用自适应神经模糊推理系统(ANFIS)进行建模与仿真.计算机仿真. 2002(7): 47-49.
[107]孟令锋.磨削加工切削参数的智能选择系统研究[硕士学位论文].成都:四川大学,2006.
[108] GIUSTI F, SANTOCHI M. COATS: an expert module for optimal tool selection. Annals of the CIRP. 1986 , 35(1): 337-340.
[112]王庆林,李莉敏,韦纪祥. UG铣制造过程实用指导.北京:清华大学出版社,2002.
[110] VAN LUTTERVEL T C A ,CHILDS T HC,JAWAHIR I S, et al. Present situation and future trends in modeling of machining operations. Annals of the CIRP. 1998, 47(2): 587-626.
[111] EVERSHEIM W, KALS H J J , KONIG W, et al. Tool management:the present and the future. Annals of the CIRP. 1991, 40(2): 631-639.
[112]梁吉文,张玉,郁鼎文.面向CIMS的计算机辅助刀具管理系统.工具技术. 2000. 34(2): 16-18.
[113]王涛,陈三松,刘小梅.计算机刀具管理系统的开发.工具技术. 2000, 34(9): 15-17.
[114]周文亚,边月明,张明贤.航空常用铝合金NC铣削数据库的研究与开发.航空制造技术. 1994(5): 14-16.
[115]任红丽,孙伟岩,刘献礼,等.聚晶立方氮化硼刀具切削数据库管理系统.哈尔滨理工大学学报. 2001, 6(5): 71-73,74.
[116]凡孝勇.旋转刀具/切削数据库应用研究[硕士学位论文].上海:上海交通大学, 2001.
[117]杨贵军.回转体刀具切削数据库的研究与应用[硕士学位论文].上海:上海交通大学,2001.
[118]张兴模,彭贝.大型多用户车削数据库软件CTRN90的开发及其应用.工具技术. 1992, 26(1): 21-28.
[119]叶文华.金属切削数据库系统及磨削模拟的理论与应用研究[博士学位论文].南京:南京航空航天大学,1991.
[120]周渠.涂层硬质合金刀具切削数据库[博士学位论文].北京:北京理工大学,1990.
[121]周渠,于启勋.涂层硬质合金切削数据库.硬质合金. 1992, 9(4): 223-226.
[122]赵文祥.硬质合金刀具材料切削数据库的建立与研究[博士学位论文].北京:北京理工大学,1994.
[123] OZBAYRAK M , De SOUZA R B R , BELL R. Design of a toolmanagement system for a flexible machining facility. Proceedings of the Institution of Mechanical Engineers Part B. Journal of Engineering Manufacture. 2001, 215: 353-370.
[124] ADAMCZYK Z, MAL EK H. Internet tools supporting creation and management of technological of CAD/ CAM systems. Journal of Materials Processing Technology. 1998, 76(1-3): 102-108.
[125]刘忠和.基于B/S网络的切削数据库与专家系统的开发和应用研究[博士学位论文].北京:北京理工大学,2003.
[126]刘忠和.基于Web环境的刀具切削数据库与专家系统[硕士学位论文].北京:北京理工大学,2000.
[127]王怀明.基于Web切削刀具信息系统的开发研究[硕士学位论文].北京:北京航空航天大学,2004.
[128]陈文亮.基于Web的数控切削参数管理及优化系统的研究与实现[博士学位论文].南京:南京航空航天大学,2001.
[129]刘培德.切削力学新篇.大连:大连理工大学出版社, 1991.
[130]臼井英治.切削磨削加工学.北京:机械工业出版社, 1982.
[131]陈明,袁人炜,薛秉源等.铝合金高速铣削中切削温度动态变化规律的试验研究.工具技术. 2000, 34(5): 7-10.
[132] Chen Ming, Sun Fanghong. Experimental research on the dynamic characteristics of the cutting temperature in the process of high-speed milling. Journal of Materials Processing Technology. 2003, 138: 468–471.
[133] P. Lezanski, M.C. Shaw. Tool Face Temperatures in High Speed Milling. Transactions of ASME. 1990, Vol.112: 132-135.
[134]史兴宽,杨巧凤,张明贤,陈明.钛合金TC4高速铣削表面的温度场研究.航空制造技术. 2002(1): 34-37.
[135] Milton C. Shaw. Metal Cutting Principles. New York: Oxford University Press, 2005.
[136]乐兑谦.金属切削刀具(第二版).北京:机械工业出版社,2005.
[137]周泽华.金属切削理论.北京:机械工业出版社, 1992.
[138] Stephenson, D.A. Assessment of Steady-State metal cutting Temperature Models based on simultaneous infrared and thermocouple data. Journal of Engineering for Industry. 1991, Vol.113: 121-128.
[139]王金鹏,曾攀,雷丽萍. 2024Al高温高应变率下动态塑性本构关系的实验研究.塑性工程学报. 2008, 15(3): 101-104,118.
[140]杨桂通.弹塑性力学引论.北京:清华大学出版社,2004.
[141]李同林.弹塑性力学.武汉:中国地质大学出版社,2006.
[142]张幼桢.金属切削理论.南京:航空工业出版社,1988.
[143]王桂生.钛的应用技术.长沙:中南大学出版社,2007.
[144]杨勇.钛合金航空整体结构件铣削加工变形的预测理论及方法研究[博士学位论文].杭州:浙江大学, 2007.
[145]陈日耀.金属切削原理(第二版).北京:机械工业出版社,2004.
[146]张颖,刘艳秋.软计算方法.北京:科学出版社,2002.
[147] S.P. Lo. An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology, 2003, Vol. 142: 665-675.
[148] A. Iqbal, N. He, L. Li and N.U. Dar. A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process. Expert Systems with Applications, 2007, Vol.32: 1020-1027.
[149] S. Kumanan, C.P. Jesuthanam and R.A. Kumar. Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness. The International Journal of AdvancedManufacturing Technology. 2008, Vol.35(7-8): 778-788.
[150]谢季坚,刘承平.模糊数学方法及其应用(第三版).武汉:华中科技大学出版社,2006.
[151] Liang Tian, Dynamic learning with neural networks and support vector machines [Ph.D. Dissertation]. West Virginia University, 2005.
[152] Howard B. Demuth, Mark H. Beale, Martin T. Hagan. Neural Network Toolbox User’s Guide. The MathWorks, Inc. 2007.
[153]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计.北京:清华大学出版社,2005.
[154] David J.C. MacKay. Bayesian Interpolation. Neural Computation. 1992, Vol.4: 415-447.
[155] F. Dan Foresee, Martin T. Hagan. Gauss-Newton approximation to Bayesian learning. Proceedings of the 1997 International Joint Conference on Neural Networks. 1997: 1930-1935.
[156] F. Dan Foresee. Generalization an Neural Networks [Ph.D. Dissertation]. Oklahoma State University, 1996.
[157]黄渝祥,邢爱芳.工程经济学(第三版).上海:同济大学出版社,2005.
[158]曹承志,王楠.智能技术.北京:清华大学出版社, 2004.
[159]徐孝凯.数据库技术基础教程.北京:机械工业出版社,2004.
[160]王珊,萨师暄.数据库系统概论(第四版).北京:高等教育出版社,2006.
[161] C.J. Date著,孟小峰,王珊译. An Introduction to Database System (Seventh Edition).北京:机械工业出版社,2000.