基于虚拟加工车削参数优化方法的研究
|
摘要
制造业是现代国民经济的重要支柱产业,数控加工技术在制造业中占有重要地位。在实际数控生产加工中,切削速度、进给量、背吃刀量等加工参数通常由编程人员指定,为避免过大的切削力、切削振动等导致机床损坏和零件加工精度降低现象的发生,编程人员往往牺牲加工效率而选择非常保守的切削参数,从而严重影响和限制了数控设备的使用效率和应用范围。针对以上加工中存在的问题,本课题以提高数控机床加工效率、降低加工成本和获得高质量产品为目的而进行了数控车削加工参数优化方法的研究,研究成果具有广泛的经济效益和社会效益。
虚拟加工技术是当前加工仿真技术发展的一个新阶段,不仅能提供逼真的加工环境仿真,而且在仿真过程中考虑了机床系统的运动控制和动态特性。本文开展了基于虚拟加工车削参数优化方法的研究,对基于虚拟加工车削参数优化方法的原理进行了分析,建立了基于虚拟加工车削参数优化方法的体系结构,包括功能体系结构和组织体系结构,并确定了具体的优化工作流程。
基于虚拟加工车削参数优化方法的前提是实现准确的加工过程仿真,本文开展了利用实体仿真技术进行加工过程仿真的研究,研究了数控车削加工的几何建模方法,实现了利用刀具扫描体与工件模型进行布尔减的加工仿真过程。通过将三维实体模型转换为二维多边形模型,有效地减少了计算时间,提高了仿真速度。在加工过程仿真同时利用“裁剪多边形”获取瞬时背吃刀量和切削工件半径为切削加工性能预测和切削参数优化奠定基础。在仿真过程中,通过经验公式模型预测车削加工动态切削力,实验结果表明预测值与测量值较好吻合,证明仿真系统具有较好的加工性能预测能力,同时也间接验证了切削参数获取方法的正确性。
在总结了单程切削加工特点的基础上,确定了以离散加工路径分段进行切削参数优化的策略。优化变量选定主轴转速和进给量。建立了恒主切削力与效率相结合的多目标优化函数,制订了单程切削加工中的约束规则。对应用遗传算法求解切削参数优化数学模型的可行性进行了分析。采用遗传算法对切削参数优化数学模型进行寻优求解。通过仿真计算,分析了遗传算法中最大迭代次数、交叉概率和变异概率对优化结果的影响。通过重复性仿真计算的方法验证了优化方法的可靠性。
在分析多程切削加工特点的基础上,研究了多程切削加工的参数优化方法。以单件产品最低加工成本为目标,建立优化参数的数学模型,制订相关约束条件。数学模型建模中考虑了瞬时工件加工半径对优化结果的影响,克服了以往优化数学模型过度简化的局限性。通过单程切削加工优化模型建立优化参数数据库,利用整数非线性优化算法优化切削参数,以达到节省加工成本的目的。通过实例计算,结果表明:所用算法较好地解决了多程切削加工中循环次数的确定和总加工余量分配的问题。
以VC++6.0为工具,开发了具有实体仿真功能的基于虚拟加工的切削参数优化软件系统。系统以车削加工几何建模技术为基础,可实现单程切削加工和多程切削加工参数优化,并自动生成能够实际生产的数控程序代码,使数控编程更加智能化。利用该系统根据恒主切削力和效率的组合优化目标对粗加工数控车削程序进行了优化,并应用优化前后的数控程序进行了实际切削加工实验。实验结果验证了基于虚拟加工车削参数优化方法的有效性和可靠性。
Manufacturing industry is an important pillar of the modern industrial economy, CNC machining technology in the manufacturing industry occupies an important position. In the actual processing of CNC, the processing parameters of the cutting speed, feed rate, cutting depth, etc. are often specified conservatively by the programmer in order to avoid excessive cutting force, vibration machine, which lead to damage machine and reduce machining precision, thus seriously limiting the efficiency in the use of numerical control equipment. The paper studies the method of CNC turning parameters optimization for the purpose of enhancing efficiency, reducing processing costs and accessing to high-quality products, the results have a wide range of economic and social benefits.
Virtual machining simulation technology is a new stage of development of the current processing technology, it not only provide a realistic simulation of the processing environment, but also take into account the process of motion and dynamic characteristics in the geometric and physical simulation. This paper studies on the method of cutting parameters optimization based on virtual machining, the architecture is analyzed, which includes the function architecture and organizations architecture, and the workflow is identified.
Because machining simulation is the basement to the cutting parameters optimization, the method of geometric modeling is proposed and the machining simulation process is realized by the use of the Boolean with the model of tool swept volume and work piece. It can effectively reduce the calculation time and improve the simulation speed by three-dimensional solid model converted to two-dimensional polygon model. At the same time in the machining process simulation, cutting parameters such as cutting depth and radius are acquired by the method of "polygon clipping", which are the basis of processing performance prediction and cutting parameters optimization system. In the simulation process, cutting force model is established with the empirical formula, the experimental results show that the predicted values are similar with the measured, which proves the simulation system has better predictive capability of processing performance and also indirectly verifies the correctness of acquired method of cutting parameters.
Based on the conclusion of a single pass turning machining features, cutting parameter optimization strategy is determined on the basis of a discrete sub-processing path. Variables selected to optimize are spindle speed and feed rate. The function of constant cutting force is established a with efficiency, the rules of constraint of single pass turning are determined. The feasibility of genetic algorithm for optimization of single pass cutting parameters is carried out. The genetic algorithm is used. Through simulation, the optimization results are compared with the different largest number of iterations, crossover probability and mutation probability. The reliability of the optimization method is verified through repetitive simulation methods.
Based on the conclusion of multi-pass turning machining features, the cutting parameters optimization method is researched. A mathematical model of minimum costs is the established. The instantaneous radius of the workpiece processing is considered in the mathematical modeling, which overcomes the limitations of over-simplification in the previous model. The integer nonlinear optimization method and GA algorithm are used to optimize cutting parameters in order to achieve the purpose of saving processing costs. Through examples, the results show that the algorithm can solve the problem of identification of multi-pass turning cycle times and allocation of the total allowance.
With VC++6.0, a cutting parameter optimization system based on NC turning simulation is developed. Using the optimization system, spindle revolutions and feed rates in NC file for rough machining are optimized according to the multiple objectives: the constant machining forces and increasing machining efficiency. Using the optimal NC file, turning experiments are carried out and the experiment results show the validity and reliability of cutting parameters optimization system.
虚拟加工技术是当前加工仿真技术发展的一个新阶段,不仅能提供逼真的加工环境仿真,而且在仿真过程中考虑了机床系统的运动控制和动态特性。本文开展了基于虚拟加工车削参数优化方法的研究,对基于虚拟加工车削参数优化方法的原理进行了分析,建立了基于虚拟加工车削参数优化方法的体系结构,包括功能体系结构和组织体系结构,并确定了具体的优化工作流程。
基于虚拟加工车削参数优化方法的前提是实现准确的加工过程仿真,本文开展了利用实体仿真技术进行加工过程仿真的研究,研究了数控车削加工的几何建模方法,实现了利用刀具扫描体与工件模型进行布尔减的加工仿真过程。通过将三维实体模型转换为二维多边形模型,有效地减少了计算时间,提高了仿真速度。在加工过程仿真同时利用“裁剪多边形”获取瞬时背吃刀量和切削工件半径为切削加工性能预测和切削参数优化奠定基础。在仿真过程中,通过经验公式模型预测车削加工动态切削力,实验结果表明预测值与测量值较好吻合,证明仿真系统具有较好的加工性能预测能力,同时也间接验证了切削参数获取方法的正确性。
在总结了单程切削加工特点的基础上,确定了以离散加工路径分段进行切削参数优化的策略。优化变量选定主轴转速和进给量。建立了恒主切削力与效率相结合的多目标优化函数,制订了单程切削加工中的约束规则。对应用遗传算法求解切削参数优化数学模型的可行性进行了分析。采用遗传算法对切削参数优化数学模型进行寻优求解。通过仿真计算,分析了遗传算法中最大迭代次数、交叉概率和变异概率对优化结果的影响。通过重复性仿真计算的方法验证了优化方法的可靠性。
在分析多程切削加工特点的基础上,研究了多程切削加工的参数优化方法。以单件产品最低加工成本为目标,建立优化参数的数学模型,制订相关约束条件。数学模型建模中考虑了瞬时工件加工半径对优化结果的影响,克服了以往优化数学模型过度简化的局限性。通过单程切削加工优化模型建立优化参数数据库,利用整数非线性优化算法优化切削参数,以达到节省加工成本的目的。通过实例计算,结果表明:所用算法较好地解决了多程切削加工中循环次数的确定和总加工余量分配的问题。
以VC++6.0为工具,开发了具有实体仿真功能的基于虚拟加工的切削参数优化软件系统。系统以车削加工几何建模技术为基础,可实现单程切削加工和多程切削加工参数优化,并自动生成能够实际生产的数控程序代码,使数控编程更加智能化。利用该系统根据恒主切削力和效率的组合优化目标对粗加工数控车削程序进行了优化,并应用优化前后的数控程序进行了实际切削加工实验。实验结果验证了基于虚拟加工车削参数优化方法的有效性和可靠性。
Manufacturing industry is an important pillar of the modern industrial economy, CNC machining technology in the manufacturing industry occupies an important position. In the actual processing of CNC, the processing parameters of the cutting speed, feed rate, cutting depth, etc. are often specified conservatively by the programmer in order to avoid excessive cutting force, vibration machine, which lead to damage machine and reduce machining precision, thus seriously limiting the efficiency in the use of numerical control equipment. The paper studies the method of CNC turning parameters optimization for the purpose of enhancing efficiency, reducing processing costs and accessing to high-quality products, the results have a wide range of economic and social benefits.
Virtual machining simulation technology is a new stage of development of the current processing technology, it not only provide a realistic simulation of the processing environment, but also take into account the process of motion and dynamic characteristics in the geometric and physical simulation. This paper studies on the method of cutting parameters optimization based on virtual machining, the architecture is analyzed, which includes the function architecture and organizations architecture, and the workflow is identified.
Because machining simulation is the basement to the cutting parameters optimization, the method of geometric modeling is proposed and the machining simulation process is realized by the use of the Boolean with the model of tool swept volume and work piece. It can effectively reduce the calculation time and improve the simulation speed by three-dimensional solid model converted to two-dimensional polygon model. At the same time in the machining process simulation, cutting parameters such as cutting depth and radius are acquired by the method of "polygon clipping", which are the basis of processing performance prediction and cutting parameters optimization system. In the simulation process, cutting force model is established with the empirical formula, the experimental results show that the predicted values are similar with the measured, which proves the simulation system has better predictive capability of processing performance and also indirectly verifies the correctness of acquired method of cutting parameters.
Based on the conclusion of a single pass turning machining features, cutting parameter optimization strategy is determined on the basis of a discrete sub-processing path. Variables selected to optimize are spindle speed and feed rate. The function of constant cutting force is established a with efficiency, the rules of constraint of single pass turning are determined. The feasibility of genetic algorithm for optimization of single pass cutting parameters is carried out. The genetic algorithm is used. Through simulation, the optimization results are compared with the different largest number of iterations, crossover probability and mutation probability. The reliability of the optimization method is verified through repetitive simulation methods.
Based on the conclusion of multi-pass turning machining features, the cutting parameters optimization method is researched. A mathematical model of minimum costs is the established. The instantaneous radius of the workpiece processing is considered in the mathematical modeling, which overcomes the limitations of over-simplification in the previous model. The integer nonlinear optimization method and GA algorithm are used to optimize cutting parameters in order to achieve the purpose of saving processing costs. Through examples, the results show that the algorithm can solve the problem of identification of multi-pass turning cycle times and allocation of the total allowance.
With VC++6.0, a cutting parameter optimization system based on NC turning simulation is developed. Using the optimization system, spindle revolutions and feed rates in NC file for rough machining are optimized according to the multiple objectives: the constant machining forces and increasing machining efficiency. Using the optimal NC file, turning experiments are carried out and the experiment results show the validity and reliability of cutting parameters optimization system.
引文
1罗然宾.对制造业信息化的战略思考.装备制造技术. 2006, (5):79~81
2董丽华,刘大昕,赵彦玲.网络化制造中加工过程仿真的研究.工具技术. 2003, 37(112):21~23
3姚英学,李荣彬.面向加工质量预测的虚拟加工检测单元的研制.中国机械工程. 2000, 11(5):520~524
4苏新兵,张登成,王建平.虚拟制造技术在飞行器设计中的应用.现代制造工程. 2007, (2):127-129
5潘爱民.虚拟数控铣削加工系统的研究与开发.计算机工程与设计, 2004, 25(2):295-297
6卢碧红.虚拟数控车削加工精度预测系统研究.大连铁道学院博士论文. 2003:4-5
7王占礼.面向虚拟制造的数控加工仿真技术研究.吉林大学博士论文.2007,6:3-5
8张伯鹏.虚拟数控机床技术.中国机械工程. 1998,9(11):66-69
9袁一平,马雷.基于投影网格的切削加工计算机仿真.机械设计与研究. 2009, 25(2):93-95
10 L. Carrino, G. Giorleo, W. Polini, U. Prisco. Dimensional Errors in Longitudinal Turning Based On the Unified Generalized Mechanics of Cutting Approach. Part I: Three-Dimensional Theory. International Journal of Machine Tools and Manufacture. 2002, (42):1509~1515
11 Y.S. Tarng, C.I. Cheng and J.Y. Kao. Modeling of Three-Dimensional Numerically Controlled End Milling Operations. International Journal of Machine Tools and Manufacture. 1995, 35(7):939-950
12范胜波.虚拟数控车削加工质量预测系统的研究.天津大学博士论文. 2005, 12:5
13 Kang Ik Soo, Kim Jeong Suk, Seo Yong Wie. Cutting Force Model Considering Tool Edge Geometry for Micro End Milling Process. Journal of Mechanical Science and Technology. 2008, 22(2): 293-299
14 M.T. Zaman, A.S. Kumar, M. Rahman. A Three-dimensional AnalyticalCutting Force Model For Micro End Milling Operation. International Journal of Machine Tools and Manufacture. 2006, 46(3-4): 353-366
15 Z.G. Wang, M. Rahman, Y.S. Wong. A Hybrid Cutting Force Model for High-Speed Milling of Titanium Alloys. CIRP Annals - Manufacturing Technology. 2005, 54(1): 71-74
16 P.W. Tse, S. Gontarz, X.J. Wang, Enhanced Eigenvector Algorithm for Recovering Multiple Sources of Vibration Signals in Machine Fault Diagnosis. Mechanical Systems and Signal Processing. 2007, 21(7): 2794-2813
17 Xie Shun-Qiang, Yu Yue-Qing, Zhang Lan-Xia. Vibration-Characteristic Simulation of a Novel Hybrid Machine Tool. 2007, 24(11): 186-192
18 Koro?ec Marjan, Kopac, Janez. Review of New Approaches in Tool Wear Measuring Techniques Focused on Vision Systems. Tehnicki Vjesnik. 2005, 12(3-4): 17-26
19 Shi Dongfeng, Gindy Nabil. Tool Wear Predictive Model Based on Least Squares Support Vector Machines. Mechanical Systems and Signal Processing. 2007, 21(4): 1799-1814
20 M.C. Lu. Analysis of Acoustic Signals Due to Tool Wear During Machining. Michigan. The University of Michigan. 2001
21 Tanie Hisashi. Shape Prediction of Molten Micro-Solder Joints Using Particle Method and Application to Evaluation of Fracture Life. 2007 Proceedings of the ASME InterPack Conference, IPACK 2007: 87-94
22 Tower Susan, Su Bingzhi, Y.C. Lee. Yield Prediction for Flip-Chip Solder Assemblies Based on Solder Shape Modeling. IEEE Transactions on Electronics Packaging Manufacturing. 1999, 22(1): 29-37
23 Gao Tong, Zhang Weihong, Qiu Kepeng. Numerical Simulation of Machined Surface Topography and Roughness in Milling Process. Journal of Manufacturing Science and Engineering, Transactions of the ASME. 2006, 128(1): 96-103
24 Zhang Wei-Hong, Tan Gang, Wan, Min. A New Algorithm for the Numerical Simulation of Machined Surface Topography in Multi-Axis Ball-End Milling. Journal of Manufacturing Science and Engineering, Transactions of theASME. 2008, 130(1): 31-311
25 J.R. Rene Mayer, Anh-Vu Phan, Guy Cloutier. Prediction of Diameter Errors in Bar Turning: a Computationally Effective Model. Applied Mathematical Modeling. 2000,(24):943~956
26 W.J. Endres. A dynamic Model of Cutting Force System in the Turning Process. SME Symposium on Monitoring and Contro1 for Manufacturing Processes.PED,1991,(44):193~212
27 H. Zhao, J.G. Li, Y.X. Yao. Cutting Parameters Optimization for Constant Cutting Force in Milling. Applied Mechanics and Materials. 2008, (10-12): 483-487
28 Janez Kopac, Marko Bahor, Mirko Sokovic. Optimal Machining Parameters for Achieving the Desire Surface Roughness in Fine Turning of Cold Pre-formed Steel Workpieces. International Journal of Machine Tools & Manufacture. 2002, (42):707~716
29 Uros Zuperl, Franci Cus. Optimization of Cutting Conditions during Cutting by Using Neural Networks, Robotics and Computer Integrated Manufacturing. 2003, (19):189~199
30 B.Arezoo, K. Ridgwong, A.M.A. Al-Ahmari. Selection of Cutting Tools and conditions of Machining Operations Using an Expert System. Computers Industry. 2000,(42):43~58
31 Franci Cus, Joze Balic. Optimization of Cutting Process by GA Approach, Robotics and Computer Integrated Manufacturing. 2003,(19):113~121
32 B.Mursec, F. Cus. Integral Model of Selection of Optimal Cutting Conditions from Different Databases of Tool Makers, Journal of Materials Processing Technology. 2003,(133):158~165
33 K.I. Lee, S.D. Nob. Virtual Manufacturing System - a Test-Bed of Engineering Activities.Annals of the CIRP.1997, 46(1):347~350
34 Hong-Sen Yan, Jaw-Jong Chen. Creative Design of Wheel Damping Mechanism.Mech.Mach.Theory. 1985, 20(6):597~600
35 Paul K Wright.Principles of Open-Architecture Manufacturing.Journal of Manufacturing System.1995, 14(3):187~202
36 Masayoshi Tomizuka.Zero Phase Error Tracking Algorithm for DigitalControl.Transaction of ASME, Journal of Dynamic Systems, Measurement, and Control.1998, 109:65~68
37 C H Chen, K W Wang, Y C Shin.An Integrated Approach toward the Modeling and Dynamic Analysis of High Speed Spindle-Part 1: System Model.Transaction of ASME, Journal of Vibration and Acoustics.1994, 116:506~513
38 Hong-Sen Yan. A Methodology for Creative Mechanism Design. Mech. Mach. Theory.1992, 27(3):235~242
39 W.A. Kline, R.E. Devor, I.A. Shareef.The Prediction of Surface Accuracy in End Milling . Transaction of ASME, Journal of Engineering for Industry.1982, 104:272~278
40 J.W. Sutherland, R.E. Devor.An Improved Method for Cutting Force and Surface Error Presiction in Flexible End Milling Systems.Transaction of ASME, Journal of Engineering for Industry.1986,108:269~279
41 S.J. You, K.F. Ehmann. Computer Synthesis of Three-Dimensional Surface.Wear.1991, 145:29~42
42 K .F. Ehmann, M.S. Hong.A Generalized Model of the Surface Generation Process in Metal Cutting.Annals of the CIRP.1994,43(1):483~486
43 Yingxue Yao, Jianguang Li, W.B. Lee. VMMC: A Test-bed for Machining.Computers in Industry.2002, 47:255~268
44王凤彪,王秀伦,段竹.机加工用量选择专家系统.电气技术与自动化. 2006, 35(2):119~121
45 A.д.马卡洛夫.切削过程最优化.杨锦华译.国防工业出版社. 1988:63~77
46刘战强,黄传真,万熠,艾兴.切削数据库研究现状与发展.计算机集成制造系统. 2003, 9(11):37~43
47王凤彪,王秀伦,段竹.机加工用量选择专家系统.电气技术与自动化. 2006, 35(2):119~121
48彭观,陈统坚,彭劲雄,傅勇辉.基于专家系统和神经网络的制造过程智能决策系统.组合机床与自动化加工技术. 1999, (2):24~27
49 K. Hitomi. Optimization of Multistage Machining System: Analysis of Optimal Machining Conditions for the Flow-type Machining System. 1971,93:498~506
50 M. Field, N. Zlatin. Computer Approach for Storage of Machine ability Data and Calculation of Machining Costs and Production Rates. Proceedings of the 8th International MATADOR, UMIST, 1967:88~95
51 M. Field, N. Zlatin, R. Williams, M. Kronenberg. Computerized Determination and Analysis of Cost and Production Rates for Machining Operations. J Eng Ind Trans ASME. 1968, 90:455~466
52 K. Okushima and K. Hitomi. A Study of Economical Machining– an Analysis of Maximum Profit Cutting Speed. International Journal of Production Research.1966, (6): 73~78
53 P. Petropoulos. Optimal selection of machining economics problem by geometric programming. Int. J. Prod. Res. 1973,11:305~314
54 D.S. Ermer. Optimization of the Constrained Machining Economics Problem by Geometric Programming. Trans. ASME. 1971, 93:1067~1072
55 D.S. Ermer and S. Kromodihardjo. Optimization of Multipass Turning with Constrains. J. Engng indust Trans. ASME. 1981, 103: 462~468
56 K. Iwata, Y. Murotsu, T. Iwatsubo and S. Fuju. A Probabilistic Approach to the Determination of the Optimum Cutting Conditions. J. Engng indust. Trans. ASME, Ser. B, 1973, (Ser. B):1099~1107
57 K. Iwata, Y. Murotsu, F. Oba. Optimization of Cutting Conditions for Multi-pass Operations Considering Probabilistic Nature in Machining Processes. J. Engng Indust, Trans, ASME. 1973, (Ser.B):210~217
58 S.O. Duffuaa, A.N. Shuaib, M. Alam. Evaluation of Optimization Methods for Machining Economics Models. Computers Operation. 1993, 10(2):227~237
59 V. Vitanov, D. Harrison, N. Mincoff, T. Vladimir ova. An Expert System for the Selection of Metal-cutting Parameters. J Mater Process Technol. 1995, 55:111~116
60 F. Cus, J. Balic. Optimization of Cutting Process by GA. Approach Robot Comput Integr Manuf. 2003, 19(1–2):113~121
61 U. Zuperl, F. Cus. Optimization of Cutting Conditions during Cutting by Using Neural Networks. Robot Computer Integral Manufature. 2003,19(1–2):189~199
62杨勇,沈秀良,邵华.基于遗传算法的铣削参数优化.机械设计与研究. 2001,17(2):59~61
63蒋昇,李爱平,林献坤.基于遗传算法的铣削加工参数模糊优化系统研究.机电一体化. 2006, (2):35~39
64詹晓娟.基于蚁群神经网络铣削数据库系统的研究与开发.哈尔滨理工大学硕士论文,2007:30-50
65 J.C. Tsai, M.L. Lin, T.Y. Chen. Selection of Cutting Parameters for High-Speed Machining of Hardened Molding Alloy with Maximum Material Removal Rate. Proceedings of the 17th Conference on CSME. 2000:. 419-423
66吴志远,邵惠鹤,吴新余.基于遗传算法的退火精确罚函数非线性约束优化方法.控制与决策. 1998,13(2):136~140
67刘海江,黄炜.基于粒子群算法的数控加工切削参数优化.同济大学学报(自然科学版). 2008, 36(6):803~806
68张发平,王丽,闫学彬.基于CAE和神经网络的切削参数优化.机床与液压. 2007, 35(3):28~31
69 Y.C. Shin, Y.S Joo. Optimization of Machining Conditions with Practical Constraints. International Journal of Production Research. 1992, 30:2907~ 2919.
70 R. Gupta, J.L. Batra, G.K. Lal. Determination of Optimal Subdivision of Depth of Cut in Multi-pass Turning with Constraints. International Journal of Production Research. 1995, 33:2555–2565
71 K. Vijayakumar. Optimization of Multi-pass Turning Operations Using Ant Colony System. International Journal of Machine Tools & Manufacture. 2003, 43:1633–1639
72 M.C. Chen, D.M. Tsai. A Simulated Annealing Approach for Optimization of Multi-pass Turning Operations. International Journal of Production Research. 1996, 34:2803–2825
73 M.C. Chen, C.T. Su. Optimization of Machining Conditions for Turning Cylindrical Stocks into Continuous Finished Profiles, International Journal of Production Research. 1998, 36(8):2115-2130
74 P.E. Amiolemhen, A.O.A. Ibhadode. Application of Genetic Algorithms—Determination of the Optimal Machining Parameters in the Conversion of a Cylindrical Bar Stock into a Continuous Finished Profile. International Journal of Machine Tools & Manufacture. 2004, 44:1403–1412
75 R. Saravanan, P. Asokan, K. Vijayakumar. Machining Parameters Optimization for Turning Cylindrical Stock into a Continuous Finished Profile Using Genetic Algorithm (GA) and Simulated Annealing (SA). International Journal of advanced Manufacture Technology. 2003, 21:1-9
76 R. Saravanan. Comparative Analysis of Conventional and Non-conventional Optimization Techniques for CNC Turning Process, International Journal of Advanced Manufacturing Technology, 2001, (17):471–476.
77 S.V. Bhaskara Reddy, M.S. Shunmugam, T.T. Narendran. Optimal Sub-division of the Depth of Cut to Achieve Minimum Production Cost in Multi-pass Turning Using a Genetic Algorithm. Journal of Materials Processing Technology. 1998, 79:101–108
78徐德安.运用正交实验法优化切削参数.机械工人. 2000, (9):13~14
79姜彬,郑敏利,李振加.数控车削的切削用量多目标优化.哈尔滨理工大学学报. 2001, 6(2):43~46
80沈浩,谢黎明,韩莹.数控车削中切削用量的多目标优化.兰州理工大学学报. 2005, 31(5):47~49
81张臣,周来水,余湛悦等.基于仿真数据的数控铣削加工多目标变参数优化.计算机辅助设计与图形学学报. 2005, 17(5):1039~1045
82张双德.基于改进型模拟退火算法的数控加工切削参数优化.煤矿机械. 2000, (6):76~78
83姚小群,姚锡凡,王位平,陈统坚.基于熵测度的加工过程模糊控制的参数优化.机械制造. 2004, 42(7):19~21
84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.机械工艺师.1999, (2):11~12
85唐普洪,王忠伟. CNC数控加工OMAT优控系统综述. CAD/CAM与制造业信息化. 2007, 5:108~111
86 W. P. Wang, K. K. Wang. Real-time Verification of Multi-axis NC Programs with Raster Graphics. IEEE International Conference on Robotics andAutomation Proceedings, San Francisco, California. 1986: 166~171
87 W.P. Wang. Solid Modeling for Optimization Metal Removal of Three-dimensional NC End Milling. Journal of Manufacturing Systems. 1988, 7(1):57~65
88 Y.S. Tarng, S.T. Cheng. Fuzzy Control of Feed Rate in End Milling Operations. International Journal of Machine Tools and Manufacture. 1993, 33(4):643~650
89 J.H. Ko, W.S. Yun, D.W. Cho. Off-line Feed Rate Scheduling Using Virtual CNC Based on an Evaluation of Cutting Performance. Computer-Aided Design. 2003,35(4):383~393
90 H.U. Lee, D.W. Cho. Intelligent Feedrate Scheduling Based on Virtual Machining. International Journal of Advanced Manufacturing Technology. 2003,22(11-12): 873~882
91 K.K. Kim, M.C. Kang, J.S. Kim. A Study on the Precision Machinability of Ball End Milling by Cutting Speed Optimization. Journal of Materials Processing Technology. 2002,130-131:357~362
92姜彬,郑敏利,李振加.数控车削用量优化切削力约束条件的建立.机械工程师. 2002, (8):38~30
93刘长清.数控铣削离线优化技术研究.哈尔滨工业大学博士论文,2007:15-26
94汪文津,王太勇,范胜波.基于自适应遗传算法的数控铣削过程参数优化仿真.制造业自动化. 2004,26(8):28~30
95仇启源,庞思勤.现代金属切削技术. 1992:50-51
96 X. Yan, K. Shirase, M. Hirao, T. Yasui. NC Program Evaluator for Higher Machining Productivity. International Journal of Machine Tools and Manufacture. 1999, 39(10):1563~1573
97李建广,姚英学,高栋等.提高虚拟车削仿真质量的工件建模方法.计算机集成制造系统-CIMS. 2002, 8(3):233~238
98闵佳奇.布尔运算在车削加工仿真中的应用.大连交通大学硕士学位论文. 2004:25~27
99卢碧红.虚拟数控车削加工精度预测系统研究.大连铁道学院博士论文. 2003:42~43
100韩荣第,周明.金属切削原理与刀具. 1998:34-41
101陈日曜.金属切削原理.机械工业出版社, 2002:60~66
102王永章.机床的数字控制技术.哈尔滨工业大学出版. 1995:187
103杨斌久,李勋.虚拟切削加工参数的优化.组合机床与自动化加工技术. 2005, 7:12~14
104李云.切削加工金属零件表面粗糙度形成的相关因素及控制.金华职业技术学院学报. 2001, 3:29~30
105 S. Chao-Ton, Ch. Mu-Chen. Computer-Aided Optimization of Multi-pass Turning Operations for Continuous Forms on CNC Lathes. IIE Transactions. 1999, 31:583~596
106何郁波,马昌凤,田亚娟.非线性互补问题的罚函数法.桂林电子工业学院学报. 2005, 25(6):61~63
107 J.H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press. 1975:25-26
108张晓缋,方浩等.遗传算法的编码机制研究.信息与控制. 1997, 26(2):134~139
109 D.E. Goldberg. The Existential Pleasures of Genetic Algorithms. Genetic Algorithms in Engineering and Computer Science, Winter G. Wiley, 1995:23~31
2董丽华,刘大昕,赵彦玲.网络化制造中加工过程仿真的研究.工具技术. 2003, 37(112):21~23
3姚英学,李荣彬.面向加工质量预测的虚拟加工检测单元的研制.中国机械工程. 2000, 11(5):520~524
4苏新兵,张登成,王建平.虚拟制造技术在飞行器设计中的应用.现代制造工程. 2007, (2):127-129
5潘爱民.虚拟数控铣削加工系统的研究与开发.计算机工程与设计, 2004, 25(2):295-297
6卢碧红.虚拟数控车削加工精度预测系统研究.大连铁道学院博士论文. 2003:4-5
7王占礼.面向虚拟制造的数控加工仿真技术研究.吉林大学博士论文.2007,6:3-5
8张伯鹏.虚拟数控机床技术.中国机械工程. 1998,9(11):66-69
9袁一平,马雷.基于投影网格的切削加工计算机仿真.机械设计与研究. 2009, 25(2):93-95
10 L. Carrino, G. Giorleo, W. Polini, U. Prisco. Dimensional Errors in Longitudinal Turning Based On the Unified Generalized Mechanics of Cutting Approach. Part I: Three-Dimensional Theory. International Journal of Machine Tools and Manufacture. 2002, (42):1509~1515
11 Y.S. Tarng, C.I. Cheng and J.Y. Kao. Modeling of Three-Dimensional Numerically Controlled End Milling Operations. International Journal of Machine Tools and Manufacture. 1995, 35(7):939-950
12范胜波.虚拟数控车削加工质量预测系统的研究.天津大学博士论文. 2005, 12:5
13 Kang Ik Soo, Kim Jeong Suk, Seo Yong Wie. Cutting Force Model Considering Tool Edge Geometry for Micro End Milling Process. Journal of Mechanical Science and Technology. 2008, 22(2): 293-299
14 M.T. Zaman, A.S. Kumar, M. Rahman. A Three-dimensional AnalyticalCutting Force Model For Micro End Milling Operation. International Journal of Machine Tools and Manufacture. 2006, 46(3-4): 353-366
15 Z.G. Wang, M. Rahman, Y.S. Wong. A Hybrid Cutting Force Model for High-Speed Milling of Titanium Alloys. CIRP Annals - Manufacturing Technology. 2005, 54(1): 71-74
16 P.W. Tse, S. Gontarz, X.J. Wang, Enhanced Eigenvector Algorithm for Recovering Multiple Sources of Vibration Signals in Machine Fault Diagnosis. Mechanical Systems and Signal Processing. 2007, 21(7): 2794-2813
17 Xie Shun-Qiang, Yu Yue-Qing, Zhang Lan-Xia. Vibration-Characteristic Simulation of a Novel Hybrid Machine Tool. 2007, 24(11): 186-192
18 Koro?ec Marjan, Kopac, Janez. Review of New Approaches in Tool Wear Measuring Techniques Focused on Vision Systems. Tehnicki Vjesnik. 2005, 12(3-4): 17-26
19 Shi Dongfeng, Gindy Nabil. Tool Wear Predictive Model Based on Least Squares Support Vector Machines. Mechanical Systems and Signal Processing. 2007, 21(4): 1799-1814
20 M.C. Lu. Analysis of Acoustic Signals Due to Tool Wear During Machining. Michigan. The University of Michigan. 2001
21 Tanie Hisashi. Shape Prediction of Molten Micro-Solder Joints Using Particle Method and Application to Evaluation of Fracture Life. 2007 Proceedings of the ASME InterPack Conference, IPACK 2007: 87-94
22 Tower Susan, Su Bingzhi, Y.C. Lee. Yield Prediction for Flip-Chip Solder Assemblies Based on Solder Shape Modeling. IEEE Transactions on Electronics Packaging Manufacturing. 1999, 22(1): 29-37
23 Gao Tong, Zhang Weihong, Qiu Kepeng. Numerical Simulation of Machined Surface Topography and Roughness in Milling Process. Journal of Manufacturing Science and Engineering, Transactions of the ASME. 2006, 128(1): 96-103
24 Zhang Wei-Hong, Tan Gang, Wan, Min. A New Algorithm for the Numerical Simulation of Machined Surface Topography in Multi-Axis Ball-End Milling. Journal of Manufacturing Science and Engineering, Transactions of theASME. 2008, 130(1): 31-311
25 J.R. Rene Mayer, Anh-Vu Phan, Guy Cloutier. Prediction of Diameter Errors in Bar Turning: a Computationally Effective Model. Applied Mathematical Modeling. 2000,(24):943~956
26 W.J. Endres. A dynamic Model of Cutting Force System in the Turning Process. SME Symposium on Monitoring and Contro1 for Manufacturing Processes.PED,1991,(44):193~212
27 H. Zhao, J.G. Li, Y.X. Yao. Cutting Parameters Optimization for Constant Cutting Force in Milling. Applied Mechanics and Materials. 2008, (10-12): 483-487
28 Janez Kopac, Marko Bahor, Mirko Sokovic. Optimal Machining Parameters for Achieving the Desire Surface Roughness in Fine Turning of Cold Pre-formed Steel Workpieces. International Journal of Machine Tools & Manufacture. 2002, (42):707~716
29 Uros Zuperl, Franci Cus. Optimization of Cutting Conditions during Cutting by Using Neural Networks, Robotics and Computer Integrated Manufacturing. 2003, (19):189~199
30 B.Arezoo, K. Ridgwong, A.M.A. Al-Ahmari. Selection of Cutting Tools and conditions of Machining Operations Using an Expert System. Computers Industry. 2000,(42):43~58
31 Franci Cus, Joze Balic. Optimization of Cutting Process by GA Approach, Robotics and Computer Integrated Manufacturing. 2003,(19):113~121
32 B.Mursec, F. Cus. Integral Model of Selection of Optimal Cutting Conditions from Different Databases of Tool Makers, Journal of Materials Processing Technology. 2003,(133):158~165
33 K.I. Lee, S.D. Nob. Virtual Manufacturing System - a Test-Bed of Engineering Activities.Annals of the CIRP.1997, 46(1):347~350
34 Hong-Sen Yan, Jaw-Jong Chen. Creative Design of Wheel Damping Mechanism.Mech.Mach.Theory. 1985, 20(6):597~600
35 Paul K Wright.Principles of Open-Architecture Manufacturing.Journal of Manufacturing System.1995, 14(3):187~202
36 Masayoshi Tomizuka.Zero Phase Error Tracking Algorithm for DigitalControl.Transaction of ASME, Journal of Dynamic Systems, Measurement, and Control.1998, 109:65~68
37 C H Chen, K W Wang, Y C Shin.An Integrated Approach toward the Modeling and Dynamic Analysis of High Speed Spindle-Part 1: System Model.Transaction of ASME, Journal of Vibration and Acoustics.1994, 116:506~513
38 Hong-Sen Yan. A Methodology for Creative Mechanism Design. Mech. Mach. Theory.1992, 27(3):235~242
39 W.A. Kline, R.E. Devor, I.A. Shareef.The Prediction of Surface Accuracy in End Milling . Transaction of ASME, Journal of Engineering for Industry.1982, 104:272~278
40 J.W. Sutherland, R.E. Devor.An Improved Method for Cutting Force and Surface Error Presiction in Flexible End Milling Systems.Transaction of ASME, Journal of Engineering for Industry.1986,108:269~279
41 S.J. You, K.F. Ehmann. Computer Synthesis of Three-Dimensional Surface.Wear.1991, 145:29~42
42 K .F. Ehmann, M.S. Hong.A Generalized Model of the Surface Generation Process in Metal Cutting.Annals of the CIRP.1994,43(1):483~486
43 Yingxue Yao, Jianguang Li, W.B. Lee. VMMC: A Test-bed for Machining.Computers in Industry.2002, 47:255~268
44王凤彪,王秀伦,段竹.机加工用量选择专家系统.电气技术与自动化. 2006, 35(2):119~121
45 A.д.马卡洛夫.切削过程最优化.杨锦华译.国防工业出版社. 1988:63~77
46刘战强,黄传真,万熠,艾兴.切削数据库研究现状与发展.计算机集成制造系统. 2003, 9(11):37~43
47王凤彪,王秀伦,段竹.机加工用量选择专家系统.电气技术与自动化. 2006, 35(2):119~121
48彭观,陈统坚,彭劲雄,傅勇辉.基于专家系统和神经网络的制造过程智能决策系统.组合机床与自动化加工技术. 1999, (2):24~27
49 K. Hitomi. Optimization of Multistage Machining System: Analysis of Optimal Machining Conditions for the Flow-type Machining System. 1971,93:498~506
50 M. Field, N. Zlatin. Computer Approach for Storage of Machine ability Data and Calculation of Machining Costs and Production Rates. Proceedings of the 8th International MATADOR, UMIST, 1967:88~95
51 M. Field, N. Zlatin, R. Williams, M. Kronenberg. Computerized Determination and Analysis of Cost and Production Rates for Machining Operations. J Eng Ind Trans ASME. 1968, 90:455~466
52 K. Okushima and K. Hitomi. A Study of Economical Machining– an Analysis of Maximum Profit Cutting Speed. International Journal of Production Research.1966, (6): 73~78
53 P. Petropoulos. Optimal selection of machining economics problem by geometric programming. Int. J. Prod. Res. 1973,11:305~314
54 D.S. Ermer. Optimization of the Constrained Machining Economics Problem by Geometric Programming. Trans. ASME. 1971, 93:1067~1072
55 D.S. Ermer and S. Kromodihardjo. Optimization of Multipass Turning with Constrains. J. Engng indust Trans. ASME. 1981, 103: 462~468
56 K. Iwata, Y. Murotsu, T. Iwatsubo and S. Fuju. A Probabilistic Approach to the Determination of the Optimum Cutting Conditions. J. Engng indust. Trans. ASME, Ser. B, 1973, (Ser. B):1099~1107
57 K. Iwata, Y. Murotsu, F. Oba. Optimization of Cutting Conditions for Multi-pass Operations Considering Probabilistic Nature in Machining Processes. J. Engng Indust, Trans, ASME. 1973, (Ser.B):210~217
58 S.O. Duffuaa, A.N. Shuaib, M. Alam. Evaluation of Optimization Methods for Machining Economics Models. Computers Operation. 1993, 10(2):227~237
59 V. Vitanov, D. Harrison, N. Mincoff, T. Vladimir ova. An Expert System for the Selection of Metal-cutting Parameters. J Mater Process Technol. 1995, 55:111~116
60 F. Cus, J. Balic. Optimization of Cutting Process by GA. Approach Robot Comput Integr Manuf. 2003, 19(1–2):113~121
61 U. Zuperl, F. Cus. Optimization of Cutting Conditions during Cutting by Using Neural Networks. Robot Computer Integral Manufature. 2003,19(1–2):189~199
62杨勇,沈秀良,邵华.基于遗传算法的铣削参数优化.机械设计与研究. 2001,17(2):59~61
63蒋昇,李爱平,林献坤.基于遗传算法的铣削加工参数模糊优化系统研究.机电一体化. 2006, (2):35~39
64詹晓娟.基于蚁群神经网络铣削数据库系统的研究与开发.哈尔滨理工大学硕士论文,2007:30-50
65 J.C. Tsai, M.L. Lin, T.Y. Chen. Selection of Cutting Parameters for High-Speed Machining of Hardened Molding Alloy with Maximum Material Removal Rate. Proceedings of the 17th Conference on CSME. 2000:. 419-423
66吴志远,邵惠鹤,吴新余.基于遗传算法的退火精确罚函数非线性约束优化方法.控制与决策. 1998,13(2):136~140
67刘海江,黄炜.基于粒子群算法的数控加工切削参数优化.同济大学学报(自然科学版). 2008, 36(6):803~806
68张发平,王丽,闫学彬.基于CAE和神经网络的切削参数优化.机床与液压. 2007, 35(3):28~31
69 Y.C. Shin, Y.S Joo. Optimization of Machining Conditions with Practical Constraints. International Journal of Production Research. 1992, 30:2907~ 2919.
70 R. Gupta, J.L. Batra, G.K. Lal. Determination of Optimal Subdivision of Depth of Cut in Multi-pass Turning with Constraints. International Journal of Production Research. 1995, 33:2555–2565
71 K. Vijayakumar. Optimization of Multi-pass Turning Operations Using Ant Colony System. International Journal of Machine Tools & Manufacture. 2003, 43:1633–1639
72 M.C. Chen, D.M. Tsai. A Simulated Annealing Approach for Optimization of Multi-pass Turning Operations. International Journal of Production Research. 1996, 34:2803–2825
73 M.C. Chen, C.T. Su. Optimization of Machining Conditions for Turning Cylindrical Stocks into Continuous Finished Profiles, International Journal of Production Research. 1998, 36(8):2115-2130
74 P.E. Amiolemhen, A.O.A. Ibhadode. Application of Genetic Algorithms—Determination of the Optimal Machining Parameters in the Conversion of a Cylindrical Bar Stock into a Continuous Finished Profile. International Journal of Machine Tools & Manufacture. 2004, 44:1403–1412
75 R. Saravanan, P. Asokan, K. Vijayakumar. Machining Parameters Optimization for Turning Cylindrical Stock into a Continuous Finished Profile Using Genetic Algorithm (GA) and Simulated Annealing (SA). International Journal of advanced Manufacture Technology. 2003, 21:1-9
76 R. Saravanan. Comparative Analysis of Conventional and Non-conventional Optimization Techniques for CNC Turning Process, International Journal of Advanced Manufacturing Technology, 2001, (17):471–476.
77 S.V. Bhaskara Reddy, M.S. Shunmugam, T.T. Narendran. Optimal Sub-division of the Depth of Cut to Achieve Minimum Production Cost in Multi-pass Turning Using a Genetic Algorithm. Journal of Materials Processing Technology. 1998, 79:101–108
78徐德安.运用正交实验法优化切削参数.机械工人. 2000, (9):13~14
79姜彬,郑敏利,李振加.数控车削的切削用量多目标优化.哈尔滨理工大学学报. 2001, 6(2):43~46
80沈浩,谢黎明,韩莹.数控车削中切削用量的多目标优化.兰州理工大学学报. 2005, 31(5):47~49
81张臣,周来水,余湛悦等.基于仿真数据的数控铣削加工多目标变参数优化.计算机辅助设计与图形学学报. 2005, 17(5):1039~1045
82张双德.基于改进型模拟退火算法的数控加工切削参数优化.煤矿机械. 2000, (6):76~78
83姚小群,姚锡凡,王位平,陈统坚.基于熵测度的加工过程模糊控制的参数优化.机械制造. 2004, 42(7):19~21
84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.机械工艺师.1999, (2):11~12
85唐普洪,王忠伟. CNC数控加工OMAT优控系统综述. CAD/CAM与制造业信息化. 2007, 5:108~111
86 W. P. Wang, K. K. Wang. Real-time Verification of Multi-axis NC Programs with Raster Graphics. IEEE International Conference on Robotics andAutomation Proceedings, San Francisco, California. 1986: 166~171
87 W.P. Wang. Solid Modeling for Optimization Metal Removal of Three-dimensional NC End Milling. Journal of Manufacturing Systems. 1988, 7(1):57~65
88 Y.S. Tarng, S.T. Cheng. Fuzzy Control of Feed Rate in End Milling Operations. International Journal of Machine Tools and Manufacture. 1993, 33(4):643~650
89 J.H. Ko, W.S. Yun, D.W. Cho. Off-line Feed Rate Scheduling Using Virtual CNC Based on an Evaluation of Cutting Performance. Computer-Aided Design. 2003,35(4):383~393
90 H.U. Lee, D.W. Cho. Intelligent Feedrate Scheduling Based on Virtual Machining. International Journal of Advanced Manufacturing Technology. 2003,22(11-12): 873~882
91 K.K. Kim, M.C. Kang, J.S. Kim. A Study on the Precision Machinability of Ball End Milling by Cutting Speed Optimization. Journal of Materials Processing Technology. 2002,130-131:357~362
92姜彬,郑敏利,李振加.数控车削用量优化切削力约束条件的建立.机械工程师. 2002, (8):38~30
93刘长清.数控铣削离线优化技术研究.哈尔滨工业大学博士论文,2007:15-26
94汪文津,王太勇,范胜波.基于自适应遗传算法的数控铣削过程参数优化仿真.制造业自动化. 2004,26(8):28~30
95仇启源,庞思勤.现代金属切削技术. 1992:50-51
96 X. Yan, K. Shirase, M. Hirao, T. Yasui. NC Program Evaluator for Higher Machining Productivity. International Journal of Machine Tools and Manufacture. 1999, 39(10):1563~1573
97李建广,姚英学,高栋等.提高虚拟车削仿真质量的工件建模方法.计算机集成制造系统-CIMS. 2002, 8(3):233~238
98闵佳奇.布尔运算在车削加工仿真中的应用.大连交通大学硕士学位论文. 2004:25~27
99卢碧红.虚拟数控车削加工精度预测系统研究.大连铁道学院博士论文. 2003:42~43
100韩荣第,周明.金属切削原理与刀具. 1998:34-41
101陈日曜.金属切削原理.机械工业出版社, 2002:60~66
102王永章.机床的数字控制技术.哈尔滨工业大学出版. 1995:187
103杨斌久,李勋.虚拟切削加工参数的优化.组合机床与自动化加工技术. 2005, 7:12~14
104李云.切削加工金属零件表面粗糙度形成的相关因素及控制.金华职业技术学院学报. 2001, 3:29~30
105 S. Chao-Ton, Ch. Mu-Chen. Computer-Aided Optimization of Multi-pass Turning Operations for Continuous Forms on CNC Lathes. IIE Transactions. 1999, 31:583~596
106何郁波,马昌凤,田亚娟.非线性互补问题的罚函数法.桂林电子工业学院学报. 2005, 25(6):61~63
107 J.H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press. 1975:25-26
108张晓缋,方浩等.遗传算法的编码机制研究.信息与控制. 1997, 26(2):134~139
109 D.E. Goldberg. The Existential Pleasures of Genetic Algorithms. Genetic Algorithms in Engineering and Computer Science, Winter G. Wiley, 1995:23~31