硬态切削过程建模技术研究发展

doi:10.16182/j.issn1004731x.joss.18-0719

摘要/Abstract

摘要： 硬态切削以加工柔性好、经济性和环保性好等特点，在汽车行业、轴承行业、液压行业等被广泛采用。而针对于预测硬态切削加工性能和优化加工参数等方面，仿真建模技术不仅起到了重要作用，还能更好地反映切削加工本质，对实际生产加工具有一定的理论指导作用。对国内外硬态切削仿真建模方法进行了介绍，综述了切屑形成、切削力、切削温度、刀具磨损和已加工表面质量的仿真建模方法，并对其今后的发展方向进行了初步的展望。

关键词: 硬态切削, 建模, 研究进展, 切削力, 刀具磨损

Abstract: The hard cutting has the features of the high maching flexibility, economical efficiency and environmental friendliness, and is widely used in automobile industry, bearing industry, hydraulic industry, etc. The simulation modeling not only plays an important role in predicting the performance of the hard machining and optimizing machining parameters, but also better reflects the nature of the machining and provides the theoretical guidance to the actual machining. The simulation modeling methods of the hard cutting at home and abroad are introduced, and the chip formation, cutting force, cutting temperature, tool wear and machined surface quality are summarized, and the future development is preliminarily forecasted.

Key words: hard cutting, modeling, research progress, cutting force, tool wear

中图分类号:

TP391.9

岳彩旭，刘鑫，姜男，刘献礼，何耿煌，李凌祥. 硬态切削过程建模技术研究发展[J]. 系统仿真学报, 2020, 32(6): 982-999.

Yue Caixu, Liu Xin, Jiang Nan, Liu Xianli, He Genghuang, Li Lingxiang. Research on Modeling Technology for Hard Cutting Process[J]. Journal of System Simulation, 2020, 32(6): 982-999.

参考文献

[1] 童万恒. 刀具主要几何角度选择及硬态切削技术[J].华东科技(学术版), 2016, 13(10): 344.
Tong Wanheng. Selection of main geometric angles of cutter and hard cutting technology[J]. East China Science and Technology(Academic Edition), 2016, 13(10): 344.
[2] 岳彩旭, 刘献礼, 姬生园, 等. 硬态切削技术[J]. 航空制造技术, 2008, 1(18):26-29.
Yue Caixu, Liu Xianli, Ji Shengyuan, et al. Hard cutting technology [J]. Aerospace Manufacturing Technology, 2008, 1(18): 26-29.
[3] Chinchanikar S, Choudhury S K. Machining of hardened steel—Experimental investigations, performance modeling and cooling techniques: A review[J]. International Journal of Machine Tools & Manufacture (S0890-6955), 2015, 89(13): 95-109.
[4] 杨奇彪. 高速切削锯齿形切屑的形成机理及表征[D].济南: 山东大学, 2012.
Yang Qibiao. Formation mechanism and characterization of serrated chips for high-speed cutting[D]. Ji’nan: Shandong University, 2012.
[5] 中山一雄. 金属切削加工理论[M]. 李云芳译. 北京:机械工业出版社, 1985.
Zhongshan Yixiong. Metal cutting theory[M]. Translated by Li Yunfang. Beijing: China machine press, 1985.
[6] Hou Z B, Ranga K. On a thermo-mechanical model of shearinstability inmachining[J]. CIRP Annals (S0007-8506), 1995, 44(1): 69-73.
[7] Manyindo B M, Oxley P L B. Modelling the Catastrophic Shear Type of Chip When Machining Stainless Steel[J]. Proceedings of the Institution of Mechanical Engineers Part C:Journal of Mechanical Engineering Science (S0954-4062), 1986, 200(53): 349-358.
[8] Vyas A. Mechanics of saw-tooth chip formation in metal cutting[J]. Journal of Manufacturing Science and Engineering-Transactions of the Asme (S1087-1357), 1999, 122(2): 163.
[9] 王敏杰, 胡荣生, 刘培德. 金属材料热塑剪切失稳临界条件的切削法测定[J]. 兵器材料科学与工程, 1990, 1: 25-30.
Wang Minjie, Hu Rongsheng, Liu Peide. cutting method for determination of critical conditions of thermoplastic shear instability in metallic materials[J]. Journal of Weapon Materials Science and Engineering, 1990, 1: 25-30.
[10] Schulz H, Abele E. Materials aspects of saw- tooth chip formation in HSC machining[J]. CIRP (S0007-8506), 2001, 50(1): 45-48.
[11] Molinari A, Musquar C, Sutter G. Adiabatic shear banding in high speed machining of Ti-6Al-4V: experiments and modeling[J]. International Journal of Plasticity (S0749-6419), 2002, 18(4): 443-459.
[12] Ekinovic S, Begovic E. An approach to determine transition area from conventional to high-speed machining by means of chip shape analysis[J]. Achieves of Materials Science (S0138-032X), 2007(28): 35-39.
[13] 施志辉, 赵玢, 王启义, 等. 金属切削加工切屑形成过程仿真技术[J]. 大连交通大学学报, 2000, 21(3): 64-68.
Shi Zhihui, Zhao Ben, Wang Qiyi, et al. Simulation of metal cutting process chip formation process[J]. Journal of Dalian Jiaotong University, 2000, 21(3): 64-68.
[14] Pawade R S, Sonawane H A, Joshi S S. An Analytical Model to Predict Specific Shear Energy in High-Speed Turning of Inconel 718[J]. International Journal of Machine Tools and Manufacture (S0890-6955), 2009, 49(12/13): 979-990.
[15] 吴申峰, 张雪萍. 淬硬轴承钢锯齿形切屑形成机理[J].上海交通大学学报, 2011, 45(1): 71-77.
Wu Shenfeng, Zhang Xueping. Formation mechanism of steel serrated chip of hardened bearing[J]. Journal of Shanghai Jiaotong University, 2011, 45(1): 71-77.
[16] 合烨, 王昌赢, 陈小安, 等. 硬态车削轴承钢GCr15 切屑形成机理分析[J]. 上海交通大学学报, 2013, 47(5): 800-805.
He Ye, Wang Changying, Chen Xiaoan, et al. Analysis of the formation mechanism of hard turning bearing steel GCr15 chips[J]. Journal of Shanghai Jiaotong University, 2013, 47(5): 800-805.
[17] Wang C, Ding F, Tang D, et al. Modeling and simulation of the high-speed milling of hardened steel SKD11 (62 HRC) based on SHPB technology[J]. International Journal of Machine Tools and Manufacture (S0890-6955), 2016, 108(13): 13-26.
[18] 杜倩倩. 硬态车削淬硬钢切屑形成机理及表面完整性研究[D]. 济南: 济南大学, 2016.
Du Qianqian. Study on the formation mechanism and surface integrity of hardened steel chips by turning them in hard state[D]. Jinan: Jinan University, 2016.
[19] Meddour I, Yallese M A, Khattabi R, et al. Investigation and modeling of cutting forces and surface roughness when hard turning of AISI 52100 steel with mixed ceramic tool: cutting conditions optimization[J]. International Journal of Advanced Manufacturing Technology (S0268-3768), 2015, 77(5/8): 1387-1399.
[20] Liu C R, Mittal S. Single-step superfinish hard machining: Feasibility and feasible cutting conditions[J]. Robotics & Computer-Integrated Manufacturing (S0736-5845), 1996, 12(1): 15-27.
[21] Ding T, Zhang S, Wang Y, et al. Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel[J]. International Journal of Advanced Manufacturing Technology (S0268-3768), 2010, 51(1-4): 45-55.
[22] Azizi M W, Belhadi S, Yallese M A, et al. Surface roughness and cutting forces modeling for optimization of machining condition in finish hard turning of AISI 52100 steel[J]. Journal of Mechanical Science & Technology (S1738-494X), 2012, 26(12): 4105-4114.
[23] Bouacha K, Yallese M A, Mabrouki T, et al. Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool[J]. International Journal of Refractory Metals & Hard Materials (S0263-4368), 2010, 28(3): 349-361.
[24] Liu X L, Yuan Z J, Li Z J. Cutting temperature of quench beiarng steel GCr15 wth PCBN cutter[J]. Chinese Jounral of Mechanical Engineeirng (S1000-9345),1999, 12(4): 254-259.
[25] Wan M, Zhang W H, Qin G H, et al. Efficient calibration of instantaneous cutting force coefficients and runout parameters for general end mills[J]. International Journal of Machine Tools &Manufacture (S0890-6955), 2007, 47: 1767-1776.
[26] Budak E, Altintas Y, Armarego E J A. Prediction of Milling Force coefficients From Orthogonal Cutting Data[J]. Journal of Manufacturing Science and Engineering (S1087-1357), 1996, 118(2): 216-224.
[27] Bao W Y, Tansel I N. Modeling micro-end-milling operations. Part I: analytical cutting force model[J]. International Journal of Machine Tools & Manufacture (S0890-6955), 2000, 40(15): 2155-2173.
[28] Smith S, Tlusty J. An Overview of Modeling and Simulation of the Milling Process[J]. Journal of Engineering for Industry (S0022-0817), 1991, 113(2): 169-175.
[29] Tuysuz O, Altintas Y, Feng H Y. Prediction of cutting forces in hree and five－axis ball－end milling with tool indentation effect[J]. International Journal of Machine Tools ＆ Manufacture (S0890-6955), 2013, 66(2): 66-81.
[30] Tuysuz O, Altintas Y, Feng H Y. Prediction of cutting forces in inclined surface ball-end milling[C]//Applied Mechanics: Canadian Congress, 2011.
[31] Okafor A C, Sultan A A. Development of a mechanistic cutting orce model for wavy-edge bull-nose helical end-milling of inconel718 under emulsion cooling strategy[J]. Applied Mathematical Modelling (S0307-904X), 2016, 40(4) : 2637-2660.
[32] Bhopale N N, Pawade R S, Joshi S S, et al. Analysis and modeling of Cutting Forces in Ball end Milling of Superalloy Inconel 718[J]. Journal of the Institution of Engineers(India): Mechanical Engineering Division (S0020-3408), 2011, 92: 11-18.
[33] Huang Y, Liang S Y, Woodruff G W. Modeling of cutting forces under hard turning conditions considering tool wear effect[J]. Journal of Manufacturing Science & Engineering (S1087-1357), 2005, 127(2): 262-270.
[34] 杨琳. 球头铣刀铣削淬硬钢模具铣削力及模具加工误差研究[D]. 哈尔滨: 哈尔滨理工大学, 2017.
Yang Lin. Study on milling force and machining error of hardened steel dies by ball end milling cutter[D]. Harbin: Harbin University of Technology, 2017.
[35] 吴石, 杨琳, 刘献礼, 等. 覆盖件模具曲面曲率特征对球头刀铣削力的影响[J]. 机械工程学报, 2017, 53(13): 188-198.
Wu Shi, Yang Lin, Liu Xianli, et al. Effect of curvature characteristics of cover mold surface on milling force of ball end cutter[J]. Journal of Mechanical Engineering, 2017, 53(13): 188-198.
[36] 李刚, 孔凡志, 王慧强, 等. 多硬度拼接淬硬Cr12MoV 铣削力的有限元分析[J]. 机械设计与研究, 2014, 30(5): 131-135.
Li Gang, Kong Fanzhi, Wang Huiqinag, et al. Finite element analysis of splicing hardened Cr12MoV milling forces with multiple hardness[J]. Mechanical Design and Research, 2014, 30(5): 131-135.
[37] 高世龙, 安立宝. CBN 刀具干式硬车切削力有限元仿真[J]. 机械设计与研究, 2016, 32(2): 131-134.
Gao Shilong, An Libao. Finite element simulation of cutting force of CBN tool dry hard vehicle[J]. Mechanical Design and Research, 2016, 32(2): 131-134.
[38] 王小纯, 曹景源, 郑涛. 人工神经网络预测高速铣削淬硬钢圆弧切削力[J]. 机械制造, 2009, 47(9): 56-58.
Wang Xiaochun, Cao Jingyuan, Zheng Tao. Artificial neural network prediction of high speed milling of hardened steel circular arc cutting force[J]. Mechanical Manufacturing, 2009, 47(9): 56-58.
[39] 曹登. 基于BP 神经网络的等温淬火球墨铸铁的切削力预测[D]. 苏州: 苏州大学, 2013.
Cao Deng. Prediction of cutting force of isothermal quenched ductile iron based on BP neural network[D]. Suzhou: Soochow University, 2013.
[40] Dosbaeva G K, El Hakim M A, Shalaby M A, et al. Cutting temperature effect on PCBN and CVD coated carbide tools in hard turning of D2 tool steel[J]. International Journal of Refractory Metals and Hard Materials(S0263-4368), 2015, 50(13): 1-8.
[41] 李林文. 面向硬切削的切削区域温度场解析建模及实验研究[D]. 武汉: 华中科技大学, 2013.
Li Linwen. Analytical modeling and experimental study of temperature field in cutting area for hard cutting[D]. Wuhan: Huazhong University of Science and Technology, 2013.
[42] Yan S, Zhu D, Zhuang K, et al. Modeling and analysis of coated tool temperature variation in dry milling of Inconel 718 turbine blade considering flank wear effect[J]. Journal of Materials Processing Technology (S0924-0136), 2014, 214(12): 2985-3001.
[43] Shihab S K, Khan Z A, Mohammad A, et al. RSM based Study of Cutting Temperature During Hard Turning with Multilayer Coated Carbide Insert[J]. Procedia Materials Science (S2211-8128), 2014, 6: 1233-1242.
[44] Stephenson D A. Tool-work thermocouple temperature measurements-theory and implementation issues[J]. Journal of Engineering for Industry (S0022-0817), 1993, 115(4): 432-437.
[45] Hirao M. Determining temperature distribution on flank face of cutting tool[J]. Journal of Materials Shaping Technology(S0931-704X), 1989, 6(3): 143-148.
[46] 段春争, 李园园, 李国和, 等. 高速切削温度场测量技术研究现状[J]. 机械设计与制造, 2008(4): 212-214.
Duan Chunzheng, Li Yuanyuan, Li Guohe, et al. The present status of measuring technique of temperature field during high speed machining[J]. Machinery Design and Manufacture, 2008(4): 212-214.
[47] Wu, Han, Duong Nick H, Ma J, et al. CEL FEM Investigation of effects of microgrooved cutting tools in high speed machining of AISI 1045 steel[C]. ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017.
[48] Du Jin, Zhang Jingjie, Wang Liguo. Heat partition and rake face temperature in the machining of H13 steel with coated cutting tools[J]. The International Journal of Advanced Manufacturing Technology (S0268-3768), 2018, 94(9/12): 3691-3702.
[49] 周浩东. 拼接淬硬钢铣削过程数值模拟及稳定性研究[D]. 杭州: 浙江工业大学, 2013.
Zhou Haodong. Numerical simulation and stability study of splicing hardened steel milling process[D]. Hangzhou: Zhejiang University of Technology, 2013.
[50] 杜倩倩, 付秀丽, 谢安冉, 等. 涂层刀具硬态车削H13钢温度场模拟研究[J]. 工具技术, 2016, 50(2): 20-23.
Du Qianqian, Fu Xiuli, Xie Anran, et al. Simulation study of H13 steel temperature field in hard turning of coated cutting tools[J]. Tool Technology, 2016, 50(2): 20-23.
[51] Kara F, AslantaŞ K, Cicek A. Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network[J]. Applied Soft Computing (S1568-4946), 2016, 38(13): 64-74.
[52] Mia M, Dhar N R. Response surface and neural network based predictive models of cutting temperature in hard turning[J]. Journal of Advanced Research (S2090-1232), 2016, 7(6): 1035-1044.
[53] 郭松. 刀具磨损引起的工件加工误差建模与补偿技术研究[D]. 南京: 南京航空航天大学, 2012.
Guo Song. Modeling and compensation technology of workpiece machining error caused by tool wear[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2012.
[54] Aouici H, Yallese M A, Chaoui K, et al. Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization[J]. Measurement (S0263-2241), 2012, 45(3): 344-353.
[55] Feng C X, Wang X F. Surface roughness predictive modeling: neural networks versus regression[J]. IIE Transactions (S0740-817X), 2003, 35(1): 11-27.
[56] Sahoo A, Rout A, Das D. Response surface and artificial neural network prediction model and optimization for surface roughness in machining[J]. International Journal of Industrial Engineering Computations (S1923-2934), 2015, 6(2): 229-240.
[57] Özel T, Karpat Y. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks[J]. International Journal of Machine Tools and Manufacture (S0890-6955), 2005, 45(4/5): 467-479.
[58] AkkuŞ H, Asilturk ｉ. Predicting surface roughness of AISI 4140 steel in hard turning process through artificial neural network, fuzzy logic and regression models[J]. Scientific Research and Essays(S1992-2248), 2011, 6(13): 2729-2736.
[59] Mia M, Dhar N R. Modeling of Surface Roughness Using RSM, FL and SA in Dry Hard Turning[J]. Arabian Journal for Science and Engineering(S2193-567X), 2017: 1-12.
[60] Iqbal A, He N, Li L, et al. A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process[J]. Expert Systems with Applications (S0957-4174), 2007, 32(4): 1020-1027.
[61] Wu D W, Matsumoto Y. The effect of hardness on residual stresses in orthogonal machining of AISI 4340 steel/[J]. Annals of the New York Academy of Sciences (S0077-8923), 1990, 509(3): 89-102.
[62] Huang K, Yang W. Analytical modeling of residual stress formation in workpiece material due to cutting[J]. International Journal of Mechanical Sciences (S0020-7403), 2016, 114(13): 21-34.
[63] Ambrogio G, Filice L, Shivpuri R, et al. Application of NN technique for predicting the in-depth residual stresses during hard machining of AISI 52100 steel[J]. International Journal of Material Forming (S1960-620), 2008, 1(1): 39-45.
[64] Umbrello D, Ambrogio G, Filice L, et al. An ANN approach for predicting subsurface residual stresses and the desired cutting conditions during hard turning[J]. Journal of Materials Processing Technology (S0924-0136), 2007, 189(1/3): 143-152.
[65] 徐颖强, 李娜娜, 李万钟, 等. 硬态切削工件表面白层厚度预测方法[J]. 机械工程学报, 2014, 51(3): 182-189.
Xu Yingqiang, Li Nana, Li Wanzhong, et al. Prediction method of white layer thickness on surface of hard cutting workpieces[J]. Journal of Mechanical Engineering, 2015, 51(3): 182-189.
[66] Umbrello D, Jawahir I S. Numerical modeling of the influence of process parameters and workpiece hardness on white layer formation in AISI 52100 steel[J]. International Journal of Advanced Manufacturing Technology (S0268-33768), 2009, 44(9/10): 955-968.
[67] 孔维森. 高速切削淬硬钢已加工表面白层和残余应力的预测与实验研究[D]. 大连: 大连理工大学, 2013.
Kong Weisen. Prediction and experimental study of white layer and residual stress on the processed surface of hardened steel by high-speed cutting[D]. Dalian: Dalian University of Technology, 2013.
[68] Caruso S, Outeiro J C, Umbrello D, et al. Modeling and experimental validation of the surface residual stresses induced by hard machining of AISI H13 tool steel[J]. International Journal of Material Forming (S1960-6206), 2010, 3(1): 515-518.
[69] 岳彩旭. 硬态切削过程的有限元仿真与实验研究[D].哈尔滨: 哈尔滨理工大学, 2010.
Yue Caixu. Finite element simulation and experimental study of hard cutting process[D]. Harbin: Harbin University of Technology, 2010.
[70] Singh D, Rao P V. Flank wear prediction of ceramic tools in hard turning[J]. International Journal of Advanced Manufacturing Technology (S0268-3768), 2010, 50(5/8): 479-493.
[71] Huang Y, Liang S Y. Modeling of CBN tool flank wear progression in finish hard turning[J]. Journal of Manufacturing Science and Engineering (S1087-1357), 2004, 126(1): 98-106.
[72] Chinchanikar S, Choudhury S K. Predictive modeling for flank wear progression of coated carbide tool in turning hardened steel under practical machining conditions[J]. International Journal of Advanced Manufacturing Technology (S0268-3768), 2015, 76(5/8): 1185-1201.
[73] Farahnakian M, Elhami S, Daneshpajooh H, et al. Mechanistic modeling of cutting forces and tool flank wear in the thermally enhanced turning of hardened steel[J]. The International Journal of Advanced Manufacturing Technology (S0268-3768), 2017, 88(9/12): 2969-2983.
[74] 徐创文, 陈花玲, 刘晓斌. 偏最小二乘回归在刀具磨损试验建模中的应用[J]. 系统仿真学报, 2007, 19(13): 3115-3118.
Xu Chuangwen, Chen Hualing, Liu Xiaobin. Application of partial least squares regression in modeling of tool wear test[J]. Journal of System Simulation, 2007, 19(13): 3115-3118.
[75] Kishawy H A, Pang L, Balazinski M. Modeling of tool wear during hard turning with self-propelled rotary tools[J]. International Journal of Mechanical Sciences (S0020-7403), 2011, 53(11): 1015-1021.
[76] Rao K V, Murthy B S N, Rao N M. Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network[J]. Measurement (S0263-2241), 2014, 51(13): 63-70.
[77] Salvatore F, Saad S, Hamdi H. Modeling and simulation of tool wear during the cutting process[J]. Procedia CIRP (S2212-8271), 2013, 8(13): 305-310.
[78] 程红玫, 高有山. 超高强度钢切削仿真和刀具磨损率建模研究[J]. 组合机床与自动化加工技术, 2017, 37(6): 145-149.
Cheng Hongmei, Gao Youshan. Simulation of ultra-high-strength steel cutting and modeling of tool wear rate[J]. Combined Machine Tools and Automatic Processing Technology, 2017, 37(6): 145-149.
[79] 张佳奕. 基于有限元仿真的PCBN 刀具磨损研究[D].哈尔滨: 哈尔滨理工大学, 2014.
Zhang Jiayi. Research on PCBN tool wear based on finite element simulation[D]. Harbin: Harbin University of Technology, 2014.