[1] LI B, LI L J, HE H B, et al. Research on modal analysis method of CNC machine tool based on operational impact excitation[J]. The International Journal of Advanced Manufacturing Technology, 2019, 103:1155-1174. [2] ZHOU W, ZHU X, WANG J, et al. A new error prediction method for machining process based on a combined model[J]. Mathematical Problems in Engineering, 2018, 2018:3703861. [3] XU L H, HUANG C Z, LI C W, et al. An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining[J]. Journal of Intelligent Manufacturing, 2021, 32:313-327. [4] SZCZEPANSKI R, ERWINSKI K, PAPROCKI M. Accelerating PSO based feedrate optimization for NURBS toolpaths using parallel computation with OpenMP[C]//201722nd International Conference on Methods and Models in Automation and Robotics (MMAR). Piscataway:IEEE Press, 2017:431-436. [5] PRASANTH R S S, HANS RAJ K. Optimization of straight cylindrical turning using artificial bee colony (ABC) algorithm[J]. Journal of the Institution of Engineers (India):Series C, 2017, 98(2):171-177. [6] 李丽, 邓兴国, 尚川博. 面向能效的曲面数控加工刀具路径优化方法[J]. 机械工程学报, 2017, 53(11):184-194. LI L, DENG X G, SHANG C B. Tool path optimization with high-efficiency and low energy consumption for surface CNC machining[J]. Journal of Mechanical Engineering, 2017, 53(11):184-194(in Chinese). [7] LI X X, LI W D, HE F Z. A multi-granularity NC program optimization approach for energy efficient machining[J]. Advances in Engineering Software, 2018, 115:75-86. [8] SOEPANGKAT B O P, PRAMUJATI B, EFFENDI M K, et al. Multi-objective optimization in drilling Kevlar fiber reinforced polymer using grey fuzzy analysis and backpropagation neural network-genetic algorithm (BPNN-GA) approaches[J]. International Journal of Precision Engineering and Manufacturing, 2019, 20(4):593-607. [9] ZHANG Z X, LUO M, ZHANG D H, et al. A force-measuring-based approach for feed rate optimization considering the stochasticity of machining allowance[J]. The International Journal of Advanced Manufacturing Technology, 2018, 97:2545-2556. [10] RAO R V, PAWAR P J. Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms[J]. Applied Soft Computing, 2010, 10(2):445-456. [11] 王鑫. 基于加工特征的机床关键零件高效数控加工切削参数优化技术研究[D]. 株洲:湖南工业大学, 2014. WANG X. Machining feature based cutting conditions optimization for high performance CNC machining of key machine parts[D]. Zhuzhou:Hunan University of Technology, 2014(in Chinese). [12] JANG D Y, JUNG J, SEOK J. Modeling and parameter optimization for cutting energy reduction in MQL milling process[J]. International Journal of Precision Engineering and Manufacturing-Green Technology, 2016, 3(1):5-12. [13] ZHAO X, ZHAO H, YANG J X, et al. An adaptive feedrate scheduling method with multi-constraints for five-axis machine tools[M]//LIU H, KUBOTA N, ZHU X, et al. Intelligent Robotics and Applications. Cham:Springer, 2015:553-564. [14] XU G D, CHEN J H, ZHOU H C, et al. Multi-objective feedrate optimization method of end milling using the internal data of the CNC system[J]. The International Journal of Advanced Manufacturing Technology, 2019, 101:715-731. [15] ZHANG W B, REN J X. Improving the machining dynamics performance and efficiency for complex channel part manufacturing by planning the tool orientations and feed rate sequence[J]. The International Journal of Advanced Manufacturing Technology, 2020, 107:3663-3689. [16] PARK H S, NGUYEN T T, KIM J C. An energy efficient turning process for hardened material with multi-criteria optimization[J]. Transactions of FAMENA, 2016, 40(1):1-14. [17] VU N C, DANG X P, HUANG S C. Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition[J]. Measurement and Control, 2021, 54(5-6):820-834. [18] MA J W, JIA Z Y, SONG D N, et al. Machining error reduction by combining of feed-speed optimization and toolpath modification in high-speed machining for parts with rapidly varied geometric features[J]. Proceedings of the Institution of Mechanical Engineers, Part C:Journal of Mechanical Engineering Science, 2018, 232(4):557-571. [19] DONG J C, WANG T Y, LI B, et al. Smooth feedrate planning for continuous short line tool path with contour error constraint[J]. International Journal of Machine Tools and Manufacture, 2014, 76:1-12. [20] MAIYAR L M, RAMANUJAM R, VENKATESAN K, et al. Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysis[J]. Procedia Engineering, 2013, 64:1276-1282. [21] 董雪娇. 基于BP和GA的叶片加工切削用量优化选择方法[D]. 西安:西安工业大学, 2014. DONG X J. Optimal selection of cutting parameters in blade NC machining based on BP neural network and genetic algorithm[D]. Xi'an:Xi'an Technological University, 2014(in Chinese). [22] YAN J H, LI L. Multi-objective optimization of milling parameters-the trade-offs between energy, production rate and cutting quality[J]. Journal of Cleaner Production, 2013, 52:462-471. [23] 孙成龙. 基于机床反馈数据和工艺知识的进给速度优化[D]. 武汉:华中科技大学, 2016. SUN C L. Feedrate optimization based on feedback data of machine tools and process knowledge[D]. Wuhan:Huazhong University of Science and Technology, 2016(in Chinese). [24] NGUYEN T T, CAO L H, DANG X P, et al. Multi-objective optimization of the flat burnishing process for energy efficiency and surface characteristics[J]. Materials and Manufacturing Processes, 2019, 34(16):1888-1901. [25] ALVAREZ J, BARRENETXEA D, MARQUINEZ J I, et al. Continuous variable feed rate:A novel method for improving infeed grinding processes[J]. The International Journal of Advanced Manufacturing Technology, 2014, 73:53-61. [26] 韩江, 张国政. 内齿珩轮强力珩齿齿面粗糙度预测与工艺参数优化[J]. 计算机集成制造系统, 2019, 25(2):403-411. HAN J, ZHANG G Z. Gear teeth surface roughness prediction and process parameters optimization on power honing with internal teeth honing wheel[J]. Computer Integrated Manufacturing Systems, 2019, 25(2):403-411(in Chinese). [27] LIU B Q, XU M J, FANG J P, et al. A feedrate optimization method for CNC machining based on chord error revaluation and contour error reduction[J]. The International Journal of Advanced Manufacturing Technology, 2020, 111:3437-3452. [28] ZHOU J F, SUN Y W, GUO D M. Adaptive feedrate interpolation with multiconstraints for five-axis parametric toolpath[J]. The International Journal of Advanced Manufacturing Technology, 2014, 71(9-12):1873-1882. [29] YANG J X, ASLAN D, ALTINTAS Y. A feedrate scheduling algorithm to constrain tool tip position and tool orientation errors of five-axis CNC machining under cutting load disturbances[J]. CIRP Journal of Manufacturing Science and Technology, 2018, 23:78-90. [30] LEE C H, YANG F Z, ZHOU H C, et al. Cross-directional feed rate optimization using tool-path surface[J]. The International Journal of Advanced Manufacturing Technology, 2020, 108(7-8):2645-2660. [31] BOSETTI P, BERTOLAZZI E. Feed-rate and trajectory optimization for CNC machine tools[J]. Robotics and Computer-Integrated Manufacturing, 2014, 30(6):667-677. [32] LAVERNHE S, TOURNIER C, LARTIGUE C. Kinematical performance prediction in multi-axis machining for process planning optimization[J]. The International Journal of Advanced Manufacturing Technology, 2008, 37(5-6):534-544. [33] BEUDAERT X, LAVERNHE S, TOURNIER C. Feedrate interpolation with axis jerk constraints on 5-axis NURBS and G1 tool path[J]. International Journal of Machine Tools and Manufacture, 2012, 57:73-82. [34] ERKORKMAZ K, LAYEGH S E, LAZOGLU I, et al. Feedrate optimization for freeform milling considering constraints from the feed drive system and process mechanics[J]. CIRP Annals, 2013, 62(1):395-398. [35] SUN Y W, ZHAO Y, XU J T, et al. The feedrate scheduling of parametric interpolator with geometry, process and drive constraints for multi-axis CNC machine tools[J]. International Journal of Machine Tools and Manufacture, 2014, 85:49-57. [36] BHARATHI A, DONG J Y. Feedrate optimization for smooth minimum-time trajectory generation with higher order constraints[J]. The International Journal of Advanced Manufacturing Technology, 2016, 82(5-8):1029-1040. [37] CHEN J, REN F, SUN Y W. Contouring accuracy improvement using an adaptive feedrate planning method for CNC machine tools[J]. Procedia CIRP, 2016, 56:299-305. [38] NI H P, ZHANG C R, JI S, et al. A bidirectional adaptive feedrate scheduling method of NURBS interpolation based on S-shaped ACC/DEC algorithm[J]. IEEE Access, 2018, 6:63794-63812. [39] WANG L, CAO J F. A look-ahead and adaptive speed control algorithm for high-speed CNC equipment[J]. The International Journal of Advanced Manufacturing Technology, 2012, 63:705-717. [40] HE G Y, MA W K, YU G M, et al. Modeling and experimental validation of cutting forces in five-axis ball-end milling based on true tooth trajectory[J]. The International Journal of Advanced Manufacturing Technology, 2015, 78:189-197. [41] GUO Q, ZHAO B, JIANG Y, et al. Cutting force modeling for non-uniform helix tools based on compensated chip thickness in five-axis flank milling process[J]. Precision Engineering, 2018, 51:659-681. [42] QI H J, TIAN Y L, ZHANG D W. Machining forces prediction for peripheral milling of low-rigidity component with curved geometry[J]. The International Journal of Advanced Manufacturing Technology, 2013, 64:1599-1610. [43] ZHANG X, ZHANG J, PANG B, et al. An accurate prediction method of cutting forces in 5-axis flank milling of sculptured surface[J]. International Journal of Machine Tools and Manufacture, 2016, 104:26-36. [44] CAI Y J, SHEN H, QI T L. Research on feedrate optimization based on cutting force model with double effect for the ball-end milling[J]. Advanced Materials Research, 2011, 291-294:2965-2969. [45] WANG L P, YUAN X, SI H, et al. Feedrate scheduling method for constant peak cutting force in five-axis flank milling process[J]. Chinese Journal of Aeronautics, 2020, 33(7):2055-2069. [46] 刘献礼, 丁云鹏, 岳彩旭, 等. 基于载荷控制的拐角铣削进给优化[J]. 机械工程学报, 2016, 52(19):189-196. LIU X L, DING Y P, YUE C X, et al. Feedrate optimization based on load control for corner-milling[J]. Journal of Mechanical Engineering, 2016, 52(19):189-196(in Chinese). [47] ZUPERL U, CUS F, REIBENSCHUH M. Neural control strategy of constant cutting force system in end milling[J]. Robotics and Computer-Integrated Manufacturing, 2011, 27(3):485-493. [48] PARK H S, QI B W, DANG D V, et al. Development of smart machining system for optimizing feedrates to minimize machining time[J]. Journal of Computational Design and Engineering, 2018, 5(3):299-304. [49] 郝文峰. 切削力指令域示波器设计及加工优化研究[D]. 武汉:华中科技大学, 2017. HAO W F. Development of the cutting force instruction domain oscilloscope and research on processing optimization[D]. Wuhan:Huazhong University of Science and Technology, 2017(in Chinese). [50] ZHANG C. Off-line feedrate optimization based on simulation of cutting forces[J]. Key Engineering Materials, 2009, 407-408:408-411. [51] 张臣, 周来水, 安鲁陵, 等. 基于铣削力仿真的离线进给速度优化技术[J]. 南京航空航天大学学报, 2009, 41(3):358-363. ZHANG C, ZHOU L S, AN L L, et al. Optimization of off-line feedrate based on simulation of milling forces[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2009, 41(3):358-363(in Chinese). [52] 黄博豪. 航空叶片切削进给优化研究[D]. 武汉:华中科技大学, 2015. HUANG B H. Federate scheduling for NC machining of aero engine fan blades[D]. Wuhan:Huazhong University of Science and Technology, 2015(in Chinese). [53] 付中涛, 杨文玉, 张彦辉, 等. 基于切削力预测模型的复杂曲面铣削进给速度优化[J]. 中国科学:技术科学, 2016, 46(7):722-730. FU Z T, YANG W Y, ZHANG Y H, et al. Feedrate optimization of complex surface milling based on predictive model of cutting force[J]. Scientia Sinica (Technologica), 2016, 46(7):722-730(in Chinese). [54] LIANG S Y, HECKER R L, LANDERS R G. Machining process monitoring and control:The state-of-the-art[J]. Journal of Manufacturing Science and Engineering, 2004, 126(2):297-310. [55] MATSUBARA A, IBARAKI S. Monitoring and control of cutting forces in machining processes:A review[J]. International Journal of Automation Technology, 2009, 3(4):445-456. [56] KIM S I, LANDERS R G, ULSOY A G. Robust machining force control with process compensation[J]. Journal of Manufacturing Science and Engineering, 2003, 125(3):423-430. [57] 唐琳. 基于主轴电流的恒负荷加工控制方法的研究与实现[D]. 武汉:华中科技大学, 2005. TANG L. Research and implement of constant cutting load machining control method based on spindle motor current[D]. Wuhan:Huazhong University of Science and Technology, 2005(in Chinese). [58] 胡世广. 具备智能特征的开放式数控系统构建技术研究[D]. 天津:天津大学, 2008. HU S G. Research on technology of building open CNC system with intelligent characteristic[D]. Tianjin:Tianjin University, 2008(in Chinese). [59] CUS F, MILFELNER M, BALIC J. An intelligent system for monitoring and optimization of ball-end milling process[J]. Journal of Materials Processing Technology, 2006, 175(1-3):90-97. [60] 郑金兴, 张铭钧, 孟庆鑫. 铣削加工过程监测和优化智能系统开发[J]. 仪器仪表学报, 2007, 28(S1):74-78. ZHENG J X, ZHANG M J, MENG Q X. Development of an intelligent system for monitoring and optimization for CNC milling[J]. Chinese Journal of Scientific Instrument, 2007, 28(S1):74-78(in Chinese). [61] 柳万珠, 刘强. 切削加工过程的在线监测与自适应控制[J]. 航空制造技术, 2012, 55(14):86-90. LIU W Z, LIU Q. Online monitoring and adaptive control for cutting process[J]. Aeronautical Manufacturing Technology, 2012, 55(14):86-90(in Chinese). [62] 韩振宇, 金鸿宇, 付云忠, 等. 基于有限元数值模型和进给速度优化的薄壁件侧铣变形在线控制[J]. 机械工程学报, 2017, 53(21):190-199. HAN Z Y, JIN H Y, FU Y Z, et al. FEM numerical model and feedrate optimization based on-line deflection control of thin-walled parts in flank milling[J]. Journal of Mechanical Engineering, 2017, 53(21):190-199(in Chinese). [63] RIDWAN F, XU X, HO F C L. Adaptive execution of an NC program with feed rate optimization[J]. The International Journal of Advanced Manufacturing Technology, 2012, 63(9-12):1117-1130. [64] LIAN R J, LIN B F, HUANG J H. A grey prediction fuzzy controller for constant cutting force in turning[J]. International Journal of Machine Tools and Manufacture, 2005, 45(9):1047-1056. [65] 林献坤, 李爱平, 张为民. 应用基于学习的模糊逻辑智能选择铣削加工参数[J]. 中国机械工程, 2007, 18(9):1076-1080. LIN X K, LI A P, ZHANG W M. Application of learning based fuzzy logic in intelligent selection of machining parameters for milling process[J]. China Mechanical Engineering, 2007, 18(9):1076-1080(in Chinese). [66] KIM D, JEON D. Fuzzy-logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors[J]. Precision Engineering, 2011, 35(1):143-152. [67] LIN J, LIAN R J. Hybrid self-organizing fuzzy and radial basis-function neural-network controller for constant cutting force in turning[J]. The International Journal of Advanced Manufacturing Technology, 2011, 53:921-933. [68] HABER R, DEL TORO R M, GODOY J, et al. Intelligent tuning of fuzzy controllers by learning and optimization[M]//Atlantis Computational Intelligence Systems. Paris:Atlantis Press, 2014:135-158. [69] NJIRI J G, IKUA B W, NYAKOE G N. Feedrate optimization for ball-end milling of sculptured surfaces using fuzzy logic controller[J]. World Academy of Science Engineering and Technology, 2011, 5(8):1660-1668. [70] 赵建华, 牟恩旭. 基于i5OS系统的主轴切削负载在线自适应优化应用程序[J]. 机械制造, 2019, 57(11):74-77. ZHAO J H, MU E X. Online adaptive optimization APP for spindle cutting load based on i5OS system[J]. Machinery, 2019, 57(11):74-77(in Chinese). [71] 雷萍. 基于现场总线的恒功率约束切削加工自适应控制[J]. 现代制造工程, 2007(7):40-42. LEI P. Adaptive control of milling process based on fieldbus and constant spindle power constrain[J]. Modern Manufacturing Engineering, 2007(7):40-42(in Chinese). [72] 黄华, 李爱平, 林献坤. 基于恒功率约束的自适应加工控制系统开发[J]. 同济大学学报(自然科学版), 2008, 36(7):956-961. HUANG H, LI A P, LIN X K. Fuzzy control system for milling machining[J]. Journal of Tongji University (Natural Science), 2008, 36(7):956-961(in Chinese). [73] 徐剑, 叶文华, 胡国志, 等. 基于开放式数控系统的恒功率自适应控制研究[J]. 制造技术与机床, 2015(8):38-42. XU J, YE W H, HU G Z, et al. Research on constant power adaptive control based on open CNC system[J]. Manufacturing Technology & Machine Tool, 2015(8):38-42(in Chinese). [74] SARHAN A A D, MATSUBARA A. Investigation about the characterization of machine tool spindle stiffness for intelligent CNC end milling[J]. Robotics and Computer-Integrated Manufacturing, 2015, 34:133-139. [75] 李曦, 曹为. 基于RBF神经网络的恒铣削功率控制的研究[J]. 机械, 2006, 33(12):22-24, 33. LI X, CAO W. Study on the constant milling power control based on the RBF neural network[J]. Machinery, 2006, 33(12):22-24, 33(in Chinese). [76] 赖兴余, 叶邦彦, 鄢春艳, 等. 基于现场总线的铣削加工过程自适应模糊控制[J]. 华南理工大学学报(自然科学版), 2005, 33(5):7-10. LAI X Y, YE B Y, YAN C Y, et al. Adaptive fuzzy control of milling process based on fieldbus[J]. Journal of South China University of Technology (Natural Science), 2005, 33(5):7-10(in Chinese). [77] NIKOL'SKII A A, KOROLEV V V. Adaptive control in high-speed electric drives of a lathe feed for noncircular turning[J]. Russian Electrical Engineering, 2016, 87(4):231-234. [78] PROSKURYAKOV N A, NEKRASOV R Y, STARIKOV A I, et al. Fuzzy controllers in the adaptive control system of a CNC lathe[J]. Russian Engineering Research, 2018, 38(3):220-222. [79] SIMSIR U. Torque-controlled adaptive speed control on a CNC marble saw machine[J]. Advances in Mechanical Engineering, 2015, 7(2):839827. [80] 唐普洪, 王忠伟. CNC数控加工OMAT优控系统综述[J]. 智能制造, 2007(5):108-111. TANG P H, WANG Z W. Overview of OMAT optimal control system for CNC machining[J]. Intelligent Manufacturing, 2007(5):108-111(in Chinese). [81] 王星辉, 罗丰, 卢秀丽, 等. ARTIS刀具监控软件在油孔钻机床上的开发应用[J]. 工业, 2015, 10(20):125-127. WANG X H, LUO F, LU X L, et al. Development and application of ARTIS tool monitoring software on oil hole drilling machine tools[J]. Industry, 2015, 10(20):125-127(in Chinese). [82] LI D D, XU M M, WEI C J, et al. A dynamic threshold-based fuzzy adaptive control algorithm for hard sphere grinding[J]. The International Journal of Advanced Manufacturing Technology, 2012, 60:923-932. [83] D'URSO P, GIL M Á. Fuzzy data analysis and classification[J]. Advances in Data Analysis and Classification, 2017, 11(4):645-657. [84] KONDRATENKO Y P, KOZLOV A V. Generation of rule bases of fuzzy systems based on modified ant colony algorithms[J]. Journal of Automation and Information Sciences, 2019, 51(3):4-25. [85] 邵克勇, 张鸿雁, 李飞, 等. 一种基于GA的模糊控制规则优化新方法[J]. 化工自动化及仪表, 2011, 38(3):261-264, 306. SHAO K Y, ZHANG H Y, LI F, et al. GA-based new optimization method for fuzzy control rules[J]. Control and Instruments in Chemical Industry, 2011, 38(3):261-264, 306(in Chinese). [86] REN A H, BAI J J, MA J X. Research on intelligent transportation system based on fuzzy neural network[J]. IOP Conference Series:Materials Science and Engineering, 2020, 768:062114. [87] ZUPERL U, CUS F, REIBENSCHUH M. Modeling and adaptive force control of milling by using artificial techniques[J]. Journal of Intelligent Manufacturing, 2012, 23(5):1805-1815. [88] ZHANG L. A study on fuzzy control learning algorithms based on cascade observers[J]. International Journal of Simulation:Systems, Science & Technology, 2016, 17(39):1-6. [89] ZUPERL U, CUS F. Simulation and visual control of chip size for constant surface roughness[J]. International Journal of Simulation Modelling, 2015, 14(3):392-403. [90] 陈立军, 赵丽丽, 周正兴. 基于规则自寻优的过热汽温模糊控制[J]. 化工自动化及仪表, 2010, 37(1):5-7, 15. CHEN L J, ZHAO L L, ZHOU Z X. Fuzzy control of superheated steam temperature based on rules self-optimizing[J]. Control and Instruments in Chemical Industry, 2010, 37(1):5-7, 15(in Chinese). [91] 高亮, 杨扬, 李新宇. 数控加工参数优化的研究现状与进展[J]. 航空制造技术, 2010, 53(22):48-51. GAO L, YANG Y, LI X Y. Research and development of optimization of NC machining parameters[J]. Aeronautical Manufacturing Technology, 2010, 53(22):48-51(in Chinese). [92] CUS F, ZUPERL U, KIKER E, et al. Adaptive self-learning controller design for feedrate maximisation of machining process[J]. Journal of Achievements in Materials and Manufacturing Engineering, 2008, 31(2):469-476. [93] XIONG G, LI Z L, DING Y, et al. Integration of optimized feedrate into an online adaptive force controller for robot milling[J]. The International Journal of Advanced Manufacturing Technology, 2020, 106:1533-1542. [94] 李杰. 基于自适应控制技术的铣削参数优化研究[D]. 天津:天津大学, 2016. LI J. Research on optimization of milling parameters based on adaptive control technology[D]. Tianjin:Tianjin University, 2016(in Chinese). [95] ZUPERL U, CUS F. System for off-line feedrate optimization and neural force control in end milling[J]. International Journal of Adaptive Control and Signal Processing, 2012, 26(2):105-123. [96] ZUPERL U. Swarm intelligence combined with neural network objective function modelling for turning process optimization[J]. Proceedings in Manufacturing Systems, 2013, 8(2):69-74. [97] 刘恒丽, 王勇, 董靖川. 数控铣削加工参数离线-在线优化研究及应用[J]. 机械工程与技术, 2020, 9(1):13-25. LIU H L, WANG Y, DONG J C. Research and application of offline-online optimization of CNC milling processing parameters[J]. Mechanical Engineering and Technology, 2020, 9(1):13-25(in Chinese). [98] LIU H L, WANG T Y, WANG D. Constant cutting force control for CNC machining using dynamic characteristic-based fuzzy controller[J]. Shock and Vibration, 2015, 2015:406294. [99] 刘恒丽. 面向机床功效的数控铣削智能优化技术及应用研究[D]. 天津:天津大学, 2017. LIU H L. Research on intelligent optimization technology and application of NC milling process for machine tool efficiency[D]. Tianjin:Tianjin University, 2017(in Chinese). |