Journal of Mechanical Engineering ›› 2023, Vol. 59 ›› Issue (19): 429-459.doi: 10.3901/JME.2023.19.429
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DING Mingna1,2, LIU Xianli1, YUE Caixu1, FAN Mengchao1, GU Hao1
Received:2023-06-06
Revised:2023-08-08
Online:2023-10-05
Published:2023-12-11
CLC Number:
DING Mingna, LIU Xianli, YUE Caixu, FAN Mengchao, GU Hao. Design, Manufacturing, Management and Control Technology of Cutting Tools for Intelligent Manufacturing Process[J]. Journal of Mechanical Engineering, 2023, 59(19): 429-459.
| [1] WANG B,TAO F,FANG X,et al. Smart manufacturing and intelligent manufacturing:A comparative review[J]. Engineering,2021,7(6):738-57. [2] 刘献礼,刘强,岳彩旭,等.切削过程中的智能技术[J].机械工程学报,2018,54(16):45-61. LIU Xianli,LIU Qiang,YUE Caixu,et al. Intelligent machining technology in cutting process[J]. Journal of Mechanical Engineering,2018,54(16):45-61. [3] LI X B,LIU X L,YUE C X,et al. Systematic review on tool breakage monitoring techniques in machining operations[J]. International Journal of Machine Tools and Manufacture,2022,176:103882. [4] 陶飞,戚庆林.面向服务的智能制造[J].机械工程学报,2018,54(16):11-23. TAO Fei,QI Qinglin. Service-oriented smart manufacturing[J]. Journal of Mechanical Engineering,2018,54(16):11-23. [5] GAO R X,WANG L,HELU M,et al. Big data analytics for smart factories of the future[J]. CIRP Annals,2020,69(2):668-692. [6] CHENG Y,YANG J,QIN C,et al. Tool design and cutting parameter optimization for side milling blisk[J]. The International Journal of Advanced Manufacturing Technology,2018,100(9-12):2495-2508. [7] FAN M,BI C,YUE C,et al. Research on double-arc cutting tool design and cutting performance. Applied Science. 2023,13:3689. [8] 刘献礼,范梦超,计伟,等. 椭球头铣刀设计及其刀具路径生成算法[J]. 机械工程学报,2018,54(15):199-212. LIU Xianli,FAN Mengchao,JI Wei,et al. Ellipsoid end mill design and tool path generation algorithm[J]. Journal of Mechanical Engineering,2018,54(15):199-212. [9] CHANG F,ZHOU G,XIAO X,et al. A function availability-based integrated product-service network model for high-end manufacturing equipment[J]. Computers & Industrial Engineering,2018,126:302-316. [10] 刘献礼,李雪冰,丁明娜,等. 面向智能制造的刀具全生命周期智能管控技术[J]. 机械工程学报,2021,57(10):196-219. LIU Xianli,LI Xuebing,DING Mingna,et al. Intelligent management and control technology of cutting tool life-cycle for Intelligent Manufacturing[J]. Journal of Mechanical Engineering,2021,57(10):196-219. [11] 叶文昌,郭必成,邓朝晖,等. 刀具智能化关键技术的研究进展及发展趋势[J/OL]. 机械工程学报:1-18[2023- 05-22].http://kns.cnki.net/kcms/detail/11.2187.TH.20230214.1445.016.html YE Wenchang,GUO Bicheng,DENG Zhaohui,et al. Advances in key technologies of the intelligence tool[J/OL]. Journal of Mechanical Engineering:1-18[2023-05-22].http://kns.cnki.net/kcms/detail/11.2187.TH.20230214.1445.016.html [12] MOHANRAJ T,SHANKAR S,RAJASEKAR R,et al. Tool condition monitoring techniques in milling process-a review[J]. Journal of Materials Research and Technology,2020,9(1):1032-1042. [13] WONG S Y,CHUAH J H,YAP H J. Technical data-driven tool condition monitoring challenges for CNC milling:A review[J]. The International Journal of Advanced Manufacturing Technology,2020,107(11-12):4837-4857. [14] LIU X,FAN M,JI W,et al. Research on models of design and NC manufacturing for ellipsoid end mill[J]. The International Journal of Advanced Manufacturing Technology,2015,85(9-12):2729-2744. [15] LIN Z,YUE C,HU D,et al. Research and development of parametric design platform for series complex cutting tools[J]. The International Journal of Advanced Manufacturing Technology,2022,121(9-10):6325-6340. [16] 李国超,孙杰,李剑峰,等. 整体式立铣刀三维精确建模与软件实现[J]. 计算机辅助设计与图形学学报,2014,26(3):411-417. LI Guochao,SUN Jie,LI Jianfeng,et al. 3D accurate modeling and software realization of integral end milling cutter[J]. Journal of Computer Aided Design and Graphics,2014,26(3):411-417. [17] LI A,ZHAO J,PEI Z,et al. Simulation-based solid carbide end mill design and geometry optimization[J]. The International Journal of Advanced Manufacturing Technology,2014,71(9-12):1889-1900. [18] 陈妮,闫博,袁媛,等. 微铣刀参数化设计系统研究[J].机械工程学报,2020,56(23):185-192. CHEN Ni,YAN Bo,YUAN Yuan,et al. Research on parametric design system of micro-mills[J]. Journal of Mechanical Engineering,2020,56(23):185-192. [19] DING M N,JI W,WANG Y W,et al. Cutting Tool CAD/CAE Integrating System Modeling[J]. Materials Science Forum,2014,800-801:698-702. [20] 牛壮. 球头铣刀几何参数集成仿真优化方法[D]. 哈尔滨理工大学,2019. NIU Zhuang. Integrated simulation and optimization method of ball-end milling cutter geometric parameters[D]. Harbin University of Science and Technology,2019. [21] BINDER M,KLOCKE F,DOEBBELER B. An advanced numerical approach on tool wear simulation for tool and process design in metal cutting[J]. Simulation Modelling Practice and Theory,2017,70:65-82. [22] NIE W,ZHENG M,XU S,et al. Stability analysis and structure optimization of unequal-pitch end mills[J]. Materials (Basel),2021,14(22). [23] CHANG S L,TSENG H C,HSIEH J K,et al. Optimum design of a cutting tool for manufacturing rotary knives[J]. Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science,2008,223(2):463-472. [24] JI W,ZHAO Z M,LIU X L. A GA-based optimization algorithm for cutting tool "shape-performance- application" integrated design approach[J]. Procedia CIRP,2016,56:90-94. [25] 李锋,曹一凡,刘维伟,等. 基于萤火虫算法的CFRP材料铣削刀具结构优化[J]. 宇航材料工艺,2019,49(1):21-25. LI Feng,CAO Yifan,LIU Weiwei,et al. Structure optimization of CFRP milling tool based on Firefly algorithm[J]. Aerospace Materials Technology,2019,49(01):21-25. [26] 岳彩旭,刘鑫,刘智博,等. 基于有限元仿真的拼接模具铣削用刀具优化[J]. 中国机械工程,2020,31(17):2085-2094. YUE Caixu,LIU Xin,LIU Zhibo,et al. Tool optimization for splicing die milling based on finite element simulation[J]. China Mechanical Engineering,2020,31(17):2085- 2094. [27] CHIBANE H,DUBOIS S,DE GUIO R. Innovation beyond optimization:Application to cutting tool design[J]. Computers & Industrial Engineering,2021,154. [28] 刘献礼,计伟,范梦超,等. 基于特征的刀具"形-性-用"一体化设计方法[J]. 机械工程学报,2016,52(11):146-153. LIU Xianli,JI Wei,FAN Mengchao,et al. Feature based cutting tool "Shape-Performance-Application" integrating design approach[J]. Journal of Mechanical Engineering,2016,52(11):146-153. [29] JI W,ZHAO Z M,LIU X L. Tool shape-performance- application integrated design approach:a development and a numerical validation[J]. The International Journal of Advanced Manufacturing Technology,2017,94(5-8):1711-1717. [30] ZHAO Z M,LIU X L,YUE C X,et al. Time-varying analytical model of ball-end milling tool wear in surface milling[J]. The International Journal of Advanced Manufacturing Technology,2020,108(4):1109-1123. [31] 刘强,张海军,刘献礼,等. 智能刀具研究综述[J]. 机械工程学报,2021,57(21):248-268. LIU Qiang,ZHANG Haijun,LIU Xianli,et al. A review of research on intelligent cutting tools[J]. Journal of Mechanical Engineering,2021,57(21):248-268. [32] PANZERA T H,SOUZA P R,RUBIO J C C,et al. Development of a three-component dynamometer to measure turning force[J]. The International Journal of Advanced Manufacturing Technology,2011,62(9-12):913-922. [33] 赵友,葛晓慧,赵玉龙. 高精度动态切削力自感知智能刀具的研究[J]. 机械工程学报,2019,55(21):178-185. ZHAO You,GE Xiaohui,ZHAO Yulong. Research on high precision dynamic cutting force self-sensing intelligent tool[J]. Journal of Mechanical Engineering,2019,55(21):178-185. [34] 李林文,李斌,KORNEL F E. 面向硬切削的切削区域温度场解析建模及实验研究[J].机械工程学报,2015,51(10):40. LI Linwen,LI Bin,KORNEL F E. Analytical modeling and experimental study of temperature field in cutting zone for hard cutting[J]. Journal of Mechanical Engineering,2015,51(10):40. [35] 殷增斌,郝肖华,陈为友,等. 一种新型切削温度感知智能刀具研究[J]. 机械工程学报,2023,59(1):242-248. YIN Zengbin,HAO Xiaohua,CHEN Weiyou,et al. Study on a new type of cutting temperature sensing smart tool[J]. Journal of Mechanical Engineering,2023,59(1):242-248. [36] LUO M,LUO H,AXINTE D,et al. A wireless instrumented milling cutter system with embedded PVDF sensors[J]. Mechanical Systems and Signal Processing,2018,110:556-568. [37] HUANG S,TAO B,LI J,et al. Estimation of the time and space-dependent heat flux distribution at the tool-chip interface during turning using an inverse method and thin film thermocouples measurement[J]. The International Journal of Advanced Manufacturing Technology,2018,99(5-8):1531-1543. [38] DEVILLEZ A,DUDZINSKI D. Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers[J]. Mechanical Systems and Signal Processing,2007,21(1):441-456. [39] HERRERA-GRANADOS G,MORITA N,HIDAI H,et al. Development of a non-rigid micro-scale cutting mechanism applying a normal cutting force control system[J]. Precision Engineering,2016,43:544-553. [40] SUN X,BATEMAN R,CHENG K,et al. Design and analysis of an internally cooled smart cutting tool for dry cutting[J]. Proceedings of the Institution of Mechanical Engineers,Part B:Journal of Engineering Manufacture,2011,226(4):585-591. [41] LIANG L,QUAN Y. Investigation of heat partition in dry turning assisted by heat pipe cooling[J]. The International Journal of Advanced Manufacturing Technology,2012,66(9-12):1931-1941. [42] KURIYAMA K,FUKUTA M,SEKIYA K,et al. Applying constant pressure unit to ductile mode cutting of hard and brittle materials[J]. International Journal of Automation Technology,2013,7(3):278-284. [43] DOBROTA D,RACZ S G,OLEKSIK M,et al. Smart cutting tools used in the processing of aluminum alloys[J]. Sensors (Basel),2021,22(1):28. [44] WANG H Y,LIU X L,YUE C X,et al. A novel grinding method for the flute with the complex edge by standard wheel[J]. The International Journal of Advanced Manufacturing Technology,2022,125(1-2):577-586. [45] LI G,SUN J,LI J. Modeling and analysis of helical groove grinding in end mill machining[J]. Journal of Materials Processing Technology,2014,214(12):3067-3076. [46] CHEN Z,LIU X,HE G,et al. An iteration-based algorithm for two-pass flute grinding of slide round milling tools[J]. The International Journal of Advanced Manufacturing Technology,2020,111(9-10):2533-2543. [47] 贾康,洪军,张银行. 一种拉刀螺旋容屑槽前刀面磨削砂轮安装位姿计算方法[J]. 机械工程学报,2019,55(11):205-214. JIA Kang,HONG Jun,ZHANG Yinhang. An approach on wheel setup calculation for helical rake flank sharpening of broaching tool[J]. Journal of Mechanical Engineering,2019,55(11):205-214. [48] 李国超,周宏根,景旭文,等.基于小生境粒子群算法的刀具容屑槽刃磨工艺设计[J].计算机集成制造系统,2019,25(07):1746-1756. LI Guochao,ZHOU Honggen,JING Xuwen,et al. Process design of tool chip groove grinding based on niche particle swarm optimization[J]. Computer Integrated Manufacturing Systems,2019,25(07):1746- 1756. [49] LI G. A new algorithm to solve the grinding wheel profile for end mill groove machining[J]. The International Journal of Advanced Manufacturing Technology,2016,90(1-4):775-784. [50] 李海宾,王成兵,马玉豪,等. 一种粗铣刀周齿分屑槽的数控磨削工艺算法[J/OL].机械科学与技术:1-6[2023-05-22].DOI:10.13433/j.cnki.1003-8728. 20220071. LI Haibin,WANG Chengbing,MA Yuhao,et al. A week of rough milling cutter tooth points crumbs of CNC grinding process algorithm[J/OL]. Mechanical Science and Technology:1-6[2023-05-22]. DOI:10.13433/j.carol carroll nki. 1003-8728.20220071. [51] LI G,ZHOU H,JING X,et al. An intelligent wheel position searching algorithm for cutting tool grooves with diverse machining precision requirements[J]. International Journal of Machine Tools and Manufacture,2017,122:149-160. [52] DENKENA B,DITTRICH M A,Böß V,et al. Self-optimizing process planning for helical flute grinding[J]. Production Engineering,2019,13(5):599-606. [53] KARPUSCHEWSKI B,JANDECKA K,MOUREK D. Automatic search for wheel position in flute grinding of cutting tools[J]. CIRP Annals,2011,60(1):347-350. [54] LI G,LIU J,ZHOU H,et al. Modeling and analysis of variable-core groove for integral cutting tools[J]. Proceedings of the Institution of Mechanical Engineers,Part E:Journal of Process Mechanical Engineering,2017,232(4):471-479. [55] LI G,DAI L,LIU J,et al. An approach to calculate grinding wheel path for complex end mill groove grinding based on an optimization algorithm[J]. Journal of Manufacturing Processes,2020,53:99-109. [56] LIU X L,WANG S P,YUE C X,et al. Numerical calculation of grinding wheel wear for spiral groove grinding[J]. The International Journal of Advanced Manufacturing Technology,2022,120(5-6):3393-3404. [57] Bianchi G,Leonesio M. Hybrid machine learning model-based approach for Intelligent Grinding[C]. I-RIM 2019. [58] 曾志伟,蒋代君. 刀具切削刃钝圆半径的图像测量法[J]. 组合机床与自动化加工技术,2014(9):88-91. ZENG Zhiwei,JIANG Daijun. Image measurement method of blunt radius of tool cutting edge[J]. Modular Machine Tool & Automatic Manufacturing Technique,2014(9):88-91. [59] 吴一全,龙云淋,周杨. 基于Arimoto熵和Zernike矩的刀具图像亚像素边缘检测[J]. 华南理工大学学报,2017,45(12):50-56. WU Yiquan,LONG Yunlin,ZHOU Yang. Subpixel edge detection of tool image based on arimoto entropy and zernike moment[J]. Journal of South China University of Technology (Natural Science Edition),2017,45(12):50-56. [60] JANG S,SHIMIZU Y,ITO S,et al. Development of an optical probe for evaluation of tool edge geometry[J]. Journal of Advanced Mechanical Design,Systems,and Manufacturing,2014,8(4):63-73. [61] ZHAO C Y,XUE W,FU W P,LI Z Q and FANG X M. Defect sample image generation method based on gans in diamond tool defect detection[J]. IEEE Transactions on Instrumentation and Measurement,2023,72:1-9. [62] ZHANG T J,ZHANG C R,WANG Y J,et al. A vision-based fusion method for defect detection of milling cutter spiral cutting edge[J]. Measurement,2021,177:1-14. [63] AREZOO B,RIDGWAY K,AL-AHMARI A. Selection of cutting tools and conditions of machining operations using an expert system[J]. Computers in Industry,2000,42(1):43-58. [64] 周敏,于谋雨,郑国磊. 基于遗传算法的火箭贮箱壁板数控加工刀具优选方法[J].计算机集成制造系统,2023,29(2):385-391. ZHOU Min,YU Mouyu,ZHENG Guolei. Optimization method of tool for NC machining of rocket tank wall plate based on genetic algorithm[J]. Computer Integrated Manufacturing Systems,2023,29(2):385-391. [65] MIZUGAKI Y,HAO M,SAKAMOTO M,et al. Optimal tool selection based on genetic algorithm in a geometric cutting simulation[J]. CIRP Annals,1994,43(1):433-436. [66] 吴宝海,梁满仓,张莹,等.复杂曲面通道多轴加工的刀具选择方法[J].机械工程学报,2018,54(3):117-124. WU Baohai,LIANG Mancang,ZHANG Ying,et al. Tool selection of multi-axis machining for channel parts with sculptured surface[J]. Journal of Mechanical Engineering,2018,54(3):117-124. [67] DUAN Y,HOU L,LENG S. A novel cutting tool selection approach based on a metal cutting process knowledge graph[J]. The International Journal of Advanced Manufacturing Technology,2021,112(11-12):3201-3214. [68] JI W,WANG L,HAGHIGHI A,et al. An enriched machining feature based approach to cutting tool selection[J]. International Journal of Computer Integrated Manufacturing,2017,31(1):1-10. [69] ZHOU G,YANG X,ZHANG C,et al. Deep learning enabled cutting tool selection for special-shaped machining features of complex products[J]. Advances in Engineering Software,2019,133:1-11. [70] 田长乐,周光辉,张俊杰,等. 碳排放约束下基于特征的刀具选配与切削参数集成优化[J]. 计算机集成制造系统,2020,26(8):2060-2072. TIAN Changle,ZHOU Guanghui,ZHANG Junjie,et al. Tool selection and integrated optimization of cutting parameters based on feature under carbon emission constraints[J]. Computer Integrated Manufacturing Systems,2020,26(8):2060-2072. [71] ZHU K,LIN X. Tool condition monitoring with multiscale discriminant sparse decomposition[J]. IEEE Transactions on Industrial Informatics,2019,15(5):2819-2827. [72] KONG D,CHEN Y,LI N. Gaussian process regression for tool wear prediction[J]. Mechanical Systems and Signal Processing,2018,104:556-574. [73] ZHU K,LI G,ZHANG Y. Big data oriented smart tool condition monitoring system[J]. IEEE Transactions on Industrial Informatics,2020,16(6):4007-4016. [74] WANG R,SONG Q,PENG Y,et al. A milling tool wear monitoring method with sensing generalization capability[J]. Journal of Manufacturing Systems,2023,68:25-41. [75] WANG R,SONG Q,PENG Y,et al. Self-adaptive fusion of local-temporal features for tool condition monitoring:A human experience free model[J]. Mechanical Systems and Signal Processing,2023,195:110310. [76] MAREI M,ZAATARI S E,LI W. Transfer learning enabled convolutional neural networks for estimating health state of cutting tools[J]. Robotics and Computer-Integrated Manufacturing,2021,71:102145. [77] LI Y,ZHAO Z,FU Y,et al. A novel approach for tool condition monitoring based on transfer learning of deep neural networks using time-frequency images[J]. Journal of Intelligent Manufacturing,2023:1-13. [78] LIU C,LI Y,WANG Q,et al. A synchronous association approach of geometry,process and monitoring information for intelligent manufacturing[J]. Robotics and Computer-Integrated Manufacturing,2019,58:120-129. [79] AN Q,TAO Z,XU X,et al. A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network[J]. Measurement,2020,154:107461. [80] WANG Y,ZHENG L,WANG Y. Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet[J]. Journal of Manufacturing Systems,2021,58:205-222. [81] LUO W,HU T,YE Y,et al. A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin[J]. Robotics and Computer-Integrated Manufacturing,2020,65:101974. [82] BI Q,WANG X,WU Q,et al. Fv-SVM-based wall-thickness error decomposition for adaptive machining of large skin parts[J]. IEEE Transactions on Industrial Informatics,2019,15(4):2426-2434. [83] HAN Z,JIN H,HAN D,et al. ESPRIT- and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system[J]. The International Journal of Advanced Manufacturing Technology,2016,89(9-12):2731-2746. [84] XU L,HUANG C,LI C,et al. Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining[J]. Journal of Intelligent Manufacturing,2020,32(1):77-90. [85] LI C,ZHAO X,CAO H,et al. A data and knowledge driven cutting parameter adaptive optimization method considering dynamic tool wear[J]. Robotics and Computer-Integrated Manufacturing,2023,81:102491. [86] WANG S M,LEE C Y,GUNAWAN H,et al. Intelligent air cutting monitoring system for milling process[J]. Applied Sciences,2022,12(9):4137. [87] CHEN X,LI C,TANG Y,et al. Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time[J]. Energy,2019,175:1021-1037. [88] WARD R,SUN C,DOMINGUEZ-CABALLERO J,et al. Machining Digital Twin using real-time model-based simulations and lookahead function for closed loop machining control[J]. The International Journal of Advanced Manufacturing Technology,2021,117(11-12):3615-3629. [89] TONG X,LIU Q,PI S,et al. Real-time machining data application and service based on IMT digital twin[J]. Journal of Intelligent Manufacturing,2019,31(5):1113-1132. [90] LIU L,ZHANG X,WAN X,et al. Digital twin-driven surface roughness prediction and process parameter adaptive optimization[J]. Advanced Engineering Informatics,2022,51:101470. [91] XIE Y,LIAN K,LIU Q,et al. Digital twin for cutting tool:Modeling,application and service strategy[J]. Journal of Manufacturing Systems,2021,58:305-312. [92] HOSSAIN M S J,SARKER B R. Inventory policy and magazine reloading schedule optimisation for cutting tools in pipe manufacturing[J]. International Journal of Production Research,2021,60(16):5029-5050. [93] CHENG C,BARTON D,PRABHU V. The servicisation of the cutting tool supply chain[J]. International Journal of Production Research,2008,48(1):1-19. [94] WANG G,NAKAJIMA H,YAN Y,et al. A methodology of tool lifecycle management and control based on RFID[C]//IEEE International Conference on Industrial Engineering & Engineering Management. IEEE,2009:1920-1924. [95] DENKENA B,KRÜGER M,SCHMIDT J. Condition-based tool management for small batch production[J]. The International Journal of Advanced Manufacturing Technology,2014,74(1-4):471-480. [96] TOMELERO R L,FERREIRA J C E,KUMAR V,et al. A lean environmental benchmarking (LEB) method for the management of cutting tools[J]. International Journal of Production Research,2017,55(13):3788-3807. [97] KASIE F M,BRIGHT G,HASHEMI-TABATABAEI M. Cutting tools assignment and control using neutrosophic case-based reasoning and best worst method[J]. Advances in Operations Research,2022:1-11. [98] SIMEONE A,ZENG Y,CAGGIANO A. Intelligent decision-making support system for manufacturing solution recommendation in a cloud framework[J]. The International Journal of Advanced Manufacturing Technology,2020,112(3-4):1035-1050. [99] DARYA B,MIKAEL H,BENGT O,et al. Digital Twin of a cutting tool[J]. Procedia CIRP,2018,72:215-218. [100] QI Q,TAO F,HU T,et al. Enabling technologies and tools for digital twin[J]. Journal of Manufacturing Systems,2021,58:3-21. |
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