Intelligent Machining Technology in Cutting Process

doi:10.3901/JME.2018.16.045

Abstract

Abstract: Metal cutting is a very complex process. In the cutting process, the related knowledge and theories of physics, chemistry, mechanics, materials science, vibration, tribology, heat transfer and other fields are involved. The cutting process control has been the focus of the cutting research. With the development of machining technology and the coming of the Industry 4.0, researchers are getting more concerned with the intelligent machining technology. It is an inevitable trend to apply the intelligent machining technology in the cutting process. The connotation and the application process of intelligent machining technology is expounded to investigate the critical technology in intelligent manufacturing. The research results in the simulation and optimization, cutting process condition monitoring, and optimization control are reviewed. Through analyzing the application prospect and problems of intelligent machining technology, the main scientific problems and key technologies to be solved are proposed. Intelligent machining is the development direction of processing technology. The application of intelligent machining technology in the cutting process will bring another technological revolution in the manufacturing industry.

Key words: artificial intelligence, cutting process, intelligent machining technology, on-line monitoring, optimize control, simulation and optimization

CLC Number:

TG506

LIU Xianli, LIU Qiang, YUE Caixu, WANG Lihui, LIANG S Y, JI Wei, GAO Hainig. Intelligent Machining Technology in Cutting Process[J]. Journal of Mechanical Engineering, 2018, 54(16): 45-61.

References

[1] KANG H S, JU Y L, CHOI S S, et al. Smart manufacturing:Past research, present findings, and future directions[J]. International Journal of Precision Engineering and Manufacturing-Green Technology,2016,3(1):111-128.
[2] LIANG S Y, HECKER R L, LANDERS R G. Machining process monitoring and control:The state-of-the-art[J]. Journal of Manufacturing Science & Engineering, 2004, 126(2):599-610.
[3] ZHANG Jingying, LIANG S Y, YAO Jun, et al. Evolutionary optimization of machining processes[J]. Journal of Intelligent Manufacturing, 2006, 17(2):203-215.
[4] DAVIS J, EDGAR T, PORTER J, et al. Smart manufacturing, manufacturing intelligence and demand-dynamic performance[J]. Computers & Chemical Engineering, 2012, 47(12):145-156.
[5] WANG Lihui,TÖRNGREN M,ONORI M. Current status and advancement of cyber-physical systems in manufacturing[J]. Journal of Manufacturing Systems, 2015, 37:517-527.
[6] WANG Lihui. An overview of function block enabled adaptive process planning for machining[J]. Journal of Manufacturing Systems, 2015, 35:10-25.
[7] WANG Lihui. Machine availability monitoring and machining process planning towards Cloud manufacturing[J]. Cirp Journal of Manufacturing Science & Technology, 2013, 6(4):263-273.
[8] WANG Zhao, JIAO Li, YAN Pei, et al. Research and development of intelligent cutting database cloud platform system[J]. International Journal of Advanced Manufacturing Technology, 2016:1-13.
[9] CAI Ligang, TIAN Yang, LIU Zhifeng, et al. Application of cloud computing to simulation of a heavy-duty machine tool[J]. International Journal of Advanced Manufacturing Technology, 2015, 84(1):291-303.
[10] WU Mingyang, HUO Tingyu, GE Jianghua. Cutting process-based optimization model of machining feature for cloud manufacturing[J]. International Journal of Advanced Manufacturing Technology, 2016, 84(1):327-334.
[11] WANG Lihui, WANG X V, GAO Liang, et al. A cloud-based approach for WEEE remanufacturing[J]. CIRP Annals-Manufacturing Technology, 2014, 63(1):409-412.
[12] WANG Lihui, KESHAVARZMANESH S, FENG H Y. A function block based approach for increasing adaptability of assembly planning and control[J]. International Journal of Production Research, 2011, 49(16):4903-4924.
[13] WANG Lihui. Planning towards enhanced adaptability in digital manufacturing[J]. International Journal of Computer Integrated Manufacturing, 2011, 24(5):378-390.
[14] TOTIS G, SORTINO M. Development of a modular dynamometer for triaxial cutting force measurement in turning[J]. International Journal of Machine Tools & Manufacture, 2011, 51(1):34-42.
[15] PANZERA T H, SOUZA P R, RUBIO J C C, et al. Development of a three-component dynamometer to measure turning force[J]. International Journal of Advanced Manufacturing Technology, 2012, 62(9-12):913-922.
[16] ZHAO You, ZHAO Yulong, LIANG Songbo, et al. A high performance sensor for triaxial cutting force measurement in turning[J]. Sensors, 2015, 15(4):7969-84.
[17] SHU Shengrong, CHENG Kai, DING Hui, et al. An innovative method to measure the cutting temperature in process by using an internally cooled smart cutting tool[J]. Journal of Manufacturing Science & Engineering, 2013, 135(6):1247-1254.
[18] BASTI A, OBIKAWA T, SHINOZUKA J. Tools with built-in thin film thermocouple sensors for monitoring cutting temperature[J]. International Journal of Machine Tools & Manufacture, 2007, 47(5):793-798.
[19] ATLURU S, HUANG S H, SNYDER J P. A smart machine supervisory system framework[J]. International Journal of Advanced Manufacturing Technology, 2012, 58(5):563-572.
[20] PARK H S, TRAN N H. Development of a smart machining system using self-optimizing control[J]. International Journal of Advanced Manufacturing Technology, 2014, 74(9):1365-1380.
[21] TETI R, JEMIELNIAK K, O'DONNELL G, et al. Advanced monitoring of machining operations[J]. CIRP Annals-Manufacturing Technology, 2010, 59(2):717-739.
[22] 王维,杨建国,姚晓栋,等. 数控机床几何误差与热误差综合建模及其实时补偿[J]. 机械工程学报, 2012, 48(7):165-170. WANG Wei, YANG Jianguo, YAO Xiaodong, et al. Synthesis modeling and real-time compensation of geometric error and thermal error for CNC machine tools[J]. Journal of Mechanical Engineering, 2012, 48(7):165-170.
[23] ZHU Weidong, WANG Zhigang, YAMAZAKI K. Machine tool component error extraction and error compensation by incorporating statistical analysis[J]. International Journal of Machine Tools & Manufacture, 2010, 50(9):798-806.
[24] TUNC L T, BUDAK E, BILGEN S, et al. Process simulation integrated tool axis selection for 5-axis tool path generation[J]. CIRP Annals-Manufacturing Technology, 2016, 64(1):381-384.
[25] CALAMAZ M, COUPARD D, GIROT F. A new material model for 2D numerical simulation of serrated chip formation when machining titanium alloy Ti-6Al-4V[J]. International Journal of Machine Tools & Manufacture, 2008, 48(3-4):275-288.
[26] ATTANASIO A, CERETTI E, RIZZUTI S, et al. 3D finite element analysis of tool wear in machining[J]. CIRP Annals-Manufacturing Technology, 2008, 57(1):61-64.
[27] ATTANASIO A, UMBRELLO D. Abrasive and diffusive tool wear FEM simulation[J]. International Journal of Material Forming, 2009, 2(1):543-546.
[28] FEITO N, LÓPEZ-PUENTE J, SANTIUSTE C, et al. Numerical prediction of delamination in CFRP drilling[J]. Composite Structures, 2014, 108(1):677-683.
[29] YUE Caixu, ZHONG Zhaonan, YU Mingming, et al. High-speed cutting finite element model parametric modeling based on ABAQUS[J]. Materials Science Forum, 2014, 800-801:353-357.
[30] 岳彩旭. 模具钢硬态切削过程刀具磨损及表面淬火效应研究[D]. 哈尔滨:哈尔滨理工大学, 2012. YUE Caicu. Tool wear and surface quenching mechanism for hard cutting process of die steel[D]. Harbin:Harbin University Of Science And Technology, 2012.
[31] 于明明. 车削过程的参数化建模及优化控制[D]. 哈尔滨:哈尔滨理工大学, 2013. YU Mingming. Parametric modeling on turning process and optimization control[D]. Harbin:Harbin University of Science And Technology, 2013.
[32] DIMLA E. DIMLA S. Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods[J]. International Journal of Machine Tools & Manufacture, 2000, 40(8):1073-1098.
[33] CHEN Xiaoqi, LI Huaizhong. Development of a tool wear observer model for online tool condition monitoring and control in machining nickel-based alloys[J]. International Journal of Advanced Manufacturing Technology, 2009, 45(7):786-800.
[34] SEEMUANG N, MCLEAY T, SLATTER T. Using spindle noise to monitor tool wear in a turning process[J]. International Journal of Advanced Manufacturing Technology, 2016, 86(9):2781-2790.
[35] FU Pan, HOPE A D. Intelligent classification of cutting tool wear states[C]//International Conference on Advances in Neural Networks. Springer-Verlag, 2006:964-969.
[36] WANG Guofeng, CUI Yinhu. On line tool wear monitoring based on auto associative neural network[J]. Journal of Intelligent Manufacturing, 2013, 24(6):1085-1094.
[37] SILVA M B D, WALLBANK J. Cutting temperature:prediction and measurement methods-a review[J]. Journal of Materials Processing Technology, 1999, 88(1-3):195-202.
[38] LONGBOTTOM J M, LANHAM J D. Cutting temperature measurement while machining-a review[J]. Aircraft Engineering & Aerospace Technology, 2013, 77(2):122-130.
[39] LI Linwen, LI Bin, EHMANN K F, et al. A thermo-mechanical model of dry orthogonal cutting and its experimental validation through embedded micro-scale thin film thermocouple arrays in PCBN tooling[J]. International Journal of Machine Tools & Manufacture, 2013, 70(4):70-87.
[40] AYDIN M, KARAKUZU C, UÇAR M, et al. Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning[J]. International Journal of Advanced Manufacturing Technology, 2013, 67(1):957-967.
[41] KOVAC P,RODIC D,PUCOVSKY V,et al. Multi-output fuzzy inference system for modeling cutting temperature and tool life in face milling[J]. Journal of Mechanical Science & Technology, 2014, 28(10):4247-4256.
[42] SINGH D, RAO P V. A surface roughness prediction model for hard turning process[J]. International Journal of Advanced Manufacturing Technology, 2007, 32(11):1115-1124.
[43] ILHAN ASILTÜRK, AKKUS H. Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method[J]. Measurement, 2011, 44(9):1697-1704.
[44] MIA M, DHAR N R. Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method[J]. International Journal of Advanced Manufacturing Technology, 2016. (on line)
[45] HECKER R L, LIANG S Y. Predictive modeling of surface roughness in grinding[J]. International Journal of Machine Tools & Manufacture, 2003, 43(8):755-761.
[46] GRZENDA M, BUSTILLO A, ZAWISTOWSKI P. A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling[J]. Journal of Intelligent Manufacturing, 2012, 23(5):1733-1743.
[47] HUANG P T B. An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations[J]. Journal of Intelligent Manufacturing, 2016, 27(3):689-700.
[48] KHORASANI A M, YAZDI M R S. Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation[J]. International Journal of Advanced Manufacturing Technology, 2015. (on line)
[49] CHEN Fan, LU Xiaodong, ALTINTAS Y. A novel magnetic actuator design for active damping of machining tools[J]. International Journal of Machine Tools & Manufacture, 2014, 85(7):58-69.
[50] CHEN Fan, HANIFZADEGAN M, ALTINTAS Y, et al. Active damping of boring bar vibration with a magnetic actuator[J]. IEEE/ASME Transactions on Mechatronics, 2015, 20(6):2783-2794.
[51] CHEN Fan, LIU Guangya. Active damping of machine tool vibrations and cutting force measurement with a magnetic actuator[J]. International Journal of Advanced Manufacturing Technology, 2016. (on line)
[52] LU Xiaodong, CHEN Fan, ALTINTAS Y. Magnetic actuator for active damping of boring bars[J]. CIRP Annals-Manufacturing Technology, 2014, 63(1):369-372.
[53] MEI Deqing, YAO Zhehe, KONG Tianrong, et al. Parameter optimization of time-varying stiffness method for chatter suppression based on magnetorheological fluid-controlled boring bar[J]. International Journal of Advanced Manufacturing Technology, 2009, 46(9):1071-1083.
[54] LIU Lijia, LIU Xianli, LIU Yuanhong. Non-uniform sampling finite-time control for networked control systems via event-driven transmission[J]. Advances in Mechanical Engineering, 2016. (on line)
[55] LIU Lijia, LIU Xianli, MAN Chuntao, et al. Delayed observer-based H ∞, control for networked control systems[J]. Neurocomputing, 2016, 179(C):101-109.
[56] 刘立佳. 变刚度-约束阻尼减振镗杆设计及特性研究[D]. 哈尔滨:哈尔滨理工大学, 2016. LIU Lijia. Design and characteristics research on damping boring bar with variable stiffness and constrained damping[D]. Harbin:Harbin University of Science And Technology, 2016.
[57] BRECHER C, MANOHARAN D, LADRA U, et al. Chatter suppression with an active workpiece holder[J]. Production Engineering, 2010, 4(2-3):239-245.
[58] 李茂月,韩振宇,富宏亚,等. 基于开放式控制器的铣削颤振在线抑制[J]. 机械工程学报, 2012, 48(17):172-182. LI Maoyue, HAN Zhenyu, FU Hongya, et al. Online milling chatter suppression based on open architecture controller[J]. Journal of Mechanical Engineering, 2012, 48(17):172-182.
[59] TSAI N C, CHEN D C, LEE R M. Chatter prevention for milling process by acoustic signal feedback[J]. International Journal of Advanced Manufacturing Technology, 2009, 47(9):1013-1021.
[60] TSAI N C, CHEN D C, LEE R M, et al. Chatter prevention and improved finish of workpiece for a milling process[J]. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 2010, 224(4):579-588.
[61] 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.
[62] 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.
[63] ZUPERL U, CUS F. System for off-line feedrate optimization and neural force control in end milling[J]. International Journal of Adaptive Control & Signal Processing, 2012, 26(2):105-123.
[64] ERKORKMAZ K, LAYEGH S E, LAZOGLU I, et al. Feedrate optimization for free form milling considering constraints from the feed drive system and process mechanics[J]. CIRP Annals-Manufacturing Technology, 2013, 62(1):395-398.
[65] YAO Xifan, ZHANG Yi, LI Bin, et al. Machining force control with intelligent compensation[J]. International Journal of Advanced Manufacturing Technology, 2013, 69(5):1701-1715.
[66] LIU Xianli, DING Yunpeng, YUE Caixu, et al. Off-line feedrate optimization with multiple constraints for corner milling of a cavity[J]. International Journal of Advanced Manufacturing Technology, 2015, 82(9):1899-1907.
[67] 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 Xianli, DING Yunpeng, YUE Caixu, et al. Feedrate optimization based on load control for corner-milling[J]. Journal of Mechanical Engineering, 2016, 52(19):189-196.