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Dynamic dam** of machining vibration: a review

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Abstract

Machining is one of the important manufacturing processes used in industry. Dynamic interaction between the tool and the workpiece may lead to the occurrence of chatter vibrations, which are associated with problems of poor surface finish, reduced workpiece quality, and low productivity. In the past, researchers developed some important methods to investigate the dynamics of machining processes. Dynamic responses of cutting systems were firstly identified by means of sensors, followed by chatter stability analysis using stability lobe diagrams to determine the stable and unstable regions, and finally, chatters were suppressed by either active or passive dam** techniques. Previous reviews emphasized on identification of the chatter vibration with less focus on control. This paper mainly reviews the state of the art on the control of machining chatter vibrations, including dam** methods related to boring, turning, and milling processes.

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References

  1. Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51:363–376

    Article  Google Scholar 

  2. Altintas Y (2012) Manufacturing automation, 2nd edn. Cambridge University Press, NY, USA

    Google Scholar 

  3. Akazawa K, Shamoto E (2008) Study on regenerative chatter vibration in ball end milling of flexible workpieces, Nagoya, Japan

  4. Tlusty J, Andrews GC (1983) A critical review of sensors for unmanned machining. CIRP Ann Manuf Technol 32:563–572

    Article  Google Scholar 

  5. Siddhpura M, Paurobally R (2012) A review of chatter vibration research in turning. Int J Mach Tools Manuf 61:27–47

    Article  Google Scholar 

  6. Khatri BC, Rathod P, Valaki JB (2015) Ultrasonic vibration-assisted electric discharge machining: a research review. Proc Inst Mech Eng B J Eng Manuf 230:319–330

    Article  Google Scholar 

  7. Ma L, Melkote SN, Castle JB (2013a) A model-based computationally efficient method for on-line detection of chatter in milling. J Manuf Sci Eng 135:1–11

    Google Scholar 

  8. Li S, Wang X, **e L, Pang S, Peng S, Liang Z, Jiao L (2015a) The milling-milling machining method and its realization. Int J Adv Manuf Technol 76:1151–1161

    Article  Google Scholar 

  9. Pop PA (2008) The study of cutting forces about dynamic stability of milling machine tools. In: Proceedings of the 9th biennial ASME conference on engineering systems design and analysis, Haifa, Israel, pp 617–626

  10. Okafor AC, Talekar VR, Irigireddy V, Gulati R (2010) Development of web-based virtual CNC milling machine tool with mechanistic cutting force models for education and learning. In: Proceedings of the ASME 2010 World Conference on Innovative Virtual Reality, Ames, Iowa, USA, pp 153–163

  11. Fekrmandi H, Unal M, Baghalian A, Tashakori S, Oyola K, Alsenawi A, Tansel IN (2016) A non-contact method for part-based process performance monitoring in end milling operations. Int J Adv Manuf Technol 83:13–20

    Article  Google Scholar 

  12. Toh CK (2004) Vibration analysis in high speed rough and finish milling hardened steel. J Sound Vib 278:101–115

    Article  Google Scholar 

  13. Kaldestad KB, Tyapin I Hovland G (2015) Robotic face milling path correction and vibration reduction, Korea, Busan

  14. Sinou JJ, Jezequel L (2007) Mode coupling instability in friction-induced vibrations and its dependency on system parameters including dam**. Eur J Mech A Solids 26:106–122

    Article  MATH  Google Scholar 

  15. Ast A, Braun S, Eberhard P, Heisel U (2007) Adaptronic vibration dam** for machine tools. CIRP Ann Manuf Technol 56:379–382

    Article  Google Scholar 

  16. van Dijk NJM, van de Wouw N, Doppenberg EJJ, Oosterling HAJ, Nijmeijer H (2012) Robust active chatter control in the high-speed milling process. IEEE Trans Control Syst Technol 20:901–917

  17. Karkoub M (2011) Robust control of the elastodynamic vibrations of a flexible rotor system with discontinuous friction. Journal of Vibration and Acoustics 501(1–9):133:034

    Google Scholar 

  18. Tsai NC, Shih LW, Lee RM (2010a) Spindle vibration suppression for advanced milling process by using self-tuning feedback control. Int J Adv Manuf Technol 48:1–10

    Article  Google Scholar 

  19. Gȯrges D, Kroneis J, Liu S (2008) Active vibration control of storage and retrieval machines. In: ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Brooklyn, New York, USA, pp 1037–1046

  20. Morimoto Y, Suzuki N, Kaneko Y, Isobe M (2014) Vibration control of relative tool-spindle displacement for computer numerically controlled lathe with pipe frame structure. J Manuf Sci Eng 502(1–4):136:044

    Google Scholar 

  21. Radecki PP, Farinholt KM, Park G, Bement MT (2010) Vibration suppression in cutting tools using a collocated piezoelectric sensor/actuator with an adaptive control algorithm. J Vib Acoust 132 002(1–8):051

    Google Scholar 

  22. Inoue T, Ishida Y, Niimi H (2012) Vibration analysis of a self-excited vibration in a rotor system caused by a ball balancer. J Vib Acoust 006(1–11):134:021

    Google Scholar 

  23. van Dijk NJM, van de Wouw N, Nijmeijer H (2015) Fixed-structure robust controller design for chatter mitigation in high-speed milling. Int J Robust Nonlinear Control 25:3495–3514

  24. Chen M, Knospe CR (2007) Control approaches to the suppression of machining chatter using active magnetic bearings. IEEE Trans Control Syst Technol 15:220–232

    Article  Google Scholar 

  25. Dohner JL, Lauffer JP, Hinnerichs TD, Shankar N, Regelbrugge M, Kwan CM, Xu R, Winterbauer B, Bridger K (2004) Mitigation of chatter instabilities in milling by active structural control. J Sound Vib 269:197–211

    Article  Google Scholar 

  26. Munoa J, Beudaert X, Erkorkmaz K, Iglesias A, Barrios A, Zatarain M (2015) Active suppression of structural chatter vibrations using machine drives and accelerometers. CIRP Ann Manuf Technol 64:385–388

    Article  Google Scholar 

  27. Lu X, Chen F, Altintas Y (2014) Magnetic actuator for active dam** of boring bars. CIRP Ann Manuf Technol 63:369–372

    Article  Google Scholar 

  28. Chen F, Lu X, Altintas Y (2014) A novel magnetic actuator design for active dam** of machining tools. International Journal of Machine Tools Manufacture 85:58–69

    Article  Google Scholar 

  29. Chen F, Hanifzadegan M, Altintas Y, Lu X (2015) Active dam** of boring bar vibration with a magnetic actuator. IEEE/ASME Transactions on Mechatronics 20(6):2783–2794

    Article  Google Scholar 

  30. Chen F (2014) Active dam** of machine tools with magnetic actuators. PhD thesis, University Of British Columbia

    Google Scholar 

  31. Khorasani AM, Jalali Aghchai A, Khorram A (2011) Chatter prediction in turning process of conical workpieces by using case-based resoning (CBR) method and Taguchi design of experiment. Int J Adv Manuf Technol 55:457–464

    Article  Google Scholar 

  32. Koohestani A, Mo JPT (2012) The application of image correlation in order to define chatter during milling of titanium. In: Proceedings of the ASME 2012 international mechanical engineering congress and exposition, Houston, Texas,USA, pp 1955–1958

  33. Tangjitsitcharoen S, Pongsathornwiwat N (2013) Development of chatter detection in milling processes. Int J Adv Manuf Technol 65:919–927

    Article  Google Scholar 

  34. Tangjitsitcharoen S, Saksri T, Ratanakuakangwan S (2015) Advance in chatter detection in ball end milling process by utilizing wavelet transform. J Intell Manuf 26:485–499

    Article  Google Scholar 

  35. Abele E, Sielaff T, Schiffler A (2012) Method for chatter detection with standard plc systems. Prod Eng 6:611–619

    Article  Google Scholar 

  36. Nair U, Krishna BM, Namboothiri VNN, Nampoori VPN (2010) Permutation entropy based real-time chatter detection using audio signal in turning process. Int J Adv Manuf Technol 46:61–68

    Article  Google Scholar 

  37. Kim DH, Song JY, Cha SK, Son H (2011) The development of embedded device to detect chatter vibration in machine tools and CNC-based autonomous compensation. J Mech Sci Technol 25:2623–2630

    Article  Google Scholar 

  38. Lamraoui M, Barakat M, Thomas M, El Badaoui M (2015) Chatter detection in milling machines by neural network classification and feature selection. J Vib Control 21:1251–1266

    Article  Google Scholar 

  39. Tarng YS, Chen MC (1994) An intelligent sensor for detection of milling chatter. J Intell Manuf 5:193–200

    Article  Google Scholar 

  40. Tansel IN, Li M, Demetgul M, Bickraj K, Kaya B, Ozcelik B (1994) Detecting chatter and estimating wear from the torque of end milling signals by using index based reasoner (IBR). Int J Adv Manuf Technol 58:109–118

    Article  Google Scholar 

  41. Li HZ, **g XB, Wang J (2014) Detection and analysis of chatter occurrence in micro-milling process. J Eng Manuf 228:1359–1371

    Article  Google Scholar 

  42. Wang M, Fei R (1999) Chatter suppression based on nonlinear vibration characteristic of electrorheological fluids. Int J Mach Tools Manuf 39:1925–1934

    Article  Google Scholar 

  43. Mei D, Kong T, Shih AJ, Chen Z (2009) Magnetorheological fluid-controlled boring bar for chatter suppression. J Mater Process Technol 209:1861–1870

    Article  Google Scholar 

  44. Wang M, Fei R (2001) On-line chatter detection and control in boring bar based on an electrorheological fluid. Mechatronics 11:779–792

    Article  Google Scholar 

  45. Mackerle J (1999) Finite-element analysis and simulation of machining: a bibliography (1976-1996). J Mater Process Technol 86:17–44

    Article  Google Scholar 

  46. Yan X, Huang S (2011) Research of four roll strip mill axial force based on the finite element simulation analysis. In: International Conference on Consumer Electronics, Communications and Networks (CECNet), **anNing, China, pp 564–568

  47. Baker JR, Rouch KE (2002) Use of finite element structural models in analyzing machine tool chatter. Finite Elem Anal Des 38:1029–1046

    Article  MATH  Google Scholar 

  48. Grossi N, Montevecchim F, Scippa A, Campatelli G (2015) 3D finite element modeling of holder-tool assembly for stability prediction in milling. 15th CIRP Conference on Modelling of Machining Operations (15th CMMO) 31:527–532

    Google Scholar 

  49. Cakir MC, Isik Y (2005) Finite element analysis of cutting tools prior to fracture in hard turning operations. Mater Des 26:105–112

    Article  Google Scholar 

  50. Weiwei W (2009) Finite element analysis of dynamic characteristic for the XK717 CNC milling machine. In: International conference on measuring technology and mechatronics automation, ICMTMA, Zhangjiajie, Hunan, China, pp 803–805

  51. Mahdavinejad R (2005) Finite element analysis of machine and workpiece instability in turning. Int J Mach Tools Manuf 45:753–760

    Article  Google Scholar 

  52. Dirikolu MH, Childs THC, Maekawa K (2001) Finite element simulation of chip flow in metal machining. Int J Mech Sci 43:2699–2713

    Article  MATH  Google Scholar 

  53. Liu H, Sun Y, Liang Y, Lu Z (2010) Three dimensional finite element simulation and analysis of residual stress in milling. In: 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, vol 551A, Harbin, China, pp 1–5

  54. Sun YZ, Liu HT, Liang YC, Zhang Z (2010) Finite element simulation of material’s stress and strain in micro-milling. In: 4th International Seminar on Modern Cutting and Measurement Engineering, vol 973S, Bei**g, China, pp 1–6

  55. Song Q, Ju G, Liu Z, Ai X (2014) Subdivision of chatter-free regions and optimal cutting parameters based on vibration frequencies for peripheral milling process. Int J Mech Sci 83:172–183

    Article  Google Scholar 

  56. Insperger T, Gradisek J, Kalveram M, Stepan G, Winert K, Govekar E (2006) Machine tool chatter and surface location error in milling processes. J Manuf Sci Eng 128:913–920

    Article  Google Scholar 

  57. Insperger T, Stepan G (2004) Updated semi-discretization method for periodic delay-differential equations with discrete delay. Int J Numer Methods Eng 61:117–141

    Article  MathSciNet  MATH  Google Scholar 

  58. Galdino dos Santos R, Teixeira Coelho R (2014) A contribution to improve the accuracy of chatter prediction in machine tools using the stability lobe diagram. J Manuf Sci Eng 136 005(1–7):021

    Google Scholar 

  59. Solis E, Peres CR, Jimenez JE, Alique JR, Monje JC (2004) A new analytical-experimental method for the identification of stability lobes in high-speed milling. Int J Mach Tools Manuf 44:1591–1597

    Article  Google Scholar 

  60. Gagnol V, Bouzgarrou BC, Ray P, Barra C (2007) Stability-based spindle design optimization. J Manuf Sci Eng 129:407–415

    Article  Google Scholar 

  61. Eynian M (2014). In: 6th CIRP international conference on high performance cutting, HPC2014 Frequency domain study of vibrations above and under stability lobes in machining systems, Trollhattan, Sweden, pp 164–169

  62. Altintas Y, Budak E (1995) Analytical prediction of stability lobes in milling. CIRP Ann Manuf Technol 44:357–362

    Article  Google Scholar 

  63. Altintas Y, Shamoto E, Lee P, Budak E (1999) Analytical prediction of stability lobes in ball end milling. J Manuf Sci Eng 121:586–592

    Article  Google Scholar 

  64. Ismail F, Soliman E (1997) A new method for the identification of stability lobes in machining. Int J Mach Tools Manuf 37:763–774

    Article  Google Scholar 

  65. Quintana G, Ciurana J, Teixidor D (2008) A new experimental methodology for identification of stability lobes diagram in milling operations. Int J Mach Tools Manuf 48:1637– 1645

    Article  Google Scholar 

  66. Tsai NC, Chen DC, Lee RM (2010b) Chatter prevention for milling process by acoustic signal feedback. Int J Adv Manuf Technol 47:1013–1021

    Article  Google Scholar 

  67. Abele E, Fiedler U (2004) Creating stability lobe diagrams during milling. CIRP Ann Manuf Technol 53:309–312

    Article  Google Scholar 

  68. Zheng CM, Junz Wang JJ, Sung CF (2013) Analytical prediction of the critical depth of cut and worst spindle speeds for chatter in end milling. J Manuf Sci Eng 136 003(01–10):011

    Google Scholar 

  69. Elias A, Rodriguez CA, Delgadillo E, Martinez A, Araya F, Flores V (2005) Chatter prediction in orthogonal cutting based on Lambert function. In: ASME 2005 International Mechanical Engineering Congress and Exposition, Orlando, Florida USA, pp 1143–1147

  70. Feng J, Sun Z, Jiang Z, Yang L (2016) Identification of chatter in milling of Ti-6Al-4V titanium alloy thin-walled workpieces based on cutting force signals and surface topography. Int J Adv Manuf Technol 82:1909–1920

    Article  Google Scholar 

  71. Wan M, Zhang WH, Dang JW, Yang Y (2010) A unified stability prediction method for milling process with multiple delays. Int J Mach Tools Manuf 50:29–41

    Article  Google Scholar 

  72. Wan M, Ma YC, Zhang WH, Yang Y (2015) Study on the construction mechanism of stablility lobes in milling process with multiple modes. Int J Adv Manuf Technol 79:589–603

    Article  Google Scholar 

  73. Gasparetto A (1998) A system theory approach to mode coupling chatter in machining. J Dyn Syst Meas Contr 120:545–547

    Article  Google Scholar 

  74. Zhang HT, Wu Y, He D, Zhao H (2015) Model predictive control to mitigate chatters in milling processes with input constraints. Int J Mach Tools Manuf 91:54–61

    Article  Google Scholar 

  75. Mahnama M, Movahhedy MR (2010) Prediction of machining chatter based on FEM simulation of chip formation under dynamic conditions. Int J Mach Tools Manuf 50:611–620

    Article  Google Scholar 

  76. Fischer A, Eberhard P (2014) Controlling vibrations of a cutting process using predictive control. Comput Mech 54:21–31

    Article  MathSciNet  MATH  Google Scholar 

  77. Wang M, Gao L, Zheng Y (2014) Prediction of regenerative chatter in the high-speed vertical milling of thin-walled workpiece made of titanium alloy. Int J Adv Manuf Technol 72:707–716

    Article  Google Scholar 

  78. Sims ND (2015) Fast chatter stability prediction for variable helix milling tools. J Mech Eng Sci 230:133–144

    Article  Google Scholar 

  79. Palanisamy P, Rajendran I, Shanmugasundaram S, Saravanan R (2006) Prediction of cutting force and temperature rise in the end-milling operation. Proc Inst Mech Eng B J Eng Manuf 220:1577–1587

    Article  Google Scholar 

  80. Bissey-Breton S, Poulachon G, Lapujoulade F (2006) Integration of tool geometry in prediction of cutting forces during milling of hard materials. Proc Inst Mech Eng B J Eng Manuf 220:579–587

    Article  Google Scholar 

  81. Li B, Cao Y, Ye X, Guan J, Yang J (2015b) Multi-scale prediction of the geometrical deviations of the surface finished by five-axis ball-end milling. Proc Inst Mech Eng B J Eng Manuf 220:1–18

    Google Scholar 

  82. Ramesh K, Alwarsamy T, Jayabal S (2015) Prediction of cutting process parameters in boring operations using artificial neural networks. J Vib Control 21:1043–1054

    Article  Google Scholar 

  83. Moradi H, Movahhedy MR, Vossoughi G (2012) Tunable vibration absorber for improving milling stability with tool wear and process dam** effects. Mech Mach Theory 52:59–77

    Article  Google Scholar 

  84. Rubio L, Ja Loya, Miguelez MH, Fernandez-Saez J (2013) Optimization of passive vibration absorbers to reduce chatter in boring. Mech Syst Sig Process 41:691–704

    Article  Google Scholar 

  85. Huang B, Chen JC (2003) An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Adv Manuf Technol 21:339–347

    Article  Google Scholar 

  86. Susanto V, Chen JC (2003) Fuzzy logic based in-process tool-wear monitoring system in face milling operations. Int J Adv Manuf Technol 21:186–192

    Google Scholar 

  87. Chen JC, Chen JC (2005) An artificial neural networks based in process tool wear prediction system in milling operations. Int J Adv Manuf Technol 25:427–434

    Article  Google Scholar 

  88. Faassen RPH, Van de Wouw N, Oosterling JAJ, Nijmeijer H (2003) Prediction of regenerative chatter by modelling and analysis of high-speed milling. Int J Mach Tools Manuf 43:1437–1446

  89. Tatar K, Gren P (2016) Estimation of the in-plane vibrations of a rotating spindle, using out-of-plane laser vibrometry measurements. Mech Syst Sig Process 72-73:660–666

    Article  Google Scholar 

  90. Galvan-Tejada CE, Galvan-Tejada I, Sandoval EI, Brena R (2012) Wifi bluetooth based combined positioning algorithm. Procedia Engineering 35:101–108

    Article  Google Scholar 

  91. Rubio L, de la Sen M (2007) Adaptive control of milling forces under fractional order holds. In: Innovative Algorithms and Techniques in Automation, Industrial Electronics and Telecommunications, Apartado, Spain, pp 257–261

  92. Kubica EG, Ismail F (1996) Active suppression of chatter in peripheral milling. part II. application of fuzzy control. Int J Adv Manuf Technol 12:236–245

    Article  Google Scholar 

  93. Xu C, Shin YC (2008) An adaptive fuzzy controller for constant cutting force in end-milling processes. J Manuf Sci Eng 130 001(1–10):031

    Google Scholar 

  94. Brecher C, Manoharan D, Ladra U, Kopken HG (2010) Chatter suppression with an active workpiece holder. Prod Eng 4:239–245

    Article  Google Scholar 

  95. Abele E, Hanselka H, Haase F, Schlote D, Schiffler A (2008) Development and design of an active work piece holder driven by piezo actuators. Prod Eng 2:437–442

    Article  Google Scholar 

  96. Tansel I, Nedbouyan A, Trujillo M (1995) Design of a smart workpiece holder (swh)to extend the useful life of micro-tools. In: IECON, USA, pp 116–120

  97. Cao HR, Zhang XW, Chen XF (2017) The concept and progress of intelligent spindles: a review. Int J Mach Tools Manuf 112:21–52

    Article  Google Scholar 

  98. Saadabad NA, Moradi H, Vossoughi G (2014). Global optimization and design of dynamic absorbers for chatter suppression in milling process with tool wear and process dam**. 24th CIRP Design Conference 21:360–366

    Google Scholar 

  99. Martins da Silva M, Venter GS, Varoto PS, Coelho RT (2015) Experimental results on chatter reduction in turning through embedded piezoelectric material and passive shunt circuits. Mechatronics 29:78–85

    Article  Google Scholar 

  100. Wan M, Ma YC, Feng J, Zhang WH (2016) Study of static and dynamic ploughing mechanisms by establishing generalized model with static milling forces. Int J Mech Sci 114:120– 131

    Article  Google Scholar 

  101. Anderson CS, Semercigil SE, Turan OF (2007) A passive adaptor to enhance chatter stability for end mills. Int J Mach Tools Manuf 47:1777–1785

    Article  Google Scholar 

  102. Moradi H, Vossoughi G, Behzad M, Movahhedy MR (2015) Vibration absorber design to suppress regenerative chatter in nonlinear milling process: application for machining of cantilever plates. Appl Math Model 39:600–620

    Article  Google Scholar 

  103. Sun Y, Xu J (2015) Experiments and analysis for a controlled mechanical absorber considering delay effect. J Sound Vib 339:25–37

    Article  Google Scholar 

  104. Sims ND (2007) Vibration absorbers for chatter suppression: a new analytical tuning methodology. J Sound Vib 301:592–607

    Article  Google Scholar 

  105. Nakano Y, Takahara H, Yasue K, Asaga R (2012) The effect of multiple dynamic absorbers on regenerative chatter and resonance in end milling process. In: Proceeding ASME 45288, Houston, Texas, USA, pp 1–7

  106. Bavastri CA, Polli ML, Voltolini DR, Presezniak FA (2015) A methodology to mitigate chatter through optimal viscoelastic absorber. J Eng Manuf 229:1348–1356

    Article  Google Scholar 

  107. Asfar KR, Akour SN (2004) Optimization analysis of impact viscous damper for controlling self-excited vibrations. J Vib Control 11:103–120

    Article  MATH  Google Scholar 

  108. Stone BJ, Andrew C (1969) Optimization of vibration absorbers: application to complex structures. J Mech Eng Sci 11:221–233

    Article  Google Scholar 

  109. Hagood NW, Flotow AV (1991) Dam** of structural vibrations with piezoelectric materials and passive electrical networks. J Sound Vib 146:243–268

    Article  Google Scholar 

  110. Shevtsov S, Soloviev A, Acopyan V, Samochenko I (2009) Helicopter rotor blade vibration control on the basis of active/passive piezoelectric dam** approach. In: 4Th international conference on physics and control (PHYSCON), Catania, Italy, pp 1–9

  111. Yigit U, Cigeroglu E, Budak E (2013) Chatter reduction in turning by using piezoelectric shunt circuits. In: Proceedings of the 31st IMAC, A Conference on Structural Dynamics, California, USA, pp 415–420

  112. Chen WM, Liu TS (2013) Modeling and experimental validation of new two degree-of-freedom piezoelectric actuators. Mechatronics 23:1163–1170

    Article  Google Scholar 

  113. Giorgio Bort CM, Leonesio M, Bosetti P (2016) A model-based adaptive controller for chatter mitigation and productivity enhancement in CNC milling machines. Robot Comput Integr Manuf 40:34–43

    Article  Google Scholar 

  114. Denkena B, Floter F (2012) Adaptive cutting force control on a milling machine with hybrid axis configuration. In: 3Rd CIRP conference on process machine interactions (3rd PMI), vol 4, pp 109–114

  115. Ma Y, Zhang X, Xu M, **e S (2013b) Hybrid model based on preisach and support vector machine for novel dual-stack piezoelectric actuator. Mech Syst Sig Process 34:156–172

    Article  Google Scholar 

  116. Zuperl U, Cus F, Reibenschuh M (2012) Modeling and adaptive force control of milling by using artificial techniques. J Intell Manuf 23:1805–1815

    Article  Google Scholar 

  117. Chen Z, Zhang HT, Zhang X, Ding H (2013) Adaptive active chatter control in milling processes. J Dyn Syst Meas Contr 007(1–7):136:021

    Google Scholar 

  118. Bosetti P, Leonesio M, Parenti P (2013) On development of an optimal control system for real-time process optimization on milling machine tools. In: 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Italy, pp 31–36

  119. Monnin J, Kuster F, Wegener K (2014a) Optimal control for chatter mitigation in milling-part 1: modeling and control design. Control Eng Pract 24:156–166

    Article  Google Scholar 

  120. Monnin J, Kuster F, Wegener K (2014b) Optimal control for chatter mitigation in milling-part 2: experimental validation. Control Eng Pract 24:167–175

    Article  Google Scholar 

  121. Mei D, Yao Z, Kong T, Chen Z (2010) Parameter optimization of time-varying stiffness method for chatter suppression based on magnetorheological fluid-controlled boring bar. Int J Adv Manuf Technol 46:1071–1083

    Article  Google Scholar 

  122. Moradi H, Vossoughi G, Movahhedy MR, Salarieh H (2013) Suppression of nonlinear regenerative chatter in milling process via robust optimal control. J Process Control 23:631–648

    Article  Google Scholar 

  123. Kalinski KJ, Galewski MA (2011) Chatter vibration surveillance by the optimal-linear spindle speed control. Mech Syst Sig Process 25:383–399

    Article  Google Scholar 

  124. Saravanamurugan S, Alwarsamy T, Devarajan K (2015) Optimization of damped dynamic vibration absorber to control chatter in metal cutting process. J Vib Control 21:949–958

    Article  Google Scholar 

  125. Chodnicki M, Kalinski KJ, Galewski MA (2015) Vibration surveillance during milling flexible details with the use of active optimal control. Journal of Low Frequency Noise Vibration and Active Control 32:145–156

    Article  Google Scholar 

  126. Kopac J, Pogacnik M (2000) Dynamic stabilization of the turn-milling process by parameter optimization. Proc Inst Mech Eng B J Eng Manuf 214:127–135

    Google Scholar 

  127. Chen F, Tzeng Y (2004) Optimization of the volumetric accuracy of high-speed computer numerical control milling with dynamic quality characteristics. J Eng Manuf 218:1741–1754

    Article  Google Scholar 

  128. Pour DS, Behbahani S (2015) Semi-active fuzzy control of machine tool chatter vibration using smart MR dampers. Int J Adv Manuf Technol 83:421–428

    Article  Google Scholar 

  129. Spencer Jr BF, Dyke SJ, Sain MK, Carlson JD (1997) Phenomenological model for magnetorheological dampers. J Eng Mech 123:230–238

  130. Kim D, Jeon D (2011) Fuzzy-logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors. Precis Eng 35:143–152

    Article  Google Scholar 

  131. Tsai NC, Shih LW, Lee RM (2010c) Counterbalance of cutting force for advanced milling operations. Mech Syst Sig Process 24:1191–1208

    Article  Google Scholar 

  132. Haber-Guerra R, Liang SY, Alique JR, Haber-Haber R (2006) Fuzzy control of spindle torque in high-speed milling processes. J Manuf Sci Eng 128:1014–1018

    Article  Google Scholar 

  133. Xu C, Shin YC (2006) Control of cutting force for creep-feed grinding processes using a multi-level fuzzy controller. J Dyn Syst Meas Contr 129:480–492

    Article  Google Scholar 

  134. Chen JC (1996) A fuzzy-nets tool-breakage detection system for end-milling operations. Int J Adv Manuf Technol 12:153–164

    Article  Google Scholar 

  135. Zhao P, Shi Y, Huang J (2016) Proportional-integral based fuzzy sliding mode control of the milling head. Control Eng Pract 53:1–13

    Article  Google Scholar 

  136. Mesina OS, Langari R (2001) A neuro-fuzzy system for tool condition monitoring in metal cutting. J Manuf Sci Eng 123:312– 318

    Article  Google Scholar 

  137. Tandon V, El-Mounayri H (2001) A novel artificial neural networks force model for end milling. Int J Adv Manuf Technol 18:693–700

    Article  Google Scholar 

  138. Zuperl U, Cus F, Reibenschuh M (2011) Neural control strategy of constant cutting force system in end milling. Rob Comput Integr Manuf 27:485–493

    Article  Google Scholar 

  139. Lee S (2000) Specific cutting force coefficients modeling of end milling. KSE International Journal 14:622–632

    Article  Google Scholar 

  140. Arnaiz-Gonzaez A, Fernandez-Valdivielso A, Bustillo A, Lopez de Lacalle LN (2016) Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling. Int J Adv Manuf Technol 83:847–859

    Article  Google Scholar 

  141. Chen Y, Qiu J, Ji H, Zhu K (2010) Tracking control of piezoelectric actuator system using inverse hysteresis model. Int J Appl Electromagn Mech 33:1555–1564

    Google Scholar 

  142. Reddy R, Saha P (2016) Kautz filters based model predictive control for resonating systems. International Journal of Dynamics and Control 33:1–19

    Google Scholar 

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Correspondence to Min Wan.

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Muhammad, B.B., Wan, M., Feng, J. et al. Dynamic dam** of machining vibration: a review. Int J Adv Manuf Technol 89, 2935–2952 (2017). https://doi.org/10.1007/s00170-016-9862-z

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