The concept and progress of intelligent spindles: A review

International Journal of Machine Tools and Manufacture

Volumn 112, Issue , 2017, Pages 21-52

  Cao, Hongrui ^a   Zhang, Xingwu ^a   Chen, Xuefeng ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Condition monitoring; Control; Intelligent spindles; Machine tools; Machining process

Indexed keywords

CONTROL ENGINEERING; MACHINE COMPONENTS; MACHINE TOOLS; MACHINING;

CORE COMPONENTS; CURRENT LIMITATION; ENABLING TECHNOLOGIES; INTELLIGENT FUNCTIONS; INTELLIGENT SPINDLES; MACHINING PROCESS; MONITORING AND CONTROL; STATE OF THE ART;

CONDITION MONITORING;

EID: 84994036942     PISSN: 08906955     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijmachtools.2016.10.005     Document Type: Review

References (358)

1. [1] Lee, J., Kao, H.-A., Yang, S., Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP 16 (2014), 3–8.
2. [2] Atluru, S., Huang, S.H., Snyder, J.P., A smart machine supervisory system framework. Int. J. Adv. Manuf. Technol. 58 (2012), 563–572.
3. [3] Mekid, S., Pruschek, P., Hernandez, J., Beyond intelligent manufacturing: a new generation of flexible intelligent NC machines. Mech. Mach. Theory 44 (2009), 466–476.
4. [4] Abele, E., Altintas, Y., Brecher, C., Machine tool spindle units. CIRP Ann. - Manuf. Technol. 59 (2010), 781–802.
5. [5] Nakamura, S., Technology development and future challenge of machine tool spindle. J. SME 1 (2012), 1–7.
6. [6] Rehorn, A.G., Jiang, J., Orban, P.E., State-of-the-art methods and results in tool condition monitoring: a review. Int. J. Adv. Manuf. Technol. 26 (2005), 693–710.
7. [7] Prickett, P., Johns, C., An overview of approaches to end milling tool monitoring. Int. J. Mach. Tools Manuf. 39 (1999), 105–122.
8. [8] Li, X., A brief review: acoustic emission method for tool wear monitoring during turning. Int. J. Mach. Tools Manuf. 42 (2002), 157–165.
9. [9] Teti, R., Jemielniak, K., O'Donnell, G., Dornfeld, D., Advanced monitoring of machining operations. CIRP Ann. Manuf. Technol. 59 (2010), 717–739.
10. [10] Kurada, S., Bradley, C., A review of machine vision sensors for tool condition monitoring. Comput. Ind. 34 (1997), 55–72.
11. [11] Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., König, W., Teti, R., Tool condition monitoring (TCM) — the status of research and industrial application. CIRP Ann. Manuf. Technol. 44 (1995), 541–567.
12. [12] Karabay, S., Design criteria for electro-mechanical transducers and arrangement for measurement of strains due to metal cutting forces acting on dynamometers. Mater. Des. 28 (2007), 496–506.
13. [13] Yaldız, S., Ünsaçar, F., Sağlam, H., Işık, H., Design, development and testing of a four-component milling dynamometer for the measurement of cutting force and torque. Mech. Syst. Signal Process. 21 (2007), 1499–1511.
14. [14] Rizal, M., Ghani, J.A., Nuawi, M.Z., Haron, C.H.C., Development and testing of an integrated rotating dynamometer on tool holder for milling process. Mech. Syst. Signal Process. 52 (2015), 559–576.
15. [15] Albrecht, A., Park, S.S., Altintas, Y., Pritschow, G., High frequency bandwidth cutting force measurement in milling using capacitance displacement sensors. Int. J. Mach. Tools Manuf. 45 (2005), 993–1008.
16. [16] Spiewak, S.A., Acceleration based indirect force measurement in metal cutting processes. Int. J. Mach. Tools Manuf. 35 (1995), 1–17.
17. [17] Auchet, S., Chevrier, P., Lacour, M., Lipinski, P., A new method of cutting force measurement based on command voltages of active electro-magnetic bearings. Int. J. Mach. Tools Manuf. 44 (2004), 1441–1449.
18. [18] Klocke, F., Reuber, M., Sicheres freiformfräsen mit on-line-prozessü-berwachung, wt. Werkstattstech. Online 90 (2000), 119–122.
19. [19] Jun, M.B., Ozdoganlar, O.B., DeVor, R.E., Kapoor, S.G., Kirchheim, A., Schaffner, G., Evaluation of a spindle-based force sensor for monitoring and fault diagnosis of machining operations. Int. J. Mach. Tools Manuf. 42 (2002), 741–751.
20. [20] Park, S.S., Altintas, Y., Dynamic compensation of spindle integrated force sensors with kalman filter. J. Dyn. Syst. Meas. Control Trans. ASME 126 (2004), 443–452.
21. [21] Kuljanic, E., Sortino, M., TWEM, a method based on cutting forces—monitoring tool wear in face milling. Int. J. Mach. Tools Manuf. 45 (2005), 29–34.
22. [22] Kuljanic, E., Sortino, M., Totis, G., Multisensor approaches for chatter detection in milling. J. Sound Vib. 312 (2008), 672–693.
23. [23] Kaya, B., Oysu, C., Ertunc, H.M., Ocak, H., A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 226 (2012), 1808–1818.
24. [24] Milner, J.L., Roth, J.T., Condition monitoring for indexable carbide end mill using acceleration data. Mach. Sci. Technol. 14 (2010), 63–80.
25. [25] Chen, S.-L., Jen, Y., Data fusion neural network for tool condition monitoring in CNC milling machining. Int. J. Mach. Tools Manuf. 40 (2000), 381–400.
26. [26] Yesilyurt, I., Ozturk, H., Tool condition monitoring in milling using vibration analysis. Int. J. Prod. Res. 45 (2007), 1013–1028.
27. [27] Wang, G., Yang, Y., Zhang, Y., Xie, Q., Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection. Sens. Actuators A: Phys. 209 (2014), 24–32.
28. [28] Dutta, R., Paul, S., Chattopadhyay, A., Fuzzy controlled backpropagation neural network for tool condition monitoring in face milling. Int. J. Prod. Res. 38 (2000), 2989–3010.
29. [29] Lamraoui, M., Thomas, M., El Badaoui, M., Cyclostationarity approach for monitoring chatter and tool wear in high speed milling. Mech. Syst. Signal Process. 44 (2014), 177–198.
30. [30] Sevilla-Camacho, P., Robles-Ocampo, J., Muñiz-Soria, J., Lee-Orantes, F., Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering. Int. J. Adv. Manuf. Technol. 81 (2015), 1187–1194.
31. [31] Hsieh, W.-H., Lu, M.-C., Chiou, S.-J., Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. Int. J. Adv. Manuf. Technol. 61 (2012), 53–61.
32. [32] Jun, C.-H., Suh, S.-H., Statistical tool breakage detection schemes based on vibration signals in NC milling. Int. J. Mach. Tools Manuf. 39 (1999), 1733–1746.
33. [33] Yesilyurt, I., End mill breakage detection using mean frequency analysis of scalogram. Int. J. Mach. Tools Manuf. 46 (2006), 450–458.
34. [34] Hsueh, Y.-W., Yang, C.-Y., Tool breakage diagnosis in face milling by support vector machine. J. Mater. Process. Technol. 209 (2009), 145–152.
35. [35] Binsaeid, S., Asfour, S., Cho, S., Onar, A., Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion. J. Mater. Process. Technol. 209 (2009), 4728–4738.
36. [36] Cho, S., Binsaeid, S., Asfour, S., Design of multisensor fusion-based tool condition monitoring system in end milling. Int. J. Adv. Manuf. Technol. 46 (2010), 681–694.
37. [37] Lauro, C., Brandão, L., Baldo, D., Reis, R., Davim, J., Monitoring and processing signal applied in machining processes – a review. Measurement 58 (2014), 73–86.
38. [38] Ghosh, N., Ravi, Y., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A., Chattopadhyay, A., Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech. Syst. Signal Process. 21 (2007), 466–479.
39. [39] Zhang, D., An adaptive procedure for tool life prediction in face milling. Proc. Inst. Mech. Eng. Part J: J. Eng. Tribol. 225 (2011), 1130–1136.
40. [40] Stavropoulos, P., Papacharalampopoulos, A., Vasiliadis, E., Chryssolouris, G., Tool wear predictability estimation in milling based on multi-sensorial data. Int. J. Adv. Manuf. Technol. 82 (2016), 509–521.
41. [41] Liu, H., Lian, L., Li, B., Mao, X., Yuan, S., Peng, F., An approach based on singular spectrum analysis and the Mahalanobis distance for tool breakage detection. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 228 (2014), 3505–3516.
42. [42] Boutros, T., Liang, M., Detection and diagnosis of bearing and cutting tool faults using hidden Markov models. Mech. Syst. Signal Process. 25 (2011), 2102–2124.
43. [43] Ai, C., Sun, Y., He, G., Ze, X., Li, W., Mao, K., The milling tool wear monitoring using the acoustic spectrum. Int. J. Adv. Manuf. Technol. 61 (2012), 457–463.
44. [44] Girardin, F., Rémond, D., Rigal, J.-F., A new method for detecting tool wear and breakage in milling. Int. J. Mater. Form. 3 (2010), 463–466.
45. [45] Marinescu, I., Axinte, D.A., A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations. Int. J. Mach. Tools Manuf. 48 (2008), 1148–1160.
46. [46] Marinescu, I., Axinte, D., A time–frequency acoustic emission-based monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously. Int. J. Mach. Tools Manuf. 49 (2009), 53–65.
47. [47] Cao, H., Chen, Xuefeng, Zi, Y., Ding, F., Huaxin, C., Jiyong, T., Zhengjia, H., End milling tool breakage detection using lifting scheme and Mahalanobis distance. Int. J. Mach. Tools Manuf. 48 (2008), 141–151.
48. [48] Zhu, K., Wong, Y.S., Hong, G.S., Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int. J. Mach. Tools Manuf. 49 (2009), 537–553.
49. [49] Ko, T.J., Cho, D.W., Adaptive modelling of the milling process and application of a neural network for tool wear monitoring. Int. J. Adv. Manuf. Technol. 12 (1996), 5–13.
50. [50] Li, S., Elbestawi, M.A., Tool condition monitoring in machining by fuzzy neural networks. J. Dyn. Syst. Meas. Control Trans. Asme 118 (1996), 665–672.
51. [51] Li, X.Q., Wong, Y.S., Nee, A.Y.C., A comprehensive identification of tool failure and chatter using a parallel multi-ART2 neural network. J. Manuf. Sci. Eng. Trans. ASME 120 (1998), 433–442.
52. [52] Kuo, R.J., Cohen, P.H., Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network. Neural Netw. 12 (1999), 355–370.
53. [53] Dong, J.F., Subrahmanyam, K.V.R., Wong, Y.S., Hong, G.S., Mohanty, A.R., Bayesian-inference-based neural networks for tool wear estimation. Int. J. Adv. Manuf. Technol. 30 (2006), 797–807.
54. [54] Cetin, O., Ostendorf, M., Bernard, G.D., Multirate coupled hidden Markov models and their application to machining tool-wear classification. Ieee Trans. Signal Process. 55 (2007), 2885–2896.
55. [55] Zhu, K.P., Wong, Y.S., Hong, G.S., Multi-category micro-milling tool wear monitoring with continuous hidden Markov models. Mech. Syst. Signal Process. 23 (2009), 547–560.
56. [56] Geramifard, O., Xu, J.X., Zhou, J.H., Li, X., Physically, A., Segmented hidden Markov model approach for continuous tool condition monitoring: diagnostics and prognostics. Ieee Trans. Ind. Inform. 8 (2012), 964–973.
57. [57] Wang, M., Wang, J., CHMM for tool condition monitoring and remaining useful life prediction. Int. J. Adv. Manuf. Technol. 59 (2012), 463–471.
58. [58] Geramifard, O., Xu, J.X., Zhou, J.H., Li, X., Multimodal hidden Markov model-based approach for tool wear monitoring. Ieee Trans. Ind. Electron. 61 (2014), 2900–2911.
59. [59] Jiaa, C.L., Dornfeld, D.A., A self-organizing approach to the prediction and detection of tool wear. Isa Trans. 37 (1998), 239–255.
60. [60] Wilkinson, P., Reuben, R.L., Jones, J.D.C., Barton, J.S., Hand, D.P., Carolan, T.A., Kidd, S.R., Tool wear prediction from acoustic emission and surface characteristics via an artificial neural network. Mech. Syst. Signal Process. 13 (1999), 955–966.
61. [61] Chen, J.C., Chen, J.C., An artificial-neural-networks-based in-process tool wear prediction system in milling operations. Int. J. Adv. Manuf. Technol. 25 (2005), 427–434.
62. [62] Giriraj, B., Raja, V.P., Gandhinadhan, R., Ganeshkumar, R., Prediction of tool wear in high speed machining using acoustic emission technique and neural network. Indian J. Eng. Mater. Sci. 13 (2006), 275–280.
63. [63] Palanisamy, P., Rajendran, I., Shanmugasundaram, S., Prediction of tool wear using regression and ANN models in end-milling operation. Int. J. Adv. Manuf. Technol. 37 (2008), 29–41.
64. [64] Basciftci, F., Seker, H., On-line prediction of tool wears by using methods of artificial neural networks and fuzzy logic. Sci. Res. Essays 5 (2010), 2883–2888.
65. [65] Yu, J.B., Online tool wear prediction in drilling operations using selective artificial neural network ensemble model. Neural Comput. Appl. 20 (2011), 473–485.
66. [66] Drouillet, C., Karandikar, J., Nath, C., Journeaux, A.-C., El Mansori, M., Kurfess, T., Tool life predictions in milling using spindle power with the neural network technique. J. Manuf. Process. 22 (2016), 161–168.
67. [67] Susanto, V., Chen, J.C., Fuzzy logic based in-process tool-wear monitoring system in face milling operations. Int. J. Adv. Manuf. Technol. 21 (2003), 186–192.
68. [68] Aliustaoglu, C., Ertunc, H.M., Ocak, H., Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech. Syst. Signal Process. 23 (2009), 539–546.
69. [69] Brezak, D., Majetic, D., Udiljak, T., Kasac, J., Tool wear estimation using an analytic fuzzy classifier and support vector machines. J. Intell. Manuf. 23 (2012), 797–809.
70. [70] Xu, C.W., Chen, H.L., Milling tool wear forecast based on the partial least-squares regression analysis. Struct. Eng. Mech. 31 (2009), 57–74.
71. [71] Azmi, A.I., Lin, R.J.T., Bhattacharyya, D., Tool wear prediction models during end milling of glass fibre-reinforced polymer composites. Int. J. Adv. Manuf. Technol. 67 (2013), 701–718.
72. [72] Wang, G.F., Qian, L., Guo, Z.W., Continuous tool wear prediction based on Gaussian mixture regression model. Int. J. Adv. Manuf. Technol. 66 (2013), 1921–1929.
73. [73] Zhang, C., Zhang, H.Y., Modelling and prediction of tool wear using LS-SVM in milling operation. Int. J. Comput. Integr. Manuf. 29 (2016), 76–91.
74. [74] Yang, M., Choi, J., A tool deflection compensation system for end milling accuracy improvement. J. Manuf. Sci. Eng. Trans. ASME 120 (1998), 222–229.
75. [75] B. Denkena, Will, J.C., Sellmeier, V., Prediction of process stability and dynamic forces of an adaptronic spindle system, in: Proceedings of the Adaptronic Congress, Göttingen, Germany, 2006, pp. 1–7.
76. [76] Wan, M., Zhang, W., Qin, G., Wang, Z., Strategies for error prediction and error control in peripheral milling of thin-walled workpiece. Int. J. Mach. Tools Manuf. 48 (2008), 1366–1374.
77. [77] Župerl, U., Čuš, F., Merged neural decision system and anfis wear predictor for supporting tool condition monitoring. Trans. FAMENA 35 (2011), 13–26.
78. [78] Marinescu, I., Axinte, D.A., An automated monitoring solution for avoiding an increased number of surface anomalies during milling of aerospace alloys. Int. J. Mach. Tools Manuf. 51 (2011), 349–357.
79. [79] Dépincé, P., Hascoët, J.-Y., Active integration of tool deflection effects in end milling. Part 2. Compensation of tool deflection. Int. J. Mach. Tools Manuf. 46 (2006), 945–956.
80. [80] Tang, Y., Xu, C., Jackson, M.J., Adaptive compensation of tool deflection in micromilling processes. Int. J. Nanomanufacturing 3 (2009), 159–168.
81. [81] Law, K.M., Geddam, A., Error compensation in the end milling of pockets: a methodology. J. Mater. Process. Technol. 139 (2003), 21–27.
82. [82] T. Dow, E. Miller, A. Sohn, K. Garrard, T. Wright, Compensation of tool forces in small diameter end mills, in: ASPE Proceedings, 1999, pp. 546–550.
83. [83] Y. Tang, C. Xu, M. Jackson, Geometrical adaptive controller for tool deflection compensation in helical end milling processes, in: Proceedings of the ASME 2009 International Manufacturing Science and Engineering Conference, American Society of Mechanical Engineers, 2009, pp. 377–383.
84. [84] Rao, V., Rao, P., Tool deflection compensation in peripheral milling of curved geometries. Int. J. Mach. Tools Manuf. 46 (2006), 2036–2043.
85. [85] X. Sui, P. Zhao, C. Zhang, P. Zhang, N. Hu, Modeling and compensation analysis of ball-end milling cutter wear, in: Proceedings of the Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on, IEEE, 2011, pp. 3293–3296.
86. [86] H.T. Nguyen, H. Wang, S.J. Hu, High-definition metrology enabled surface variation control by reducing cutter-spindle deflection, in: Proceedings of the ASME 2014 International Manufacturing Science and Engineering Conference, American Society of Mechanical Engineers, Detroit, Michigan, USA., 2014, pp. V001T004A038.
87. [87] Suh, S.-H., Cho, J.-H., Hascoet, J.-Y., Incorporation of tool deflection in tool path computation: simulation and analysis. J. Manuf. Syst. 15 (1996), 190–199.
88. [88] Altintaş, Y., Budak, E., Analytical prediction of stability lobes in milling. CIRP Ann. Manuf. Technol. 44 (1995), 357–362.
89. [89] Budak, E., Altintas, Y., Analytical prediction of chatter stability in milling – Part 1: general formulation. J. Dyn. Syst. Meas. Control Trans. ASME 120 (1998), 22–30.
90. [90] Insperger, T., Stépán, G., Semi-discretization method for delayed systems. Int. J. Numer. Methods Eng. 55 (2002), 503–518.
91. [91] Insperger, T., Stépán, G., Updated semi-discretization method for periodic delay-differential equations with discrete delay. Int. J. Numer. Methods Eng. 61 (2004), 117–141.
92. [92] Merdol, S.D., Altintas, Y., Multi frequency solution of chatter stability for low immersion milling. J. Manuf. Sci. Eng. Trans. ASME 126 (2004), 459–466.
93. [93] Quintana, G., Ciurana, J., Ferrer, I., Rodríguez, C.A., Sound mapping for identification of stability lobe diagrams in milling processes. Int. J. Mach. Tools Manuf. 49 (2009), 203–211.
94. [94] Ding, Y., Zhu, L., Zhang, X., Ding, H., A full-discretization method for prediction of milling stability. Int. J. Mach. Tools Manuf. 50 (2010), 502–509.
95. [95] Ahmadi, K., Ismail, F., Stability lobes in milling including process damping and utilizing multi-frequency and semi-discretization methods. Int. J. Mach. Tools Manuf. 54–55 (2012), 46–54.
96. [96] Liu, Y., Zhang, D., Wu, B., An efficient full-discretization method for prediction of milling stability. Int. J. Mach. Tools Manuf. 63 (2012), 44–48.
97. [97] Quo, Q., Sun, Y., Jiang, Y., On the accurate calculation of milling stability limits using third-order full-discretization method. Int. J. Mach. Tools Manuf. 62 (2012), 61–66.
98. [98] Ozoegwu, C.G., Omenyi, S.N., Ofochebe, S.M., Hyper-third order full-discretization methods in milling stability prediction. Int. J. Mach. Tools Manuf. 92 (2015), 1–9.
99. [99] Zhang, Z., Li, H., Meng, G., Liu, C., A novel approach for the prediction of the milling stability based on the Simpson method. Int. J. Mach. Tools Manuf. 99 (2015), 43–47.
100. [100] Altintas, Y., Weck, M., Chatter stability of metal cutting and grinding. CIRP Ann. Manuf. Technol. 53 (2004), 619–642.
101. [101] Quintana, G., Ciurana, J., Chatter in machining processes: a review. Int. J. Mach. Tools Manuf. 51 (2011), 363–376.
102. [102] Wang, S.-M., Luo, M.-J., Lin, S.-Y., Liao, H.-W., Application of wavelet transform on diagnosis and prediction of milling chatter. Chin. J. Mech. Eng. 20 (2007), 67–70.
103. [103] Cao, H., Lei, Y., He, Z., Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform. Int. J. Mach. Tools Manuf. 69 (2013), 11–19.
104. [104] Cao, H., Zhou, K., Chen, X., Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators. Int. J. Mach. Tools Manuf. 92 (2015), 52–59.
105. [105] Fu, Y., Zhang, Y., Zhou, H.M., Li, D.Q., Liu, H.Q., Qiao, H.Y., Wang, X.Q., Timely online chatter detection in end milling process. Mech. Syst. Signal Process. 75 (2016), 668–688.
106. [106] Delio, T., Tlusty, J., Smith, S., Use of audio signals for chatter detection and control. J. Eng. Ind. 114 (1992), 146–157.
107. [107] Schmitz, T.L., Chatter recognition by a statistical evaluation of the synchronously sampled audio signal. J. Sound Vib. 262 (2003), 721–730.
108. [108] Schmitz, T.L., Medicus, K., Dutterer, B., Exploring once-per-revolution audio signal variance as a chatter indicator. Mach. Sci. Technol. 6 (2002), 215–233.
109. [109] Tsai, N.-C., Chen, D.-C., Lee, R.-M., Chatter prevention for milling process by acoustic signal feedback. Int. J. Adv. Manuf. Technol. 47 (2010), 1013–1021.
110. [110] Soliman, E., Ismail, F., Chatter detection by monitoring spindle drive current. Int. J. Adv. Manuf. Technol. 13 (1997), 27–34.
111. [111] Lamraoui, M., El Badaoui, M., Guillet, F., Chatter detection in CNC milling processes based on Wiener-SVM approach and using only motor current signals. Vib. Eng. Technol. Mach. 23 (2015), 567–578.
112. [112] Kwak, J.-S., Ha, M.-K., Neural network approach for diagnosis of grinding operation by acoustic emission and power signals. J. Mater. Process. Technol. 147 (2004), 65–71.
113. [113] Du, R., Elbestawi, M.A., Ullagaddi, B.C., Chatter detection in milling based on the probability distribution of cutting force signal. Mech. Syst. Signal Process. 6 (1992), 345–362.
114. [114] N. Pongsathornwiwat, S. Tangjitsitcharoen, Intelligent monitoring and detection of chatter in ball-end milling process on CNC machining center, in: Proceedings of the Computers and Industrial Engineering (CIE), 2010 40th International Conference on, 2010, pp. 1–6.
115. [115] Govekar, E., Gradišek, J., Grabec, I., Analysis of acoustic emission signals and monitoring of machining processes. Ultrasonics 38 (2000), 598–603.
116. [116] Van Dijk, N.J.M., Doppenberg, E.J.J., Faassen, R.P.H., van de Wouw, N., Oosterling, J.A.J., Nijmeijer, H., Automatic in-process chatter avoidance in the high-speed milling process. J. Dyn. Syst. Meas. Control Trans. ASME, 132, 2010, 031006.
117. [117] Tangjitsitcharoen, S., In-process monitoring and detection of chip formation and chatter for CNC turning. J. Mater. Process. Technol. 209 (2009), 4682–4688.
118. [118] Yao, Z., Mei, D., Chen, Z., On-line chatter detection and identification based on wavelet and support vector machine. J. Mater. Process. Technol. 210 (2010), 713–719.
119. [119] Wang, L., Liang, M., Chatter detection based on probability distribution of wavelet modulus maxima. Robot. Comput. Integr. Manuf. 25 (2009), 989–998.
120. [120] Choi, T., Shin, Y.C., On-line chatter detection using wavelet-based parameter estimation. J. Manuf. Sci. Eng. Trans. ASME 125 (2003), 21–28.
121. [121] K. Bickraj, B. Kaya, A. Yapici, M. Li, I.N. Tansel, B. Ozcelik, Inspection of Chatter Damage in End Milling Operations by Using Wavelet Transformations, Tampico, México, 2007, pp. 1–6.
122. [122] Wu, Y., Du, R., Feature extraction and assessment using wavelet packets for monitoring of machining processes. Mech. Syst. Signal Process. 10 (1996), 29–53.
123. [123] Zhang, Z., Li, H., Meng, G., Tu, X., Cheng, C., Chatter detection in milling process based on the energy entropy of VMD and WPD. Int. J. Mach. Tools Manuf. 108 (2016), 106–112.
124. [124] Schmitz, T.L., Davies, M.A., Medicus, K., Snyder, J., Improving high-speed machining material removal rates by rapid dynamic analysis. CIRP Ann. Manuf. Technol. 50 (2001), 263–268.
125. [125] Bishop, C.M., Neural Networks for Pattern Recognition, 1995, Oxford University Press Inc., New York, United States.
126. [126] Hino, J., Okubo, S., Yoshimura, T., Chatter prediction in end milling by FNN model with pruning, JSME. Int. J. Ser. C Mech. Syst. Mach. Elem. Manuf. 49 (2006), 742–749.
127. [127] Lamraoui, M., Barakat, M., Thomas, M., Badaoui, M.E., Chatter detection in milling machines by neural network classification and feature selection. J. Vib. Control 21 (2013), 1251–1266.
128. [128] Xu, D., A Fuzzy Logic Approach for Chatter Detection and Suppression in End Milling, 2003, University of Ottawa, Canada.
129. [129] Liang, M., Yeap, T., Hermansyah, A., A fuzzy system for chatter suppression in end milling. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 218 (2004), 403–417.
130. [130] Wang, L., Chatter Detection and Suppression Using Wavelet and Fuzzy Control Approaches in End Milling, 2005, University of Ottawa, Canada.
131. [131] Wu, S., Jia, D.K., Liu, X.L., Yan, F.G., Li, Y.F., Application of continuous wavelet features and multi-class sphere SVM to chatter prediction. Adv. Mater. Res. 188 (2011), 675–680.
132. [132] B. Chen, J. Yang, J. Zhao, J. Ren, Milling chatter prediction based on the information entropy and support vector machine, in: Proceedings of the International Industrial Informatics and Computer Engineering Conference Atlantis Press, Xi'an, 2015, pp. 376–380.
133. [133] Sun, H.B., Zhang, X.Z., Wang, J.Y., Online machining chatter forecast based on improved local mean decomposition. Int. J. Adv. Manuf. Technol. 84 (2016), 1045–1056.
134. [134] J. Kang, C.J. Feng, H.Y. Hu, Q. Shao, Research on chatter prediction and monitor based on DHMM pattern recognition theory, in: Proceedings of the 2007 IEEE International Conference on Automation and Logistics, 2007, pp. 1368–1372.
135. [135] Mei, D., Li, X., Chen, Z., Prediction of cutting chatter based on hidden Markov model. Key Eng. Mater. 353–358 (2007), 2712–2715.
136. [136] C.L. Zhang, X. Yue, Y.T. Jiang, W. Zheng, A hybrid approach of ANN and HMM for cutting chatter monitoring, in: Z. Jiang, C.L. Zhang (Eds.), Manufacturing Science and Engineering, Pts 1–5, 2010, pp. 3225–3232.
137. [137] Munoa, J., Beudaert, X., Dombovari, Z., Altintas, Y., Budak, E., Brecher, C., Stepan, G., Chatter suppression techniques in metal cutting. CIRP Ann. Manuf. Technol. 65 (2016), 785–808.
138. [138] Haber, R.E., Peres, C.R., Alique, A., Ros, S., Gonzalez, C., Alique, J.R., Toward intelligent machining: hierarchical fuzzy control for the end milling process. Ieee Trans. Control Syst. Trans. 6 (1998), 188–199.
139. [139] Smith, S., Delio, T., Sensor-based chatter detection and avoidance by spindle speed selection. J. Dyn. Syst., Meas., Control Trans. ASME 114 (1992), 486–492.
140. [140] Giorgio Bort, C.M., Leonesio, M., Bosetti, P., A model-based adaptive controller for chatter mitigation and productivity enhancement in CNC milling machines. Robot. Comput. Integr. Manuf. 40 (2016), 34–43.
141. [141] Yilmaz, A., Al-Regib, E., Ni, J., Machine tool chatter suppression by multi-level random spindle speed variation. J. Manuf. Sci. Eng. Trans. ASME, 124, 2002, 208.
142. [142] Insperger, T., Stepan, G., Stability analysis of turning with periodic spindle speed modulation via semidiscretization. J. Vib. Control 10 (2004), 1835–1855.
143. [143] Ries M, P.S., Gebert, K., Increase of Material Removal Rate With an Active HSC Milling Spindle. 2006, Adaptronic Congress, Gцttingen, Germany.
144. [144] Denkena, B., Gümmer, O., Process stabilization with an adaptronic spindle system. Prod. Eng. 6 (2012), 485–492.
145. [145] Monnin, J., Kuster, F., Wegener, K., Optimal control for chatter mitigation in milling-Part 1: modeling and control design. Control Eng. Pract. 24 (2014), 156–166.
146. [146] Monnin, J., Kuster, F., Wegener, K., Optimal control for chatter mitigation in milling—Part 2: experimental validation. Control Eng. Pract. 24 (2014), 167–175.
147. [147] S. Kern, A. Schiffler, R. Nordmann, E. Abele, Modelling and active damping of a motor spindle with speed-dependent dynamics, in: Proceedings of the 9th International Conference on Vibrations in Rotating Machinery, 2008, pp. 465–475.
148. [148] van Dijk, N., van de Wouw, N., Doppenberg, E., Oosterling, H., Nijmeijer, H., Chatter control in the high-speed milling process using mu-synthesis. Proc. Am. Control Conf., 2010, 6121–6126.
149. [149] van Dijk, N.J.M., van de Wouw, N., Doppenberg, E.J.J., Oosterling, H.A.J., Nijmeijer, H., Robust active chatter control in the high-speed milling process. Ieee Trans. Control Syst. Trans. 20 (2012), 901–917.
150. [150] Denkena, B., Bickel, W., Ponick, B., Emmrich, J., Dynamic analysis of a motor-integrated method for a higher milling stability. Prod. Eng. 5 (2011), 691–699.
151. [151] Chen, M., Knospe, C.R., Control approaches to the suppression of machining chatter using active magnetic bearings. Ieee Trans. Control Syst. Trans. 15 (2007), 220–232.
152. [152] Chen, Z., Zhang, H.-T., Zhang, X., Ding, H., Adaptive active chatter control in milling processes. J. Dyn. Syst. Meas. Control Trans. ASME, 136, 2013, 021007.
153. [153] Zhang, H.-T., Wu, Y., He, D., Zhao, H., Model predictive control to mitigate chatters in milling processes with input constraints. Int. J. Mach. Tools Manuf. 91 (2015), 54–61.
154. [154] Shankar, B.K., Regelbrugge, N., Winfough, M.E., Smart, W.R., spindle unit for active chatter suppression of a milling machine: I. Overview, fabrication and assembly. Proc. SPIE, 1998, 160–166.
155. [155] Dohner, J.L., Lauffer, J.P., Hinnerichs, T.D., Shankar, N., Regelbrugge, M., Kwan, C.M., Xu, R., Winterbauer, B., Bridger, K., Mitigation of chatter instabilities in milling by active structural control. J. Sound Vib. 269 (2004), 197–211.
156. [156] T. Kohmäscher, Active spindle bearing device for chatter control within milling machines, in: Proceedings of the Conference-Speech, 3rd PhD Symp., Terrassa, Spain, 2004.
157. [157] Lin, C.-W., Tu, J.F., Kamman, J., An integrated thermo-mechanical-dynamic model to characterize motorized machine tool spindles during very high speed rotation. Int. J. Mach. Tools Manuf. 43 (2003), 1035–1050.
158. [158] Cao, Y., Altintas, Y., A general method for the modeling of spindle-bearing systems. J. Mech. Des. -Trans. ASME 126 (2004), 1089–1104.
159. [159] Li, H., Shin, Y.C., Integrated dynamic thermo-mechanical modeling of high speed spindles, part 1: model development. J. Manuf. Sci. Eng. Trans. ASME 126 (2004), 148–158.
160. [160] Li, H.Q., Shin, Y.C., Integrated dynamic thermo-mechanical modeling of high speed spindles, part 2: solution procedure and validations. J. Manuf. Sci. Eng. Trans. ASME 126 (2004), 159–168.
161. [161] M.D.E. Abele, M. Kreis, Beeinflussbarkeit von Lebenszykluskosten durch Wissensaustausch–Produzieren mit Blick auf die Lebenszykluskosten, wt-online, 2006, pp. 447–454.
162. [162] Wächter, T.J., Siart, U., Eibert, T.F., Bonerz, S., Multi-sensor Doppler radar for machine tool collision detection. Adv. Radio Sci. 12 (2014), 35–41.
163. [163] Abele, E., Korff, D., Avoidance of collision-caused spindle damages—challenges, methods and solutions for high dynamic machine tools. CIRP Ann. Manuf. Technol. 60 (2011), 425–428.
164. [164] Abele, E., Brecher, C., Gsell, S.C., Hassis, A., Korff, D., Steps towards a protection system for machine tool main spindles against crash-caused damages. Prod. Eng. 6 (2012), 631–642.
165. [165] Koike, R., Kakinuma, Y., Aoyama, T., Ohnishi, K., Tool collision detection in high-speed feeding based on disturbance observer. Procedia CIRP 14 (2014), 478–483.
166. [166] Tönissen, S., Koike, R., Kakinuma, Y., Aoyama, T., Klocke, F., Monitoring of tool collision in drilling by disturbance observer. CIRP J. Manuf. Sci. Technol. 7 (2014), 274–282.
167. [167] T. Rudolf, C. Brecher, F. Possel-Dölken, Contact-based collision detection – a new approach to avoid hard collisions in machine tools, in: Proceedings of the International Conference on Smart Machining Systems, Gaithersburg, Maryland, USA., 2007.
168. [168] Byrne, G., O'Donnell, G., An integrated force sensor solution for process monitoring of drilling operations. CIRP Ann. Manuf. Technol. 56 (2007), 89–92.
169. [169] Berger, M., Korff, D., Avoiding collision damage of motor spindles through an innovative overload protection system. Adv. Mater. Res. 1018 (2014), 357–364.
170. [170] Schumann, M., Witt, M., Klimant, P., A real-time collision prevention system for machine tools (part II). Procedia CIRP 41 (2016), 789–794.
171. [171] Bryan, J., International status of thermal error research. CIRP Ann. Manuf. Technol. 39 (1990), 645–656.
172. [172] Weck, M., McKeown, P., Bonse, R., Herbst, U., Reduction and compensation of thermal errors in machine tools. CIRP Ann. Manuf. Technol. 44 (1995), 589–598.
173. [173] Ramesh, R., Mannan, M.A., Poo, A.N., Error compensation in machine tools — a review: part I: geometric, cutting-force induced and fixture-dependent errors. Int. J. Mach. Tools Manuf. 40 (2000), 1235–1256.
174. [174] Mayr, J., Jedrzejewski, J., Uhlmann, E., Alkan Donmez, M., Knapp, W., Härtig, F., Wendt, K., Moriwaki, T., Shore, P., Schmitt, R., Brecher, C., Würz, T., Wegener, K., Thermal issues in machine tools. CIRP Ann. Manuf. Technol. 61 (2012), 771–791.
175. [175] Li, Y., Zhao, W., Lan, S., Ni, J., Wu, W., Lu, B., A review on spindle thermal error compensation in machine tools. Int. J. Mach. Tools Manuf. 95 (2015), 20–38.
176. [176] Takabi, J., Khonsari, M.M., On the thermally-induced failure of rolling element bearings. Tribol. Int. 94 (2016), 661–674.
177. [177] Ohishi, S., Matsuzaki, Y., Experimental investigation of air spindle unit thermal characteristics. Precis. Eng. 26 (2002), 49–57.
178. [178] Moriwaki, T., Shamoto, E., Analysis of thermal deformation of an ultraprecision air spindle system. CIRP Ann. Manuf. Technol. 47 (1998), 315–319.
179. [179] Pahk, H.J., Lee, S.W., Kwon, H.D., Thermal error measurement and modelling techniques for the five-degree-of-freedom spindle drifts in computer numerically controlled machine tools. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 215 (2001), 469–485.
180. [180] Srinivasa, N., Ziegert, J.C., Mize, C.D., Spindle thermal drift measurement using the laser ball bar. Precis. Eng. 18 (1996), 118–128.
181. [181] Yang, S.-H., Kim, K.-H., Park, Y.K., Measurement of spindle thermal errors in machine tool using hemispherical ball bar test. Int. J. Mach. Tools Manuf. 44 (2004), 333–340.
182. [182] Chang, C.-F., Chen, J.-J., Thermal growth control techniques for motorized spindles. Mechatronics 19 (2009), 1313–1320.
183. [183] Hsieh, K.-H., Chen, T.-R., Chang, P., Tang, C.-H., Thermal growth measurement and compensation for integrated spindles. Int. J. Adv. Manuf. Technol. 64 (2013), 889–901.
184. [184] Sarhan, A.A.D., Investigate the spindle errors motions from thermal change for high-precision CNC machining capability. Int. J. Adv. Manuf. Technol. 70 (2014), 957–963.
185. [185] Yan, Z.-S., Lin, W.-H., Liu, C.-H., Measurement of the thermal elongation of high speed spindles in real time using a cat's eye reflector based optical sensor. Sens. Actuators A: Phys. 221 (2015), 154–160.
186. [186] Denkena, B., Scharschmidt, K.-H., Kompensation thermischer Verlagerungen, wt. Werkstattstech. Online 97 (2007), 913–917.
187. [187] Du, Z.C., Yao, S.Y., Yang, J.G., Thermal behavior analysis and thermal error compensation for motorized spindle of machine tools. Int. J. Precis. Eng. Manuf. 16 (2015), 1571–1581.
188. [188] Brecher, C., Hirsch, P., Weck, M., Compensation of thermo-elastic machine tool deformation based on control internal data. CIRP Ann. Manuf. Technol. 53 (2004), 299–304.
189. [189] Brecher, C., Wissmann, A., Compensation of thermo-dependent machine tool deformations due to spindle load: investigation of the optimal transfer function in consideration of rough machining. Prod. Eng. 5 (2011), 565–574.
190. [190] Brecher, C., Wissmann, A., Compensation of thermo-dependent machine tool deformations due to spindle load based on reduced modeling effort. Int. J. Autom. Technol.(5), 2011, 679–687.
191. [191] Fraser, S., Attia, M.H., Osman, M.O.M., Modelling, identification and control of thermal deformation of machine tool structures, Part 1: concept of generalized modelling. J. Manuf. Sci. Eng. 120 (1998), 623–631.
192. [192] Fraser, S., Attia, M.H., Osman, M.O.M., Modelling, identification and control of thermal deformation of machine tool structures, Part 2: generalized transfer functions. J. Manuf. Sci. Eng. 120 (1998), 632–639.
193. [193] Fraser, S., Attia, M.H., Osman, M.O.M., Modelling, identification and control of thermal deformation of machine tool structures, Part 5: experimental verification. J. Manuf. Sci. Eng. 121 (1999), 517–523.
194. [194] O. Horejš, M. Mareš, P. Kohút, P. Bárta, J. Hornych, A compensation technique of machine tool thermal errors built on thermal transfer functions, in: Proceedings of the 5th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2009, 2009.
195. [195] O. Horejš, M Mareš, L. Novotný, Advanced modelling of thermally induced displacements and its implementation into standard CNC controller of horizontal milling center, in: Procedia CIRP, 2012, pp. 67–72.
196. [196] Li, Y.L., Wang, M.Y., Hu, Y.M., Wu, B., Thermal error prediction of the spindle using improved fuzzy-filtered neural networks. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 230 (2016), 770–778.
197. [197] Cheng, Q., Qi, Z., Zhang, G.J., Zhao, Y.S., Sun, B.W., Gu, P.H., Robust modelling and prediction of thermally induced positional error based on grey rough set theory and neural networks. Int. J. Adv. Manuf. Technol. 83 (2016), 753–764.
198. [198] Yang, J., Shi, H., Feng, B., Zhao, L., Ma, C., Mei, X.S., Thermal error modeling and compensation for a high-speed motorized spindle. Int. J. Adv. Manuf. Technol. 77 (2015), 1005–1017.
199. [199] Li, D.X., Feng, P.F., Zhang, J.F., Wu, Z.J., Yu, D.W., Calculation method of convective heat transfer coefficients for thermal simulation of a spindle system based on RBF neural network. Int. J. Adv. Manuf. Technol. 70 (2014), 1445–1454.
200. [200] Huang, Y.Q., Zhang, J., Li, X., Tian, L.J., Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle. Int. J. Adv. Manuf. Technol. 71 (2014), 1669–1675.
201. [201] Yang, H., Ni, J., Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. Int. J. Mach. Tools Manuf. 45 (2005), 455–465.
202. [202] Chen, J.S., Hsu, W.Y., Characterizations and models for the thermal growth of a motorized high speed spindle. Int. J. Mach. Tools Manuf. 43 (2003), 1163–1170.
203. [203] Yang, S., Yuan, J., Ni, J., The improvement of thermal error modeling and compensation on machine tools by CMAC neural network. Int. J. Mach. Tools Manuf. 36 (1996), 527–537.
204. [204] Chen, J.S., Yuan, J., Ni, J., Thermal error modelling for real-time error compensation. Int. J. Adv. Manuf. Technol. 12 (1996), 266–275.
205. [205] Liang, R., Ye, W., Zhang, H.H., Yang, Q., The thermal error optimization models for CNC machine tools. Int. J. Adv. Manuf. Technol. 63 (2012), 1167–1176.
206. [206] Li, Y., Zhao, W.H., Wu, W.W., Lu, B.H., Chen, Y.B., Thermal error modeling of the spindle based on multiple variables for the precision machine tool. Int. J. Adv. Manuf. Technol. 72 (2014), 1415–1427.
207. [207] Fan, K.G., Yang, J.G., Yang, L.Y., Orthogonal polynomials-based thermally induced spindle and geometric error modeling and compensation. Int. J. Adv. Manuf. Technol. 65 (2013), 1791–1800.
208. [208] Pahk, H.J., Lee, S.W., Thermal error measurement and real time compensation system for the CNC machine tools incorporating the spindle thermal error and the feed axis thermal error. Int. J. Adv. Manuf. Technol. 20 (2002), 487–494.
209. [209] J. Mayr, M. Egeter, S. Weikert, K. Wegener, Thermal Error Compensation of Rotary Axes and Main Spindles Using Cooling Power as Input Parameter, 2015.
210. [210] Gebhardt, M., Mayr, J., Furrer, N., Widmer, T., Weikert, S., Knapp, W., High precision grey-box model for compensation of thermal errors on five-axis machines. Cirp Ann. Manuf. Technol. 63 (2014), 509–512.
211. [211] Zhang, Y., Yang, J., Jiang, H., Machine tool thermal error modeling and prediction by grey neural network. Int. J. Adv. Manuf. Technol. 59 (2012), 1065–1072.
212. [212] Miao, E.-M., Gong, Y.-Y., Niu, P.-C., Ji, C.-Z., Chen, H.-D., Robustness of thermal error compensation modeling models of CNC machine tools. Int. J. Adv. Manuf. Technol. 69 (2013), 2593–2603.
213. [213] Ramesh, R., Mannan, M.A., Poo, A.N., Keerthi, S.S., Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network—support vector machine model. Int. J. Mach. Tools Manuf. 43 (2003), 405–419.
214. [214] Ramesh, R., Mannan, M.A., Poo, A.N., Support vector machines model for classification of thermal error in machine tools. Int. J. Adv. Manuf. Technol. 20 (2002), 114–120.
215. [215] Chen, J.-S., Hsu, W.-Y., Characterizations and models for the thermal growth of a motorized high speed spindle. Int. J. Mach. Tools Manuf. 43 (2003), 1163–1170.
216. [216] Li, Y., Zhao, W., Wu, W., Lu, B., Chen, Y., Thermal error modeling of the spindle based on multiple variables for the precision machine tool. Int. J. Adv. Manuf. Technol. 72 (2014), 1415–1427.
217. [217] Huang, Y., Zhang, J., Li, X., Tian, L., Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle. Int. J. Adv. Manuf. Technol. 71 (2014), 1669–1675.
218. [218] Xiang, S., Lu, H., Yang, J., Thermal error prediction method for spindles in machine tools based on a hybrid model, Proceedings of the Institution of Mechanical Engineers. Part B: J. Eng. Manuf. 229 (2015), 130–140.
219. [219] Moriwaki, T., Thermal deformation and its on-line compensation of hydrostatically supported precision spindle. CIRP Ann. Manuf. Technol. 37 (1988), 393–396.
220. [220] Chen, J.-S., Computer-aided accuracy enhancement for multi-axis CNC machine tool. Int. J. Mach. Tools Manuf. 35 (1995), 593–605.
221. [221] Srinivasa, N., Ziegert, J.C., Automated measurement and compensation of thermally induced error maps in machine tools. Precis. Eng. J. Am. Soc. Precis. Eng. 19 (1996), 112–132.
222. [222] Li, S.H., Zhang, Y.Q., Zhang, G.X., A study of pre-compensation for thermal errors of NC machine tools. Int. J. Mach. Tools Manuf. 37 (1997), 1715–1719.
223. [223] Ni, J., CNC machine accuracy enhancement through real-time error compensation. J. Manuf. Sci. Eng. 119 (1997), 717–725.
224. [224] Pahk, H., Lee, S.W., Thermal error measurement and real time compensation system for the CNC machine tools incorporating the spindle thermal error and the feed axis thermal error. Int. J. Adv. Manuf. Technol. 20 (2002), 487–494.
225. [225] Yang, H., Ni, J., Dynamic modeling for machine tool thermal error compensation. J. Manuf. Sci. Eng. Trans. Asme 125 (2003), 245–254.
226. [226] Kim, K.D., Kim, M.S., Chung, S.C., Real-time compensatory control of thermal errors for high-speed machine tools. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 218 (2004), 913–924.
227. [227] Fan, K.C., An intelligent thermal error compensation system for CNC machining centers. J. Chin. Soc. Mech. Eng. 28 (2007), 91–97.
228. [228] Yang, Z.Y., Sun, M.L., Li, W.Q., Liang, W.Y., Modified Elman network for thermal deformation compensation modeling in machine tools. Int. J. Adv. Manuf. Technol. 54 (2011), 669–676.
229. [229] Vyroubal, J., Compensation of machine tool thermal deformation in spindle axis direction based on decomposition method. Precis. Eng. J. Int. Soc. Precis. Eng. Nanotechnol. 36 (2012), 121–127.
230. [230] Hsieh, K.H., Chen, T.R., Chang, P., Tang, C.H., Thermal growth measurement and compensation for integrated spindles. Int. J. Adv. Manuf. Technol. 64 (2013), 889–901.
231. [231] Wang, W., Zhang, Y., Yang, J.G., Zhang, Y.S., Yuan, F., Geometric and thermal error compensation for CNC milling machines based on Newton interpolation method. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 227 (2013), 771–778.
232. [232] Zhang, Y., Yang, J.G., Xiang, S.T., Xiao, H.X., Volumetric error modeling and compensation considering thermal effect on five-axis machine tools. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 227 (2013), 1102–1115.
233. [233] Yang, J., Mei, X.S., Zhao, L., Ma, C., Shi, H., Feng, B., Thermal error compensation on a computer numerical control machine tool considering thermal tilt angles and cutting tool length. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 229 (2015), 78–97.
234. [234] Liu, K., Sun, M.J., Zhu, T.J., Wu, Y.L., Liu, Y., Modeling and compensation for spindle's radial thermal drift error on a vertical machining center. Int. J. Mach. Tools Manuf. 105 (2016), 58–67.
235. [235] Mayr, J., Muller, M., Weikert, S., Automated thermal main spindle and B-axis error compensation of 5-axis machine tools. Cirp Ann. Manuf. Technol. 65 (2016), 479–482.
236. [236] E. Uhlmann, P. Marcks, Compensation of thermal deformations at machine tools using adaptronic CRP-structures, in: M. Mitsuishi, K. Ueda, F. Kimura (Eds.), Manufacturing Systems and Technologies for the New Frontier: The 41st CIRP Conference on Manufacturing Systems May 26–28, 2008, Tokyo, Japan, Springer London, London, 2008, pp. 183–186.
237. [237] Uhlmann, E., Saoji, M., Peukert, B., Utilization of thermal energy to compensate quasi-static deformations in modular machine tool frames. Procedia CIRP 40 (2016), 1–6.
238. [238] D. Ni, M. Jing, H. Fan, M. Li, H. Liu, J. Li, Study on monitoring and warning system for high-speed motorized spindle based on vibration signals, in: Proceedings of the 5th Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2013, January 16–17, 2013, IEEE Computer Society, Hong Kong, China, 2013, pp. 1076–1079.
239. [239] Dyer, S.W., Ni, J., Adaptive influence coefficient control of single-plane active balancing systems for rotating machinery, Transactions-ASME. Trans. ASME J. Manuf. Sci. Eng. 123 (2001), 291–298.
240. [240] Fan, H.-W., Jing, M.-Q., Liu, H., Yang, T.-T., New machine tool motorized spindle integrated with one electromagnetic ring balancer driven by optimal square wave. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 229 (2015), 1509–1522.
241. [241] Hredzak, B., Guo, G., New electromechanical balancing device for active imbalance compensation. J. Sound Vib. 294 (2006), 737–751.
242. [242] Zhang, Y., Mei, X., Shao, M., Xu, M., An improved holospectrum-based balancing method for rotor systems with anisotropic stiffness. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 227 (2013), 246–260.
243. [243] Ma, S., Pei, S., Wang, L., Xu, H., A novel active online electromagnetic balancing method-principle and structure analysis. J. Vib. Acoust. Trans. ASME, 134, 2012, 034503.
244. [244] Pan, X., Wu, H., Gao, J.J., Mu, Y., Study on online active balancing system of rotating machinery and target control method. Wseas Trans. Syst. 13 (2014), 302–311.
245. [245] Grobel, L., Balancing turbine-generator rotors. Gen. Electr. Rev. 56 (1953), 22–25.
246. [246] Darlow, M.S., Balancing of High-Speed Machinery, 1989, Springer-Verlag, New York.
247. [247] Han, D.J., Generalized modal balancing for non-isotropic rotor systems. Mech. Syst. Signal Process. 21 (2007), 2137–2160.
248. [248] Deepthikumar, M.B., Sekhar, A.S., Srikanthan, M.R., Modal balancing of flexible rotors with bow and distributed unbalance. J. Sound Vib. 332 (2013), 6216–6233.
249. [249] Qiao, X., Zhu, C., The active unbalanced vibration compensation of the flexible switched reluctance motorized spindle. J. Vib. Control 20 (2013), 1934–1945.
250. [250] Goodman, T.P., A least-squares method for computing balance corrections. J. Manuf. Sci. Eng. 86 (1964), 273–277.
251. [251] S.H. Lee, B.S. Kim, J.D. Moon, D.H. Kim, A study on active balancing for rotating machinery using influence coefficient method, in: Computational Intelligence in Robotics and Automation, 2005, CIRA 2005, Proceedings, 2005 IEEE International Symposium on, 2005, pp. 659–664.
252. [252] M.S. Darlow, In situ balancing of flexible rotors using influence coefficient balancing and the unified balancing approach, in: Proceedings of the ASME 1983 International Gas Turbine Conference and Exhibit, New York, USA, 1983, pp. 1–5.
253. [253] Darlow, M.S., Smalley, A.J., Parkinson, A.G., Demonstration of a unified approach to the balancing of flexible rotors. J. Eng. Gas Turbines Power 103 (1981), 101–107.
254. [254] Parkinson, A.G., Darlow, M.S., Smalley, A.J., A theoretical introduction to the development of a unified approach to flexible rotor balancing. J. Sound Vib. 68 (1980), 489–506.
255. [255] Qu, L., Rotor balancing based on holospectrum analysis: principle and practice. China Mech. Eng. 9 (1998), 60–63.
256. [256] Liao, Y., Lang, G., Wu, F., Qu, L., An Improvement to Holospectrum based field balancing method by reselection of balancing object. J. Vib. Acoust. Trans. ASME 131 (2009), 987–1002.
257. [257] Vegte, J.V., Continuous automatic balancing of rotating systems. J. Mech. Eng. Sci. 6 (1964), 264–269.
258. [258] Vegte, J.V.D., Lake, R.T., Balancing of rotating systems during operation. J. Sound Vib. 57 (1978), 225–235.
259. [259] Zhou, S., Shi, J., Active balancing and vibration control of rotating machinery: a survey. Shock Vib. Dig. 33 (2001), 361–371.
260. [260] Knospe, C.R., Tamer, S.M., Experiments in robust control of rotor unbalance response using magnetic bearings. Mechatronics 7 (1997), 217–229.
261. [261] Zeng, S., Wang, X.X., The electromagnetic balancing regulator and the automatic balancing system. J. Sound Vib. 209 (1998), 5–13.
262. [262] Dyer, S.W., Shi, J., Ni, J., Shin, K.-K., Robust optimal influence-coefficient control of multiple-plane active rotor balancing systems. J. Dyn. Syst. Meas. Control 124 (2002), 41–46.
263. [263] Zhou, S., Shi, J., Optimal one-plane active balancing of a rigid rotor during acceleration. J. Sound Vib. 249 (2002), 196–205.
264. [264] Moon, J.D., Kim, B.S., Lee, S.H., Development of the active balancing device for high-speed spindle system using influence coefficients. Int. J. Mach. Tools Manuf. 46 (2006), 978–987.
265. [265] Fan, H., Jing, M., Wang, R., Liu, H., Zhi, J., New electromagnetic ring balancer for active imbalance compensation of rotating machinery. J. Sound Vib. 333 (2014), 3837–3858.
266. [266] Active balancing:Ring Balancer AB 9000, in, 〈http://www.hofmann-balancing.com/products/active-balancing-systems/ring-balancer-ab-9000.html〉.
267. [267] D. Birkenstack, O. Jager, Multi-chambered fluid balancing apparatus, in, US, 1976.
268. [268] J. Gao, P. Zhang, Simulative study of automatic balancing of grinding wheel using a continuously-dripping liquid-injection balancing head, in: Proceedings of the Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, 2006, pp. 8002–8005.
269. [269] Active balancing: HydroBalancer HB 6000, in, 〈http://www.hofmann-balancing.com/products/active-balancing-systems/hydrobalancer-hb-6000.html〉.
270. [270] Xi, S.T., Cao, H.R., Chen, X.F., Zhang, X.W., Jin, X.L., A frequency-shift synchrosqueezing method for instantaneous speed estimation of rotating machinery. J. Manuf. Sci. Eng. Trans. ASME, 137, 2015.
271. [271] Neugebauer, R., Fischer, J., Praedicow, M., Condition-based preventive maintenance of main spindles. Prod. Eng. 5 (2011), 95–102.
272. [272] Hoshi, T., Damage monitoring of ball bearing. CIRP Ann. Manuf. Technol. 55 (2006), 427–430.
273. [273] Tan, J., Chen, X., Wang, J., Chen, H., Cao, H., Zi, Y., He, Z., Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis. Mech. Syst. Signal Process. 23 (2009), 811–822.
274. [274] Yan, R., Gao, R.X., Wavelet domain principal feature analysis for spindle health diagnosis. Struct. Health Monit. Int. J. 10 (2011), 631–642.
275. [275] Hsieh, N.-K., Lin, W.-Y., Young, H.-T., High-speed spindle fault diagnosis with the empirical mode decomposition and multiscale entropy method. Entropy 17 (2015), 2170–2183.
276. [276] Niu, L.K., Cao, H.R., He, Z.J., Li, Y.M., Dynamic modeling and vibration response simulation for high speed rolling ball bearings with localized surface defects in raceways. J. Manuf. Sci. Eng. Trans. ASME, 136, 2014.
277. [277] Niu, L.K., Cao, H.R., He, Z.J., Li, Y.M., A systematic study of ball passing frequencies based on dynamic modeling of rolling ball bearings with localized surface defects. J. Sound Vib. 357 (2015), 207–232.
278. [278] De Castelbajac, C., Ritou, M., Laporte, S., Furet, B., Monitoring of distributed defects on HSM spindle bearings. Appl. Acoust. 77 (2014), 159–168.
279. [279] Vogl, G.W., Donmez, M.A., A defect-driven diagnostic method for machine tool spindles. CIRP Ann. Manuf. Technol. 64 (2015), 377–380.
280. [280] Holm-Hansen, B.T., Gao, R.X., Vibration analysis of a sensor-integrated ball bearing. J. Vib. Acoust. Trans. ASME 122 (2000), 384–392.
281. [281] Soylemezoglu, A., Jagannathan, S., Saygin, C., Mahalanobis taguchi system (MTS) as a prognostics tool for rolling element bearing failures. J. Manuf. Sci. Eng. Trans. ASME, 132, 2010, 051014.
282. [282] Jiang, S.Y., Mao, H.B., Investigation of variable optimum preload for a machine tool spindle. Int. J. Mach. Tools Manuf. 50 (2010), 19–28.
283. [283] Cao, H., Holkup, T., Altintas, Y., A comparative study on the dynamics of high speed spindles with respect to different preload mechanisms. Int. J. Adv. Manuf. Technol. 57 (2011), 871–883.
284. [284] Hwang, Y.-K., Lee, C.-M., A review on the preload technology of the rolling bearing for the spindle of machine tools. Int. J. Precis. Eng. Manuf. 11 (2010), 491–498.
285. [285] Tu, J.F., Stein, J.L., On-line preload monitoring for anti-friction spindle beatings of high-speed machine tools. J. Dyn. Syst. Meas. Control Trans. ASME 117 (1995), 43–53.
286. [286] Hwang, Y.-K., Park, I.-H., Paik, K.-S., Lee, C.-M., Development of a variable preload spindle by using an electromagnetic actuator. Int. J. Precis. Eng. Manuf. 15 (2014), 201–207.
287. [287] T. Tsuneyoshi, Spindle preload measurement and analysis, in: Proceedings of the 2007 ASPE Summer Topical Meeting, State College, PA, 2007, pp. 35–38.
288. [288] Law, L.-S., Kim, J.H., Liew, W.Y.H., Lee, S.-K., An approach based on wavelet packet decomposition and HilbertHuang transform (WPDHHT) for spindle bearings condition monitoring. Mech. Syst. Signal Process. 33 (2012), 197–211.
289. [289] Law, L.-S., Kim, J., Liew, W., Lee, S.-K., An approach to monitoring the thermomechanical behavior of a spindle bearing system using acoustic emission (AE) energy. Int. J. Precis. Eng. Manuf. 14 (2013), 1169–1175.
290. [290] Q. Li, Z. Pi, Research on spindle bearings state recognition of CNC milling machine based on noise monitoring, in: Proceedings of the 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5–7, 2011, IEEE Computer Society, Zhangjiajie, Hunan, China, 2011, pp. 1019–1021.
291. [291] F. Aschauer, S. Bonerz, Condition monitoring in motor spindle systems, in: Proceedings of the 4th International Chemnitz Manufacturing Colloquium, Chemnitz 2016, pp. 219–230.
292. [292] Slatter, R., Holland, L., Abele, E., Magnetoresistive sensors for the condition monitoring of high-frequency spindles. Procedia CIRP 46 (2016), 177–180.
293. [293] Mehta, P., Werner, A., Mears, L., Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion. J. Intell. Manuf. 26 (2015), 331–346.
294. [294] Saravanan, S., Yadava, G.S., Rao, P.V., Condition monitoring studies on spindle bearing of a lathe. Int. J. Adv. Manuf. Technol. 28 (2006), 993–1005.
295. [295] Yao, X.-H., Fu, J.-Z., Chen, Z.-C., Intelligent fault diagnosis using rough set method and evidence theory for NC machine tools. Int. J. Comput. Integr. Manuf. 22 (2009), 472–482.
296. [296] Y. Xue, H. Wang, X. Luo, Q. He, Monitor system design for machine electric spindle based on MCGS, in: Proceedings of the 2010 WASE International Conference on Information Engineering, ICIE 2010, August 14–15, 2010, IEEE Computer Society, Beidaihe, Hebei, China, 2010, pp. 248–252.
297. [297] Dong, X., Zhang, W., Degradation analysis of grinding machine spindle systems based on complexity. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 229 (2015), 1467–1471.
298. [298] Liu, Z.W., Cao, H.R., Chen, X.F., He, Z.J., Shen, Z.J., Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing 99 (2013), 399–410.
299. [299] Holm-Hansen, B.T., Gao, R.X., Zhang, L., Customized wavelet for bearing defect detection. J. Dyn. Syst. Meas. Control Trans. ASME 126 (2005), 740–745.
300. [300] Zhang, C.L., Li, B., Chen, B.Q., Cao, H.R., Zi, Y.Y., He, Z.J., Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. Mech. Syst. Signal Process. 64–65 (2015), 162–187.
301. [301] Bediaga, I., Mendizabal, X., Etxaniz, I., Munoa, J., An integrated system for machine tool spindle head ball bearing fault detection and diagnosis. IEEE Instrum. Meas. Mag. 16 (2013), 42–47.
302. [302] R.X. Gao, R. Yan, L. Zhang, K.B. Lee, Condition monitoring of operating spindle based on stochastic subspace identification, in: Proceedings of the ASME 2007 International Mechanical Engineering Congress and Exposition, United States, 2007, pp. 1129–1135.
303. [303] Yan, R., Gao, R.X., Zhang, L., In-process modal parameter identification for spindle health monitoring. Mechatronics 31 (2015), 42–49.
304. [304] L. Zhang, R. Yan, R.X. Gao, K. Lee, Design of a real-time spindle health monitoring and diagnosis system based on open systems architecture, in: Proceedings of the International Smart Machining Systems Conference, France, 2007, pp. 373–378.
305. [305] Katter, J.G., Tu, J.F., Bearing condition monitoring for preventive maintenance in a production environment. Tribol. Trans. 39 (1996), 936–942.
306. [306] Tu, J.F., Stein, J.L., Active thermal preload regulation for machine tool spindles with rolling element bearings. J. Manuf. Sci. Eng. Trans. ASME 118 (1996), 499–505.
307. [307] Chen, J.-S., Chen, K.-W., Bearing load analysis and control of a motorized high speed spindle. Int. J. Mach. Tools Manuf. 45 (2005), 1487–1493.
308. [308] Hwang, Y.K., Lee, C.M., Development of a newly structured variable preload control device for a spindle rolling bearing by using an electromagnet. Int. J. Mach. Tools Manuf. 50 (2010), 253–259.
309. [309] Schmitz, T.L., Donalson, R.R., Predicting high-speed machining dynamics by substructure analysis. CIRP Ann. Manuf. Technol. 49 (2000), 303–308.
310. [310] V. Gagnol, B.C. Bouzgarrou, P. Ray, C. Barra, Modelling approach for a high speed machine tool spindle-bearing system, in: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 1, Pts A–C, 2005, pp. 305–313.
311. [311] Budak, E., Erturk, A., Ozguven, H.N., A modeling approach for analysis and improvement of spindle-holder-tool assembly dynamics. CIRP Ann. Manuf. Technol. 55 (2006), 369–372.
312. [312] Erturk, A., Ozguven, H.N., Budak, E., Analytical modeling of spindle-tool dynamics on machine tools using Timoshenko beam model and receptance coupling for the prediction of tool point FRF. Int. J. Mach. Tools Manuf. 46 (2006), 1901–1912.
313. [313] Namazi, M., Altintas, Y., Abe, T., Rajapakse, N., Modeling and identification of tool holder-spindle interface dynamics. Int. J. Mach. Tools Manuf. 47 (2007), 1333–1341.
314. [314] Schmitz, T.L., Powell, K., Won, D., Scott Duncan, G., Gregory Sawyer, W., Ziegert, J.C., Shrink fit tool holder connection stiffness/damping modeling for frequency response prediction in milling. Int. J. Mach. Tools Manuf. 47 (2007), 1368–1380.
315. [315] An, C.H., Zhang, Y., Xu, Q., Zhang, F.H., Zhang, J.F., Zhang, L.J., Wang, J.H., Modeling of dynamic characteristic of the aerostatic bearing spindle in an ultra-precision fly cutting machine. Int. J. Mach. Tools Manuf. 50 (2010), 374–385.
316. [316] Jiang, S., Zheng, S., A modeling approach for analysis and improvement of spindle-drawbar-bearing assembly dynamics. Int. J. Mach. Tools Manuf. 50 (2010), 131–142.
317. [317] Wang, C.-C., Yau, H.-T., Theoretical analysis of high speed spindle air bearings by a hybrid numerical method. Appl. Math. Comput. 217 (2010), 2084–2096.
318. [318] Cao, H., Holkup, T., Chen, X., He, Z., Study on characteristic variations of high-speed spindles induced by centrifugal expansion deformations. J. Vibroeng. 14 (2012), 1278–1291.
319. [319] Cao, H., Li, B., He, Z., Finite element model updating of machine-tool spindle systems. J. Vib. Acoust. Trans. ASME, 135, 2013, 024503.
320. [320] Chen, X.-A., Liu, J.-F., He, Y., Zhang, P., Shan, W.-T., An integrated model for high-speed motorized spindles – dynamic behaviors. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 227 (2013), 2467–2478.
321. [321] Li, Y.M., Cao, H.R., Niu, L.K., Jin, X.L., A General method for the dynamic modeling of ball bearing-rotor systems. J. Manuf. Sci. Eng. Trans. ASME, 137, 2015.
322. [322] H.R. Cao, S.T. Xi, W. Cheng, Model updating of spindle systems based on the identification of joint dynamics, Shock Vib., 2015.
323. [323] Cao, H.R., Li, Y.M., Chen, X.F., A new dynamic model of ball-bearing rotor systems based on rigid body element. J. Manuf. Sci. Eng. Trans. ASME, 138, 2016.
324. [324] Bossmanns, B., Tu, J.F., Thermal model for high speed motorized spindles. Int. J. Mach. Tools Manuf. 39 (1999), 1345–1366.
325. [325] J.L. Stein, J.E. Harder Iii, Modeling and analysis for thermal control of spindles for reconfigurable machines, in: Proceedings of the 2001 ASME International Mechanical Engineering Congress and Exposition, November 11–16, 2001, American Society of Mechanical Engineers, New York, NY, United States, 2002, pp. 675–683.
326. [326] Creighton, E., Honegger, A., Tulsian, A., Mukhopadhyay, D., Analysis of thermal errors in a high-speed micro-milling spindle. Int. J. Mach. Tools Manuf. 50 (2010), 386–393.
327. [327] Chen, D., Bonis, M., Zhang, F., Dong, S., Thermal error of a hydrostatic spindle. Precis. Eng. 35 (2011), 512–520.
328. [328] Kim, S.-M., Lee, S.-K., Prediction of thermo-elastic behavior in a spindle-bearing system considering bearing surroundings. Int. J. Mach. Tools Manuf. 41 (2001), 809–831.
329. [329] Holkup, T., Cao, H., Kolar, P., Altintas, Y., Zeleny, J., Thermo-mechanical model of spindles. CIRP Ann. Manuf. Technol. 59 (2010), 365–368.
330. [330] Faassen, R.P.H., van de Wouw, N., Oosterling, J.A.J., Nijmeijer, H., Prediction of regenerative chatter by modelling and analysis of high-speed milling. Int. J. Mach. Tools Manuf. 43 (2003), 1437–1446.
331. [331] Cao, Y., Altintas, Y., Modeling of spindle-bearing and machine tool systems for virtual simulation of milling operations. Int. J. Mach. Tools Manuf. 47 (2007), 1342–1350.
332. [332] Erturk, A., Ozguven, H.N., Budak, E., Effect analysis of bearing and interface dynamics on tool point FRF for chatter stability in machine tools by using a new analytical model for spindle-tool assemblies. Int. J. Mach. Tools Manuf. 47 (2007), 23–32.
333. [333] Gagnol, V., Bouzgarrou, B.C., Ray, P., Barra, C., Model-based chatter stability prediction for high-speed spindles. Int. J. Mach. Tools Manuf. 47 (2007), 1176–1186.
334. [334] Gourc, E., Seguy, S., Arnaud, L., Chatter milling modeling of active magnetic bearing spindle in high-speed domain. Int. J. Mach. Tools Manuf. 51 (2011), 928–936.
335. [335] Cao, H., Li, B., He, Z., Chatter stability of milling with speed-varying dynamics of spindles. Int. J. Mach. Tools Manuf. 52 (2012), 50–58.
336. [336] Ozsahin, O., Budak, E., Ozguven, H.N., Identification of bearing dynamics under operational conditions for chatter stability prediction in high speed machining operations. Precis. Eng. J. Int. Soc. Precis. Eng. Nanotechnol. 42 (2015), 53–65.
337. [337] Cao, H.R., Zhou, K., Chen, X.F., Stability-based selection of cutting parameters to increase material removal rate in high-speed machining process. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 230 (2016), 227–240.
338. [338] Altintas, Y., Cao, Y., Virtual design and optimization of machine tool spindles. CIRP Ann. Manuf. Technol. 54 (2005), 379–382.
339. [339] Kim, S.M., Lee, S.K., Spindle housing design parameter optimization considering thermo-elastic behaviour. Int. J. Adv. Manuf. Technol. 25 (2005), 1061–1070.
340. [340] Maeda, O., Cao, Y., Altintas, Y., Expert spindle design system. Int. J. Mach. Tools Manuf. 45 (2005), 537–548.
341. [341] Ertürk, A., Budak, E., Özgüven, H.N., Selection of design and operational parameters in spindle–holder–tool assemblies for maximum chatter stability by using a new analytical model. Int. J. Mach. Tools Manuf. 47 (2007), 1401–1409.
342. [342] Gagnol, V., Bouzgarrou, B.C., Ray, P., Barra, C., Dynamic analyses and design optimization of high-speed spindle-bearing system. Adv. Integr. Des. Manuf. Mech. Eng. II, 2007, 505–518.
343. [343] Gagnol, V., Bouzgarrou, B.G., Ray, P., Barra, C., Stability-based spindle design optimization. J. Manuf. Sci. Eng. Trans. ASME 129 (2007), 407–415.
344. [344] Lin, C.-W., Tu, J.F., Model-based design of motorized spindle systems to improve dynamic performance at high speeds. J. Manuf. Process. 9 (2007), 94–108.
345. [345] Liang, Y., Chen, W., Sun, Y., Yu, N., Zhang, P., Liu, H., An expert system for hydro/aero-static spindle design used in ultra precision machine tool. Robot. Comput. Integr. Manuf. 30 (2014), 107–113.
346. [346] Liu, J., Chen, X., Dynamic design for motorized spindles based on an integrated model. Int. J. Adv. Manuf. Technol. 71 (2014), 1961–1974.
347. [347] Lin, C.-W., Lin, Y.-K., Chu, C.-H., Dynamic models and design of spindle-bearing systems of machine tools: a review. Int. J. Precis. Eng. Manuf. 14 (2013), 513–521.
348. [348] Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., Ueda, K., Cyber-physical systems in manufacturing. CIRP Ann. Manuf. Technol. 65 (2016), 621–641.
349. [349] Neugebauer, R., Denkena, B., Wegener, K., Mechatronic systems for machine tools. CIRP Ann. Manuf. Technol. 56 (2007), 657–686.
350. [350] Park, G., Bement, M.T., Hartman, D.A., Smith, R.E., Farrar, C.R., The use of active materials for machining processes: a review. Int. J. Mach. Tools Manuf. 47 (2007), 2189–2206.
351. [351] Möhring, H.-C., Brecher, C., Abele, E., Fleischer, J., Bleicher, F., Materials in machine tool structures. CIRP Ann. Manuf. Technol. 64 (2015), 725–748.
352. [352] Drossel, W.G., Bucht, A., Pagel, K., Mäder, T., Junker, T., Adaptronic applications in cutting machines. Procedia CIRP 46 (2016), 303–306.
353. [353] Vicente Abellan-Nebot, J., Subiron, F. Romero, A review of machining monitoring systems based on artificial intelligence process models. Int. J. Adv. Manuf. Technol. 47 (2010), 237–257.
354. [354] Jardine, A.K.S., Lin, D., Banjevic, D., A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20 (2006), 1483–1510.
355. [355] Goyal, D., Pabla, B.S., Condition based maintenance of machine tools– a review. CIRP J. Manuf. Sci. Technol. 10 (2015), 24–35.
356. [356] W. Guo, H.R. Cao, Z.J. He, L.H. Yang, Fatigue life analysis of rolling bearings based on quasistatic modeling, Shock Vib., 2015.
357. [357] Gao, R., Wang, L., Teti, R., Dornfeld, D., Kumara, S., Mori, M., Helu, M., Cloud-enabled prognosis for manufacturing. CIRP Ann. Manuf. Technol. 64 (2015), 749–772.
358. [358] A. Vijayaraghavan, W. Sobel, A. Fox, D. Dornfeld, P. Warndorf, Improving machine tool interoperability using standardized interface protocols: MTConnect, in: Proceedings of the 2008 International Symposium on Flexible Automation (ISFA), Atlanta, GA, USA., 2008, pp. 1–6.