Abstract
Computer numerical control (CNC) machine tools, as advanced manufacturing equipment, have been widely used in modern manufacturing. The machining accuracy acting as a main index of performance has long been more concerned by the engineering and academic community. However, in practice, it is more important that the CNC machine tools should have the ability to maintain their original accuracy or keep the accuracy within an acceptable range under complex working conditions after long-term service. This paper presents an overview of the research progress on the accuracy decline mechanism and the accuracy retention approaches of the CNC machine tools. First, the leading theory of accuracy decline and the mechanism of accuracy decline, such as moving components, residual stress and deformation, bolt creep and looseness, interface morphology, and ambient temperature, are described in detail. The accuracy decline is evaluated well. Then, various retention accuracy approaches are comprehensively reviewed, and the evaluation of accuracy retention is introduced. Finally, the challenges and opportunities for industry and academia are discussed, and several principle conclusions are drawn.
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The authors declare that the data and material used or analyzed in the present study can be obtained from the corresponding author at reasonable request.
References
Zhang S, To S, Zhang G, Zhu Z (2015) A review of machine-tool vibration and its influence upon surface generation in ultra-precision machining. Int J Mach Tools Manuf 91:34–42. https://doi.org/10.1016/j.ijmachtools.2015.01.005
Sartori S, Zhang G (1995) Geometric error measurement and compensation of machines. CIRP Ann 44(2):599–609. https://doi.org/10.1016/s0007-8506(07)60507-1
Fan K, Chen H, Kuo T (2012) Prediction of machining accuracy degradation of machine tools. Precis Eng 36(2):288–298. https://doi.org/10.1016/j.precisioneng.2011.11.002
Fu G, Fu J, Xu Y, Chen Z, Lai J (2014) Accuracy enhancement of five-axis machine tool based on differential motion matrix: geometric error modeling, identification and compensation. Int J Mach Tools Manuf 89:170–181. https://doi.org/10.1016/j.ijmachtools.2014.11.005
Liu Y, Guo L, Gao H, You Z, Ye Y, Zhang B (2022) Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: a review. Mech Syst Signal Process 164:108068. https://doi.org/10.1016/j.ymssp.2021.108068
Roylance B (2003) Machine failure and its avoidance—what is tribology’s contribution to effective maintenance of critical machinery? P I Mech Eng J-J Eng 217:349–364. https://doi.org/10.1243/135065003322445278
Ding W, Huang X, Zhu S, Wang M (2012) Research progress on accuracy failure of CNC machine tools (Chinese). Machine tool & hydraulics 15:150–153. https://doi.org/10.3969/j.issn.1001-3881.2012.15.039
Gorjian N, Ma L, Mittinty M, Yarlagadda P, Sun Y (2009) A review on degradation models in reliability analysis. Proceedings of the 4th World Congress on Engineering Asset Management 42:369–384. https://doi.org/10.1007/978-0-85729-320-6_42
Liu S, Zhang X (2014) Latest development of accuracy recession of numerically-controlled machine tools. Adv Mat Res 971–973:565–568. https://doi.org/10.4028/www.scientific.net/AMR.971-973.565
Zhao B, Yang P, Chen K, Gao J (2011) Research on life of machine accuracy and precision reliability assessment concerning about a turn-milling combined machine tool. 2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering 2011:168–173. https://doi.org/10.1109/ICQR2MSE.2011.5976589
Zhang G, Huang Y, Shi W, Fu W (2003) Predicting dynamic behaviours of a whole machine tool structure based on computer-aided engineering. Int J Mach Tools Manuf 43:699–706. https://doi.org/10.1016/s0890-6955(03)00026-9
Chlebus E, Dybala B (1999) Modelling and calculation of properties of sliding guideways. Int J Mach Tools Manuf 39:1823–1839. https://doi.org/10.1016/S0890-6955(99)00041-3
Ma J, Zhao W, Zhang G (2015) Research status and analyses on accuracy retentivity of domestic CNC machine tools (Chinese). China mechanical engineering 26(022):3108–3115. https://doi.org/10.3969/j.issn.1004-132X.2015.22.020
Meng H, Ludema K (1995) Wear models and predictive equations: their form and content. Wear 181–183:443–457. https://doi.org/10.1016/0043-1648(95)90158-2
Ludema K (1996) Mechanism-based modeling of friction and wear. Wear 200:1–7. https://doi.org/10.1016/S0043-1648(96)07312-7
Jones A (1960) A general theory for elastically constrained ball and radial roller bearings under arbitrary load and speed conditions. J Basic Eng 82(2):309. https://doi.org/10.1115/1.3662587
Hertz H (1882) On the contact of rigid elastic solids and on hardness. In: Hertz H (ed) Reprinted from Assorted papers, Chap 6, MacMillan, pp 163–183
Harris T, Mindel M (1973) Rolling element bearing dynamics. Wear 23(3):311–337. https://doi.org/10.1016/0043-1648(73)90020-3
Lin M (1989) Design and mechanics of the ball screw mechanism. Dissertation, University of Wisconsin Madison
Cao H, Niu L, Xi S, Chen X (2018) Mechanical model development of rolling bearing-rotor systems: a review. Mech Syst Signal Process 102:37–58. https://doi.org/10.1016/j.ymssp.2017.09.023
Sahin Y (2005) The prediction of wear resistance model for the metal matrix composites. Wear 258(11–12):1717–1722. https://doi.org/10.1016/j.wear.2004.11.024
Tan Y, Zhang L, Hu Y (2014) A wear model of plane sliding pairs based on fatigue contact analysis of asperities. Tribol T 58(1):148–157. https://doi.org/10.1080/10402004.2014.956907
Lim S (1998) Recent developments in wear-mechanism maps. Tribol Int 31(1–3):87–97. https://doi.org/10.1016/S0301-679X(98)00011-5
Burwell J, Strang C (1952) On the empirical law of adhesive wear. J Appl Phys 23(1):18–28. https://doi.org/10.1063/1.1701970
Archard J (1953) Contact and rubbing of flat surfaces. J Appl Phys 24(8):981–988. https://doi.org/10.1063/1.1721448
Archard J, Hirst W (1956) The wear of metals under unlubricated conditions. Proc R Soc Lond A 236(1206):397–410. https://doi.org/10.1098/rspa.1956.0144
Hu Y, Wang C, Yang R, Tang Y (2014) The attenuation model of sliding guides wear theory based on Archard wear model. IEEE Int Conf Mechatron Autom 2014:1570–1574. https://doi.org/10.1109/ICMA.2014.6885934
Zhao B, Zhang S, Li J, Wang P (2016) Friction characteristics of sliding guideway material considering original surface functional parameters under hydrodynamic lubrication. P I Mech Eng J-J Eng 231(7):813–825. https://doi.org/10.1177/1350650116681941
Ekinci T, Mayer J (2007) Relationships between straightness and angular kinematic errors in machines. Int J Mach Tools Manuf 47:1997–2004. https://doi.org/10.1016/j.ijmachtools.2007.02.002
Fan J, Wang X, Li Y (2012) Research on analytical method of CNC machine tools precision recession. Appl Mech Mater 159:213–217. https://doi.org/10.4028/www.scientific.net/AMM.159.213
Tan Y, Zhang L, Wang K, Hu Y (2015) Modeling of precision retaining ability for slide guide of machine tool based on wear analysis (Chinese). T Chin Soc Agric Mach 46(2):351–356. https://doi.org/10.6041/j.issn.1000-1298.2015.02.052
Zhou S, Sun B (2017) Parameter identification and optimization of slide guide joint of CNC machine tools. IOP Conference Series: Mater Sci Eng 265:012025. https://doi.org/10.1088/1757-899x/265/1/012025
Xu H, He T, Zhong N, Zhao B, Liu Z (2022) Transient thermomechanical analysis of micro cylindrical asperity sliding contact of SnSbCu alloy. Tribol Int 167:107362. https://doi.org/10.1016/j.triboint.2021.107362
Bilkay O, Anlagan O (2004) Computer simulation of stick-slip motion in machine tool slideways. Tribol Int 37:347–351. https://doi.org/10.1016/j.triboint.2003.11.006
Kim G, Han J, Lee S (2014) Motion error estimation of slide table on the consideration of guide parallelism and pad deflection. Int J Precis Eng Manuf 15(9):1935–1946. https://doi.org/10.1007/s12541-014-0548-x
Ma Y, Ye Z, Sun S (2013) Shen H (2013) Research on slideways characteristics based on machine tool stiffness (Chinese). Modular Machine Tool & Automatic Manufacturing Technique 11:1–4. https://doi.org/10.3969/j.issn.1001-2265.2013.11.001
Zhu L, Li L, Liu J, Zhang Z (2009) A method for measuring the guideway straightness error based on polarized interference principle. Int J Mach Tools Manuf 49:285–290. https://doi.org/10.1016/j.ijmachtools.2008.10.009
Tang H, Duan J, Zhao Q (2017) A systematic approach on analyzing the relationship between straightness & angular errors and guideway surface in precise linear stage. IntJ Mach Tools Manuf 120:12–19. https://doi.org/10.1016/j.ijmachtools.2017.04.010
Mahdi R, Stephan K, Friedrich B (2015) Experimental investigations on stick-slip phenomenon and friction characteristics of linear guides. Precis Eng 100:1023–1031. https://doi.org/10.1016/j.proeng.2015.01.462
Chang J, Wu J, Hung J (2007) Characterization of the dynamic behavior of a linear guideway mechanism. Struct Eng Mech 25(1):1–20. https://doi.org/10.12989/sem.2007.25.1.001
Huang B, Gao H, Xu M, Wu X, Zhao M (2010) Guo L (2010) Life prediction of CNC linear rolling guide based on DFNN performance degradation model. Seventh International Conference on Fuzzy Systems and Knowledge Discovery FSKD 3:1310–1314. https://doi.org/10.1109/FSKD.2010.5569106
Vogl G, Jameson N, Archenti A, Szipka K, Donmez M (2019) Root-cause analysis of wear-induced error motion changes of machine tool linear axes. Int J Mach Tools Manuf 143:38–48. https://doi.org/10.1016/j.ijmachtools.2019.05.004
Lin C, Hung J, Lo T (2010) Effect of preload of linear guides on dynamic characteristics of a vertical column–spindle system. Int J Mach Tools Manuf 50:741–746. https://doi.org/10.1016/j.ijmachtools.2010.04.002
Hung J, Lai Y, Lin C, Lo T (2011) Modeling the machining stability of a vertical milling machine under the influence of the preloaded linear guide. Int J Mach Tools Manuf 51:731–739. https://doi.org/10.1016/j.ijmachtools.2011.05.002
Sun W, Kong X, Wang B, Li X (2014) Statics modeling and analysis of linear rolling guideway considering rolling balls contact. P I Mech Eng C-J Mec 229(1):168–179. https://doi.org/10.1177/0954406214531943
Ni Y, Zhou H, Shao C, Li J (2019) Research on the error averaging effect in a rolling guide pair. Chin J Mech Eng 32(1):1–12. https://doi.org/10.1186/s10033-019-0386-y
He G, Shi P, Guo L, Ding B (2020) A linear model for the machine tool assembly error prediction considering roller guide error and gravity-induced deformation. P I Mech Eng C-J Mec 234(15):2939–2950. https://doi.org/10.1177/0954406220911401
Rahmani M, Bleicher F (2016) Experimental and numerical studies of the influence of geometric deviations in the performance of machine tools linear guides. Procedia CIRP 41:818–823. https://doi.org/10.1016/j.procir.2015.08.089
Zhang L, Gao H, Dong D, Fu G, Liu Q (2018) Wear calculation-based degradation analysis and modeling for remaining useful life prediction of ball screw. Math Probl Eng 2018:1–18. https://doi.org/10.1155/2018/2969854
Wang Y, Guo Z, Liu B, Zhu Y, Luo H (2017) Investigation of ball screw’s alignment error based on dynamic modeling and magnitude analysis of worktable sensed vibration signals. Assem Autom 37(4):483–489. https://doi.org/10.1108/aa-08-2016-088
Cheng Q, Qi B, Liu Z, Zhang C, Xue D (2019) An accuracy degradation analysis of ball screw mechanism considering time-varying motion and loading working conditions. Mech Mach Theory 134:1–23. https://doi.org/10.1016/j.mechmachtheory.2018.12.024
Wen J, Gao H (2018) Degradation assessment for the ball screw with variational autoencoder and kernel density estimation. Adv Mech Eng 10(9):168781401879726. https://doi.org/10.1177/1687814018797261
Wen J, Gao H (2018) Remaining useful life prediction of the ball screw system based on weighted Mahalanobis distance and an exponential model. J Vibroeng 20(4):1691–1707. https://doi.org/10.21595/jve.2018.1909
Wei C, Liou W, Lai R (2012) Wear analysis of the offset type preloaded ball–screw operating at high speed. Wear 292–293:111–123. https://doi.org/10.1016/j.wear.2012.05.024
Wei C, Lin J (2003) Kinematic analysis of the ball screw mechanism considering variable contact angles and elastic deformations. J Mech Design 125(4):717–733. https://doi.org/10.1115/1.1623761
Wei C, Lin J, Horng J (2009) Analysis of a ball screw with a preload and lubrication. Tribol Int 42(11):1816–1831. https://doi.org/10.1016/j.triboint.2008.12.013
Li T, Yuan J, Zhang Y, Zhao C (2020) Time-varying reliability prediction modeling of positioning accuracy influenced by frictional heat of ball-screw systems for CNC machine tools. Precis Eng 64:147–156. https://doi.org/10.1016/j.precisioneng.2020.04.002
Abele E, Altintas Y, Brecher C (2010) Machine tool spindle units. CIRP Ann 59(2):781–802. https://doi.org/10.1016/j.cirp.2010.05.002
Lin C, Lin Y, Chu C (2013) Dynamic models and design of spindle-bearing systems of machine tools: a review. Int J Precis Eng Manuf 14(3):513–521. https://doi.org/10.1007/s12541-013-0070-6
Xi S, Cao H, Chen X, Niu L (2018) Dynamic modeling of machine tool spindle bearing system and model based diagnosis of bearing fault caused by collision. Procedia CIRP 77:614–617. https://doi.org/10.1016/j.procir.2018.08.197
Tu J, Katter J (1996) Bearing force monitoring in a three-shift production environment. Tribol T 39(1):201–207. https://doi.org/10.1080/10402009608983521
Sabirov F, Gilovoj L, Bogan A, Yakhutlov M, Nartyzhev R (2018) Application of CAE for modeling the parametric failure of the spindle due to rigidity by bearing wear. IEEE International Conference 2018:471–473. https://doi.org/10.1109/ITMQIS.2018.8524941
Zhang T, Chen X, Gu J, Wang Z (2018) Influences of preload on the friction and wear properties of high-speed instrument angular contact ball bearings. Chinese J Aeronaut 31(3):597–607. https://doi.org/10.1016/j.cja.2017.07.006
Ngo T, Than V, Wang C, Huang J (2017) Analyzing characteristics of high-speed spindle bearing under constant preload. P I Mech Eng J-J Eng 232(5):568–581. https://doi.org/10.1177/1350650117723484
Zhang L, Xuan J (2020) Qualitative research on the relationship between spindle vibration characteristics and bearing thermal load. J Phys: Int Conf Phys Mech Mathematic Sci 1707(1):012014. https://doi.org/10.1088/1742-6596/1707/1/012014
Spiewak S, Nickel T (2001) Vibration based preload estimation in machine tool spindles. Int J Mach Tools Manuf 41(4):567–588. https://doi.org/10.1016/S0890-6955(00)00081-X
Raja V, Thyla P, Radhakrishnan P (2006) A strategy of investigation on the thermal behaviour of motorised spindles under high-speed machining. Int J Comput Appl Technol 27(1):1–11. https://doi.org/10.1504/IJCAT.2006.010984
Zivkovic A, Knezev M, Zeljkovic M, Navalusic S, Beju L (2019) A study of thermo-elastic characteristics of the machine tool spindle. MATEC Web of Conferences 290:1009. https://doi.org/10.1051/matecconf/201929001009
Ma W, Mi J, Yang Q (2016) Research on positioning accuracy retentivity of machining center based on residual stress (Chinese). China mechanical engineering 27(017):2293–2297. https://doi.org/10.3969/j.issn.1004-132X.2016.17.004
Hu M, Yu C, Zhao W, Cun H, Yuan S, Zhang W (2014) Optimization method of VSR process parameters for large machine tool body (Chinese). China Mechanic Eng 25(23):3137–3142. https://doi.org/10.3969/j.issn.1004-132X.2014.23.003
Palumbo G, Piccininni A, Piglionico V, Guglielmi P, Sorgente D, Tricarico L (2015) Modelling residual stresses in sand-cast superduplex stainless steel. J Mater Process Tech 217:253–261. https://doi.org/10.1016/j.jmatprotec.2014.11.006
Capello E, Davoli P, Bassanini G, Bisi A (1999) Residual stresses and surface roughness in turning. J Eng Mater Technol 21(3):346–351. https://doi.org/10.1115/1.2812385
Ullah I, Zhang S, Zhang Q, Wang R (2021) Numerical investigation on serrated chip formation during high-speed milling of ti-6Al-4V alloy. J Mater Process 71:589–603. https://doi.org/10.1016/j.jmapro.2021.09.056
El-Axir M (2002) A method of modeling residual stress distribution in turning for different materials. Int J Mach Tools Manuf 42:1055–1063. https://doi.org/10.1016/S0890-6955(02)00031-7
Ulutan D, Arisoy Y, Özel T, Mears L (2014) Empirical modeling of residual stress profile in machining nickel-based superalloys using the sinusoidal decay function. Procedia CIRP 13:365–370. https://doi.org/10.1016/j.procir.2014.04.062
Arunachalam R, Mannan M, Spowage A (2004) Residual stress and surface roughness when facing age hardened inconel 718 with CBN and ceramic cutting tools. Int J Mach Tools Manuf 44:879–887. https://doi.org/10.1016/j.ijmachtools.2004.02.016
Sasahara H (2005) The effect on fatigue life of residual stress and surface hardness resulting from different cutting conditions of 0.45%C steel. Int J Mach Tools Manuf 45:131–136. https://doi.org/10.1016/j.ijmachtools.2004.08.002
Navas V, Gonzalo O, Bengoetxea I (2012) Effect of cutting parameters in the surface residual stresses generated by turning in AISI 4340 steel. Int J Mach Tools Manuf 61:48–57. https://doi.org/10.1016/j.ijmachtools.2012.05.008
Zhao Y, Xu J, Cai L, Shi W, Liu Z (2016) Stiffness and damping model of bolted joint based on the modified three-dimensional fractal topography. P I Mech Eng C-J Mec 231(2):279–293. https://doi.org/10.1177/0954406216631577
Ibrahim R, Pettit C (2005) Uncertainties and dynamic problems of bolted joints and other fasteners. J Sound Vib 279(3–5):857–936. https://doi.org/10.1016/j.jsv.2003.11.064
Zhao T, Hu J (2016) A study on the nonlinear vibration of bolted joints in CNC machine tools. 2016 International Conference on Cybernetics, Robotics and Control (CRC) 2016:48–54. https://doi.org/10.1109/CRC.2016.020
Dong Y, Hess D (2000) Shock-induced loosening of dimensionally non-conforming threaded fasteners. J Sound Vib 231(2):451–459. https://doi.org/10.1006/jsvi.1999.2635
Pai N, Hess D (2002) Three-dimensional finite element analysis of threaded fastener loosening due to dynamic shear load. Eng Fail Anal 9(4):383–402. https://doi.org/10.1016/S1350-6307(01)00024-3
Li Y, Zhang G, Zhang Z, Wang P (2018) A modelling method of bolt joints based on basic characteristic parameters of joint surfaces. IOP Conference Series: Earth and Environmental Science 113:012050. https://doi.org/10.1088/1755-1315/113/1/012050
Liu H, Wu J, Liu K, Kuang K, Luo Q, Liu Z, Wang Y (2019) Pretightening sequence planning of anchor bolts based on structure uniform deformation for large CNC machine tools. Int J Mach Tools Manuf 136:1–18. https://doi.org/10.1016/j.ijmachtools.2018.09.002
Wang Y, Wu J, Liu H, Xu S (2016) Modeling and numerical analysis of multi-bolt elastic interaction with bolt stress relaxation. P I Mech Eng C-J Mec 230(15):2579–2587. https://doi.org/10.1177/0954406215615155
Hu M, Ma J, Jin T, Zhao W, Zhang W (2016) The optimization method of the layout of the anchor bolt of machine tools considering the variation of the moving load. Modul Mach Tool Autom Manufac Tech 1001–2265:0150–4. https://doi.org/10.13462/j.cnki.mmtamt.2016.04.040
Zhang Z, Xiao Y, Xie Y, Su Z (2019) Effects of contact between rough surfaces on the dynamic responses of bolted composite joints: multiscale modeling and numerical simulation. Compos Struct 211:13–23. https://doi.org/10.1016/j.compstruct.2018.12.019
Zhao Y, Yang C, Cai L, Shi W, Liu Z (2016) Surface contact stress-based nonlinear virtual material method for dynamic analysis of bolted joint of machine tool. Precis Eng 43:230–240. https://doi.org/10.1016/j.precisioneng.2015.08.002
Jiang K, Liu Z, Yang C, Zhang C, Tian Y, Zhang T (2022) Effects of the joint surface considering asperity interaction on the bolted joint performance in the bolt tightening process. Tribol Int 167:107408. https://doi.org/10.1016/j.triboint.2021.107408
Mi L, Yin G, Sun M, Wang X (2012) Effects of preloads on joints on dynamic stiffness of a whole machine tool structure. J Mech Sci Technol 26(2):495–508. https://doi.org/10.1007/s12206-011-1033-4
Alkelani A, Housari B, Nassar S (2008) A proposed model for creep relaxation of soft gaskets in bolted joints at room temperature. Press Vess-T ASME 130(1):011211. https://doi.org/10.1115/1.2826430
Delhomme F, Debicki G (2010) Numerical modelling of anchor bolts under pullout and relaxation tests. Constr Build Mater 24(7):1232–1238. https://doi.org/10.1016/j.conbuildmat.2009.12.015
Liu H, Wu j, Wang Y (2015) Impact of anchor bolts creep relaxation on geometric accuracy decline of large computer numerical control machine tool. Journal of xi’an jiaotong university 49(9):14–19. https://doi.org/10.7652/xjtuxb201509003
Xu H, Wang F, Cheng X (2007) Pullout creep properties of grouted soil anchors. J Cent South Univ 14(S1):474–477. https://doi.org/10.1007/s11771-007-0310-y
Todd M, Nichols J, Nichols C, Virgin L (2004) An assessment of modal property effectiveness in detecting bolted joint degradation: theory and experiment. J Sound Vib 275(3–5):1113–1126. https://doi.org/10.1016/j.jsv.2003.10.037
Zhang W, Liu X, Huang Z, Zhu J (2021) Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling. Adv Mech Eng 13(2):1–15. https://doi.org/10.1177/1687814021992181
Li S, Gao H, Liu Q, Liu B (2018) Dynamic modeling method of the bolted joint with uneven distribution of joint surface pressure. AIP Adv 8:35116. https://doi.org/10.1063/1.5021823
Ren Y, Beards C (1998) Identification of ‘Effective’ linear joints using coupling and joint identification techniques. J Vib Acoust 120(2):331–338. https://doi.org/10.1115/1.2893835
Greenwood J, Williamson J (1966) Contact of nominally flat surfaces. Proc R Soc Lond A 295(1442):300–319. https://doi.org/10.1098/rspa.1966.0242
Pullen J, Williamson J (1972) On the plastic contact of rough surfaces. Proc R Soc Lond A 327:159–173. https://doi.org/10.1098/rspa.1972.0038
Chang W, Etsion I, Bogy D (1987) An elastic-plastic model for the contact of rough surfaces. J Tribol 109(2):257–263. https://doi.org/10.1115/1.3261348
Xu Y, Zhang D (2016) Modeling and simulation of the equivalent material damping loss factor of fixed joint interface. Adv Mech Eng 8(8):1–9. https://doi.org/10.1177/1687814016665552
Tian Y, Liu Z, Dong X (2018) Bearing deformation of heavy-duty machine tool-foundation systems and deformation detection methods. P I Mech Eng C-J Mec 233(9):3232–3245. https://doi.org/10.1177/0954406218813396
Zhao Y, Maietta D, Chang L (2000) An asperity microcontact model incorporating the transition from elastic deformation to fully plastic flow. J Tribol 122(1):86–93. https://doi.org/10.1115/1.555332
Kucharski S, Klimczak T, Polijaniuk A, Kaczmarek J (1994) Finite-elements model for the contact of rough surfaces. Wear 177(1):1–13. https://doi.org/10.1016/0043-1648(94)90112-0
Yang D, Liu Z (2015) Surface plastic deformation and surface topography prediction in peripheral milling with variable pitch end mill. Int J Mach Tools Manuf 91:43–53. https://doi.org/10.1016/j.ijmachtools.2014.11.009
Bryan J (1990) International status of thermal error research. CIRP Ann 39(2):645–656. https://doi.org/10.1016/s0007-8506(07)63001-7
Mayr J, Jedrzejewski J, Uhlmann E, Donmez M, Knapp W, Härtig F, Wendt K, Moriwaki T, Shore P, Schmitt R, Brecher C, Würz T, Wegener K (2012) Thermal issues in machine tools. CIRP Ann 61(2):771–791. https://doi.org/10.1016/j.cirp.2012.05.008
Li Y, Zhao W, Lan S, Ni J, Wu W, Lu B (2015) A review on spindle thermal error compensation in machine tools. Int J Mach Tools Manuf 95:20–38. https://doi.org/10.1016/j.ijmachtools.2015.04.008
Ramesh R, Mannan M, Poo A (2000) Error compensation in machine tools—a review. Int J Mach Tools Manuf 40(9):1257–1284. https://doi.org/10.1016/S0890-6955(00)00010-9
Mian N, Fletcher S, Longstaff A, Myers A (2013) Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations. Precis Eng 37(2):372–379. https://doi.org/10.1016/j.precisioneng.2012.10.006
Tanabe I, Orimo T (2001) Countermeasure for reducing thermal deformation of a coordinate measuring machine caused by ambient temperature fluctuation: conservation of energy for protecting the earth. Transac Japan Soc Mechanic Eng C67(662):3357–3362. https://doi.org/10.1299/kikaic.67.3357
Luo F, Song D (2013) Study on the influence of ambient temperature on spindle box of the wheel groove milling machine based on ANSYS. Appl Mech Mater 475–476:1513–1516. https://doi.org/10.4028/www.scientific.net/AMM.475-476.1513
Fujishima M, Narimatsu K, Irino N, Ido Y (2018) Thermal displacement reduction and compensation of a turning center. CIPP J Manuf Sci Tec 22:111–115. https://doi.org/10.1016/j.cirpj.2018.04.003
Groos L, Held C, Keller F, Wendt K, Franke M, Gerwien N (2020) Mapping and compensation of geometric errors of a machine tool at different constant ambient temperatures. Precis Eng 63:10–17. https://doi.org/10.1016/j.precisioneng.2020.01.001
Kim B, Song Y, Park C (2011) Robust thermal error modeling and compensation for a nano level thermal drift in a high precision lathe. Int J Precis Eng Manuf 12(4):657–661. https://doi.org/10.1007/s12541-011-0085-9
Moriwaki T, Shamoto E (1998) Analysis of thermal deformation of an ultraprecision air spindle system. CIRP Ann 47(1):315–319. https://doi.org/10.1016/S0007-8506(07)62841-8
Tanabe I, Takada K (1994) Thermal deformation of machine tool structures using resin concrete (thermal behaviour of concrete bed of machine tool in fluctuating ambient temperature). JSME Int J C37(2):384–389. https://doi.org/10.1299/jsmec1993.37.384
Li F, Li T, Jiang Y, Wang H, Ehmann K (2019) Explicit error modeling of dynamic thermal errors of heavy machine tool frames caused by ambient temperature fluctuations. J Manuf Process 48:320–338. https://doi.org/10.1016/j.jmapro.2019.10.018
Пpoтникoв A, Li C, Yu L (1987) Precision and reliability of CNC machine tools (Chinese). China machine press, Beijing
Wang Y, Wu J, Liu K, Liu H, Liu Z, Lian M (2019) Quantitative evaluation and error sensitivity analysis of accuracy retentivity of CNC machine tools (Chinese). J Mech Eng 55(005):130–136. https://doi.org/10.3901/JME.2019.05.130
Guo S, Tang S, Zhang D (2019) A recognition methodology for the key geometric errors of a multi-axis machine tool based on accuracy retentivity analysis. Complexity 2019:1–21. https://doi.org/10.1155/2019/8649496
Wang Y, Wu J, Liu H, Kang K, Liu K (2018) Geometric accuracy long-term continuous monitoring using strain gauges for CNC machine tools. Int J Adv Manuf Tech 98(5–8):1145–1153. https://doi.org/10.1007/s00170-018-2337-7
Jone E, George J (2006) Aging, maintenance, and reliability-approaches to preserving equipment health and extending equipment life. IEEE Power Energy M 4(3):59–67. https://doi.org/10.1109/MPAE.2006.1632455
Lee W, Wu H, Yun H, Kim H, Jun M, Sutherland J (2019) Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP 80:506–511. https://doi.org/10.1016/j.procir.2018.12.019
Hou Y, Chen D, Zheng L (1985) Effect of surface topography of scraped machine tool guideways on their tribological behaviour. Tribol Int 18(2):125–129. https://doi.org/10.1016/0301-679X(85)90054-4
Moreno M, Ruiz J, Azpeitia D, González J, Fernández L (2020) Friction improvement via grinding wheel texturing by dressing. Int J Adv Manuf Tech 107(11–12):4939–4954. https://doi.org/10.1007/s00170-020-05350-6
Nallasamy P, Saravanakumar N, Nagendran S, Suriya E, Yashwant D (2014) Tribological investigations on Mos2-based nanolubricant for machine tool slideways. P I Mech Eng J-J Eng 229(5):559–567. https://doi.org/10.1177/1350650114556394
Schneider Y (1984) Formation of surfaces with uniform micropatterns on precision machine and instruments parts. Precis Eng 6(4):219–225. https://doi.org/10.1016/0141-6359(84)90007-2
Hsieh T, Jywe W, Huang H, Chen S (2011) Development of a laser-based measurement system for evaluation of the scraping workpiece quality. Opt Lasers Eng 49(8):1045–1053. https://doi.org/10.1016/j.optlaseng.2011.04.005
Serdobintsev Y, Kukhtik M, Kuach D (2021) A study of the influence of surface layer properties on the tribotechnical characteristics of plain machine tool slideways. 6th Int Conf Indus Eng 2021:1236–1245. https://doi.org/10.1007/978-3-030-54814-8
Yue H, Deng J, Ge D, Li X, Zhang Y (2019) Effect of surface texturing on tribological performance of sliding guideway under boundary lubrication. J Manuf Process 47:172–182. https://doi.org/10.1016/j.jmapro.2019.09.031
Fan J, Tao H, Wu C, Pan R, Tang Y, Li Z (2018) Kinematic errors prediction for multi-axis machine tools’ guideways based on tolerance. Int J Adv Manuf Tech 98(5):1131–1144. https://doi.org/10.1007/s00170-018-2335-9
Wang Y, Liu B, Guo Z (2017) Wear resistance of machine tools’ bionic linear rolling guides by laser cladding. Opt Lasers Eng 91:55–62. https://doi.org/10.1016/j.optlastec.2016.12.015
Han J, Feng H, Ou Y, Liang Y, Yin A, Zu L (2016) Measurement and control system design of precision retentivity of rolling linear guide pair. Int J Mater Mech Manuf 4(1):74–79. https://doi.org/10.7763/IJMMM.2016.V4.228
Tsai P, Cheng C, Cheng Y (2017) A novel method based on operational modal analysis for monitoring the preload degradation of linear guideways in machine tools. Mechanic Eng J 4(2):16–00480. https://doi.org/10.1299/mej.16-00480
Majda P (2012) Modeling of geometric errors of linear guideway and their influence on joint kinematic error in machine tools. Precis Eng 36(3):369–378. https://doi.org/10.1016/j.precisioneng.2012.02.001
Cheng D, Yang W, Park J, Park T, Kim S, Kim G, Park C (2014) Friction experiment of linear motion roller guide THK SRG25. Int J Precis Eng Manuf 15(3):545–551. https://doi.org/10.1007/s12541-014-0369-y
Syamsul H, Oiwa T, Tanaka T, Asama J (2014) Positioning error improvement based on ultrasonic oscillation for a linear motion rolling bearing during sinusoidal motion. Precis Eng 38(3):617–627. https://doi.org/10.1016/j.precisioneng.2014.02.012
Chang J, Chao J, Huang Y, Chen J (2010) Prognostic experiment for ball screw preload loss of machine tool through the Hilbert-Huang transform and multiscale entropy method. The 2010 IEEE International Conference on Information and Automation 2010:376–380. https://doi.org/10.1109/ICINFA.2010.5512064
Möhring H, Bertram O (2012) Integrated autonomous monitoring of ball screw drives. CIRP Ann 61(1):355–358. https://doi.org/10.1016/j.cirp.2012.03.138
Benker M, Kleinwort R, Zäh M (2019) Estimating remaining useful life of machine tool ball screws via probabilistic classification. IEEE International Conference on Prognostics and Health Management (ICPHM) 2019:1–7. https://doi.org/10.1109/ICPHM.2019.8819445
Tsai P, Cheng C, Hwang Y (2014) Ball screw preload loss detection using ball pass frequency. Mech Syst Signal Pr 48(1–2):77–91. https://doi.org/10.1016/j.ymssp.2014.02.017
Wen J, Gao H, Liu Q, Hong X, Sun Y (2018) A new method for identifying the ball screw degradation level based on the multiple classifier system. Measurement 130:118–127. https://doi.org/10.1016/j.measurement.2018.08.005
Mauro S, Pastorelli S, Johnston E (2015) Influence of controller parameters on the life of ball screw feed drives. Adv Mech Eng 7(8):1–11. https://doi.org/10.1177/1687814015599728
Lin C, Jay F (2007) Model-based design of motorized spindle systems to improve dynamic performance at high speeds. J Manuf Process 9(2):94–108. https://doi.org/10.1016/s1526-6125(07)70111-1
Brecher C, Spachtholz G, Paepenmüller F (2007) Developments for high performance machine tool spindles. CIRP Ann 56(1):395–399. https://doi.org/10.1016/j.cirp.2007.05.092
Jiang D, Wang T, Jiang Y, Liu L, Hu M (2010) Reliability assessment of machine tool spindle bearing based on vibration feature. International Conference on Digital Manufacturing & Automation 2010:154–157. https://doi.org/10.1109/ICDMA.2010.105
Wang R, Zhang Z, Xia Z, Miao J, Guo Y (2019) A new approach for rolling bearing fault diagnosis based on EEMD hierarchical entropy and improved CS-SVM. Prognostics and System Health Management Conference 2019:1–6. https://doi.org/10.1109/PHM-Qingdao46334.2019.8942988
Rastegari A (2017) Vibration analysis of machine tool spindle units. 12th World Congress on Engineering Asset Management & 13th International Conference on Vibration Engineering and Technology of Machinery 2017:511–522. https://doi.org/10.1007/978-3-319-95711-1_51
Choudhury A, Tandon N (2000) Application of acoustic emission technique for the detection of defects in rolling element bearings. Tribol Int 33(1):39–45. https://doi.org/10.1016/S0301-679X(00)00012-8
Katter J, Tu J (1996) Bearing condition monitoring for preventive maintenance in a production environment. Tribol T 39(4):936–942. https://doi.org/10.1080/10402009608983615
Tu J (1995) Thermoelastic instability monitoring for preventing spindle bearing seizure. Tribol T 38(1):11–18. https://doi.org/10.1080/10402009508983374
Li P, Liu Y, Wang Z, Zhang W, Zhou X (2016) Process improvement of CNC lathe bed casting structure (Chinese). Modern Cast Iron 36(1):74–78. https://doi.org/10.3969/j.issn.1003-8345.2016.01.012
Wang R, Sui X, Wei D (2016) Research and analysis of residual stress measurement of ram casting in gantry machine center. 3rd International Conference on Materials Engineering, Manufacturing Technology and Control 2016:380–384. https://doi.org/10.2991/icmemtc-16.2016.73
Mittal S, Liu C (1998) A method of modeling residual stresses in superfinish hard turning. Wear 218(1):21–33. https://doi.org/10.1016/S0043-1648(98)00201-4
Soori M, Asmael M (2020) Mechanical behavior of materials in metal cutting operations, a review. J New Technol Mater 10(2):79–89. https://www.researchgate.net/publication/345724625
Sasahara H, Obikawa T, Shirakashi T (2004) Prediction model of surface residual stress within a machined surface by combining two orthogonal plane models. Int J Mach Tools Manuf 44:815–822. https://doi.org/10.1016/j.ijmachtools.2004.01.002
Li J, Jing L, Chen M (2009) An FEM study on residual stresses induced by high-speed end-milling of hardened steel SKD11. J Mater Process Tech 209(9):4515–4520. https://doi.org/10.1016/j.jmatprotec.2008.10.042
Li Y, Shi Z, Lin J, Yang Y, Saillard P, Said R (2018) Effect of machining-induced residual stress on springback of creep age formed AA2050 plates with asymmetric creep-ageing behaviour. Int J Mach Tools Manuf 132:113–122. https://doi.org/10.1016/j.ijmachtools.2018.05.003
Reimer A, Fitzpatrick S, Luo X, Zhao J (2017) Numerical investigation of mechanical induced stress during precision end milling hardened tool steel. Solid State Phenom 261:362–369. https://doi.org/10.4028/www.scientific.net/SSP.261.362
Attanasio A, Ceretti E, Cappellini C, Giardini C (2011) Residual stress prediction by means of 3D FEM simulation. Adv Mat Res 223(2011):431–438. https://doi.org/10.4028/www.scientific.net/AMR.223.431
Liu H, Sun Y, Lu Z (2009) 3D finite element simulation and experiment of residual stress on the cutting surface. In 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies 7282(1):728220–728225. https://doi.org/10.1117/12.830894
Chen M, Xu B (2012) Bolted joint looseness damage detection using electromechanical impedance measurements by PZT sensors. In Third International Conference on Smart Materials and Nanotechnology in Engineering 8409(1):840925–840929. https://doi.org/10.1117/12.923329
Doebling S, Farrar C, Prime M (1998) A summary review of vibration-based damage identification methods. The Shock and Vibration Digest 30(2):91–105. https://doi.org/10.1177/058310249803000201
Nichols J, Todd M, Wait J (2003) Using state space predictive modeling with chaotic interrogation in detecting joint preload loss in a frame structure experiment. Smart Mater Struct 12(4):580–601. https://doi.org/10.1088/0964-1726/12/4/310
Bailey J, Jeelani S, Becker S (1976) Surface integrity in machining AISI 4340 steel. J Eng Technol 98(3):999–1006. https://doi.org/10.1115/1.3439063
Li W, Lu C, Yang Z (2013) The research of the assembly precision of CNC machine tools considering the joint surface characteristics. Appl Mech Mater 397–400:198–201. https://doi.org/10.4028/www.scientific.net/AMM.397-400.198
Shen J, Xu P, Yu Y (2019) Dynamic characteristics analysis and finite element simulation of steel–BFPC machine tool joint surface. J Manuf Sci E-T ASME 142(1):011006. https://doi.org/10.1115/1.4045417
Shi Y, Zhao X, Zeng L, Wang H, Zhang D (2011) Dynamic characteristic analysis and structural modification of a 5-axis horizontal machining center considering joint surface. Mater Sci Forum 697–698:513–516. https://doi.org/10.4028/www.scientific.net/MSF.697-698.513
Omar O, Wardany T, Ng E, Elbestawi M (2007) An improved cutting force and surface topography prediction model in end milling. Int J Mach Tools Manuf 47:1263–1275. https://doi.org/10.1016/j.ijmachtools.2006.08.021
Jiang B, Zhang K, Yang L, Zhang M, Wang S (2014) Machining error and milling process control methods for joint surface of machine tool. Mater Sci Forum 800–801:666–671. https://doi.org/10.4028/www.scientific.net/MSF.800-801.666
Kartini (2020) Finite element method for modeling of real rough surface. International Journal of Advanced Research in Engineering and Technology 11(7):134–149. https://doi.org/10.34218/IJARET.11.7.2020.015
Abdulshahed A, Longstaff A, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27:158–168. https://doi.org/10.1016/j.asoc.2014.11.012
Creighton E, Honegger A, Tulsian A, Mukhopadhyay D (2010) Analysis of thermal errors in a high-speed micro-milling spindle. Int J Mach Tools Manuf 50(4):386–393. https://doi.org/10.1016/j.ijmachtools.2009.11.002
Huang J, Zhou Z, Liu M, Zhang E, Chen M, Pham D, Ji C (2015) Real-time measurement of temperature field in heavy-duty machine tools using fiber Bragg grating sensors and analysis of thermal shift errors. Mechatronics 31:16–21. https://doi.org/10.1016/j.mechatronics.2015.04.004
Fujishima M, Narimatsu K, Irino N, Mori M, Ibaraki S (2019) Adaptive thermal displacement compensation method based on deep learning. CIRP J Manuf Sci Technol 25:22–25. https://doi.org/10.1016/j.cirpj.2019.04.002
Wąsik M, Kolka A (2017) Machining accuracy improvement by compensation of machine and workpiece deformation. Procedia Manufacturing 11:2187–2194. https://doi.org/10.1016/j.promfg.2017.07.365
Kegg R (1984) One-line machine and process diagnostics. CIRP Ann 33(2):469–473. https://doi.org/10.1016/s0007-8506(16)30168-8
Li X, Li D, Lao Y, Zhang R, Han Y, Yao X (2020) Overview of machine tool error detection technology. J Phys: Conf Ser 1550:032152. https://doi.org/10.1088/1742-6596/1550/3/032152
Han Z, Jin H, Liu Y, Fu H (2013) A review of geometric error modeling and error detection for CNC machine tools. Appl Mech Mater 303–306:627–631. https://doi.org/10.4028/www.scientific.net/AMM.303-306.627
Frankowiak M, Grosvenor R, Prickett P (2005) A review of the evolution of microcontroller-based machine and process monitoring. Int J Mach Tools Manuf 45(4–5):573–582. https://doi.org/10.1016/j.ijmachtools.2004.08.018
Guo S, Pang B, Jiang G, Chen H, Li Z (2020) Time-varying reliability modelling and quasi-static accuracy optimization of precision CNC machine tools. J Phys: Conf Ser 1654:012022. https://doi.org/10.1088/1742-6596/1654/1/012022
Zhang Z, Cai L, Cheng Q, Liu Z, Gu P (2016) A geometric error budget method to improve machining accuracy reliability of multi-axis machine tools. J Intell Manuf 30(2):495–519. https://doi.org/10.1007/s10845-016-1260-8
Xing K, Achiche S, Mayer J (2019) Five-axis machine tools accuracy condition monitoring based on volumetric errors and vector similarity measures. Int J Mach Tools Manuf 138:80–93. https://doi.org/10.1016/j.ijmachtools.2018.12.002
Funding
This work was supported by Jinan University and Institute Innovation Team Program (Grant No. 2020GXRC025), the National New Material Production and Application Demonstration Platform Construction Program (Grant No. 2020–370104-34–03-043952), the Taishan Scholar Project of Shandong Province (Grant No. ts201712002), and the Science Education Industry Integration Pilot Project of Shandong Province (Grant nos.2020KJC-ZD05).
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Liu, W., Zhang, S., Lin, J. et al. Advancements in accuracy decline mechanisms and accuracy retention approaches of CNC machine tools: a review. Int J Adv Manuf Technol 121, 7087–7115 (2022). https://doi.org/10.1007/s00170-022-09720-0
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DOI: https://doi.org/10.1007/s00170-022-09720-0