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A Literature Review on Prediction Methods for Forced Responses and Associated Surface Form/Location Errors in Milling

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Abstract

During milling operations, regenerative and periodic forces are generated from the interactions of cutting tools and workpieces. While the regenerative forces can be preempted by proper choice of process parameters, the periodic forces cannot. The latter induce forced responses which leave behind imprints on machined parts causing, for example, tolerance concerns. Fortunately, the responses and thus the imprints can be mitigated by proper choice of parameters. In the published literature, the imprints seem to be predominantly named according to the type of analytical treatment of the problem. They are mostly called (surface) form error (SFE) when deemed to result from static deflections at instants of periodic forces while they are mostly called surface location error (SLE) when deemed to result from dynamic responses to the periodic forces. Both SFE and SLE are referred to as periodic force induced surface error (PFISE) here. This work presents a comprehensive literature review of the available PFISE modeling methods, including concise generic explanations of the analytical formulations and the solution algorithms. The review contains more than 340 literature references which also cover data-driven methods for which measurements/images of machined surface are used to generate point cloud of surface topography from which patterns of PFISE can be identified or empirical/black box models of surface errors (which include PFISE) can be calibrated. The results of PFISE modeling is usually applied in error compensation algorithms and, hence, works on such applications are also reviewed. This literature review unveils the trends of over 6 decades and still continuing research and recommends avenues to further the research in the future.

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Data sharing not applicable to this article as no datasets were generated during the current study. The illustrative numerical results were based on parametric values drawn from duly cited published papers.

References

  1. Tricco AC, Lillie E, Zarin W, O’Brien K, Colquhoun H, Kastner M, Levac D, Ng C, Sharpe JP, Wilson K, Kenny M, Warren R, Wilson C, Stelfox HT, Straus SE (2016) A scoping review on the conduct and reporting of scoping reviews. BMC Med Res Methodol 16(1):1–10

    Google Scholar 

  2. Altintas Y, Budak E (1995) Analytical prediction of stability lobes in milling. CIRP Ann Manuf Technol 44(1):357–362

    Google Scholar 

  3. Insperger T, Stépán G (2004) Updated semi-discretization method for periodic delay-differential equations with discrete delay. Int J Numer Meth Eng 61(1):117–141

    MathSciNet  Google Scholar 

  4. Ding Y, Zhu L, Zhang X, Ding H (2010) A full-discretization method for prediction of milling stability. Int J Mach Tools Manuf 50(5):502–509

    Google Scholar 

  5. Ozoegwu CG, Omenyi SN, Ofochebe SM (2015) Hyper-third order full-discretization methods in milling stability prediction. Int J Mach Tools Manuf 92:1–9

    Google Scholar 

  6. Ozoegwu CG, Eberhard P (2019) Tensor-based automatic arbitrary order computation of the full-discretization method for milling stability analysis. In: Altenbach H, Irschik H, Matveenko V (eds) Contributions to advanced dynamics and continuum mechanics, chap 11. Springer International Publishing, Cham, Switzerland, pp 179–205

    Google Scholar 

  7. Mann BP, Young KA, Louis S, Schmitz TL, Dilley DN (2005) Simultaneous stability and surface location error predictions in milling. J Manuf Sci Eng 127(3):446–453

    Google Scholar 

  8. Budak E, Altintas Y (1995) Modeling and avoidance of static form errors in peripheral milling of plates. Int J Mach Tools Manuf 35(3):459–476

    Google Scholar 

  9. Kline WA, Devor RE, Shareef IA (1982) The prediction of surface accuracy in end milling. J Manuf Sci Eng 104(3):272–278

    Google Scholar 

  10. Budak E, Altintas Y (1994) Peripheral milling conditions for improved dimensional accuracy. Int J Mach Tools Manuf 34(7):907–918

    Google Scholar 

  11. Lim EM, Feng H-Y, Menq C-H, Lin Z-H (1995) The prediction of dimensional error for sculptured surface productions using the ball-end milling process. Part 1: chip geometry analysis and cutting force prediction. Int J Mach Tools Manuf 35(8):1149–1169

    Google Scholar 

  12. Guo Y, Ye W, Xu X (2021) Numerical and experimental investigation of the temperature rise of cutting tools caused by the tool deflection energy. Machines 9(6):122

    Google Scholar 

  13. Kline WA, Devor RE, Lindberg JR (1982) The prediction of cutting forces in end milling with application to cornering cut. Int J Mach Tool Design Res 22(1):7–22

    Google Scholar 

  14. National Twist Drill and Tool Co. (1961) Accuracy of milled surfaces. Part I: effect of cutter diameter, number of teeth, runout and feed. Tech. Rep. I

  15. National Twist Drill and Tool Co. (1961) Accuracy of milled surfaces. Part I: effect of end mill deflection. Tech. Rep. 2

  16. National Twist Drill and Tool Co. (1962) Accuracy of milled surfaces. Part 3: arbor type cutters versus end mills. Tech. Rep. 1

  17. National Twist Drill and Tool Co. (1968) The influence of numerical control and adaptive control upon cutting tool design I—end mills. Tech. Rep. 2

  18. Schmitz T, Ziegert J (1999) Examination of surface location error due to phasing of cutter vibrations. Precis Eng 23(1):51–62

    Google Scholar 

  19. Bolsunovskiy S, Vermel V, Gubanov G, Kacharava I, Kudryashov A (2013) Thin-walled part machining process parameters optimization based on finite-element modeling of workpiece vibrations. Proc CIRP 8:276–280

    Google Scholar 

  20. Bachrathy D, Insperger T, Stépán G (2009) Surface properties of the machined workpiece for helical mills. Mach Sci Technol 13(2):227–245

    Google Scholar 

  21. Fuh KH, Chang HY (1997) An accuracy model for the peripheral milling of aluminum alloys using response surface design. J Mater Process Technol 72(1):42–47

    Google Scholar 

  22. Yuan L, Zeng S, Chen Z (2015) Simultaneous prediction of surface topography and surface location error in milling. Proc Inst Mech Eng C J Mech Eng Sci 229(10):1805–1829

    Google Scholar 

  23. Schmitz TL, Mann BP (2006) Closed-form solutions for surface location error in milling. Int J Mach Tools Manuf 46(12–13):1369–1377

    Google Scholar 

  24. Islam MN, Lee HU, Cho D-W (2008) Prediction and analysis of size tolerances achievable in peripheral end milling. Int J Adv Manuf Technol 39(1):129–141

    Google Scholar 

  25. Ozoegwu CG (2014) Least squares approximated stability boundaries of milling process. Int J Mach Tools Manuf 79:24–30

    Google Scholar 

  26. Ozoegwu CG (2016) High order vector numerical integration schemes applied in state space milling stability analysis. Appl Math Comput 273:1025–1040

    MathSciNet  Google Scholar 

  27. Eksioglu C, Kilic ZM, Altintas Y (2012) Discrete-time prediction of chatter stability, cutting forces, and surface location errors in flexible milling systems. J Manuf Sci Eng 134(6):1–13

    Google Scholar 

  28. Zhang SJ, To S, Zhang GQ, Zhu ZW (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

    Google Scholar 

  29. Yue C, Gao H, Liu X, Liang SY, Wang L (2019) A review of chatter vibration research in milling. Chin J Aeronaut 32(2):215–242

    Google Scholar 

  30. Sun Y, Zheng M, Jiang S, Zhan D, Wang R (2023) A state-of-the-art review on chatter stability in machining thin-walled parts. Machines 11(3):359

    Google Scholar 

  31. Ozturk E, Budak E (2007) Modeling of 5-axis milling processes. Mach Sci Technol 11(3):287–311

    Google Scholar 

  32. Layegh K SE, Lazoglu I (2017) 3D surface topography analysis in 5-axis ball-end milling. CIRP Ann 66(1):133–136

    Google Scholar 

  33. Artetxe E, Olvera D, de Lacalle LNL, Campa FJ, Olvera D, Lamikiz A (2017) Solid subtraction model for the surface topography prediction in flank milling of thin-walled integral blade rotors (IBRs). Int J Adv Manuf Technol 90(1):741–752

    Google Scholar 

  34. Fallah M, Arezoo B (2012) Compensation of reference surface errors in the machining of free form features. Proc Inst Mech Eng B J Eng Manuf 226(5):824–836

    Google Scholar 

  35. Omar OE, El-Wardany T, Ng E, Elbestawi MA (2007) An improved cutting force and surface topography prediction model in end milling. Int J Mach Tools Manuf 47(7–8):1263–1275

    Google Scholar 

  36. Agarwal A, Desai KA (2021) Modeling of flatness errors in end milling of thin-walled components. Proc Inst Mech Eng B J Eng Manuf 235(3):543–554

    Google Scholar 

  37. Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools—a review. Part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf 40:1235–1256

    Google Scholar 

  38. Suh S-H, Cho J-H, Hascoet J-Y (1996) Incorporation of tool deflection in tool path computation: simulation and analysis. J Manuf Syst 15(3):190–199

    Google Scholar 

  39. Chen W, Xue J, Tang D, Chen H, Qu S (2009) Deformation prediction and error compensation in multilayer milling processes for thin-walled parts. Int J Mach Tools Manuf 49(11):859–864

    Google Scholar 

  40. Zuo X, Zhang C, Li H, Wu X, Zhou X (2018) Error analysis and compensation in machining thin-walled workpieces based on the inverse reconstruction model. Int J Adv Manuf Technol 95(5):2369–2377

    Google Scholar 

  41. Wimmer S, Zaeh MF (2018) The prediction of surface error characteristics in the peripheral milling of thin-walled structures. J Manuf Mater Process 2(1):13

    Google Scholar 

  42. Dotcheva A, Millward M, Lewis H (2008) The evaluation of cutting-force coefficients using surface error measurements. J Mater Process Technol 196(1–3):42–51

    Google Scholar 

  43. Wang B, Hao H, Wang M, Hou J, Feng Y (2013) Identification of instantaneous cutting force coefficients using surface error. Int J Adv Manuf Technol 68(1):701–709

    Google Scholar 

  44. Wan M, Yuan H, Feng J, Zhang W-H, Yin W (2017) Industry-oriented method for measuring the cutting forces based on the deflections of tool shank. Int J Mech Sci 130:315–323

    Google Scholar 

  45. Mears L, Roth JT, Djurdjanovic D, Yang X, Kurfess T (2009) Quality and inspection of machining operations: CMM integration to the machine tool. J Manuf Sci Eng 131(5):051006

    Google Scholar 

  46. Chen YP, Gao J, Wu LF (2010) Review on deflection compensation methods for machining of thin-walled components. Appl Mech Mater 29–32:1768–1776

    Google Scholar 

  47. Del Sol I, Rivero A, de Lacalle LN, Gamez AJ (2019) Thin-wall machining of light alloys: a review of models and industrial approaches. Materials 12(12):2012

  48. Wang X, Zhao B, Ding W, Pu C, Wang X, Peng S, Ma F (2022) A short review on machining deformation control of aero-engine thin-walled casings. Int J Adv Manuf Technol 121(5):2971–2985

    Google Scholar 

  49. Fei J, Xu F, Lin B, Huang T (2020) State of the art in milling process of the flexible workpiece. Int J Adv Manuf Technol 109(5):1695–1725

    Google Scholar 

  50. Wu G, Li G, Pan W, Raja I, Wang X, Ding S (2021) A state-of-art review on chatter and geometric errors in thin-wall machining processes. J Manuf Process 68:454–480

    Google Scholar 

  51. Mali RA, Gupta TVK, Ramkumar J (2021) A comprehensive review of free-form surface milling—advances over a decade. J Manuf Process 62:132–167

    Google Scholar 

  52. Li X, Huang T, Zhao H, Zhang X, Yan S, Dai X, Ding H (2022) A review of recent advances in machining techniques of complex surfaces. Sci China Technol Sci 65(9):1915–1939

    Google Scholar 

  53. Pathak VK, Singh R (2022) A comprehensive review on computational techniques for form error evaluation. Archiv Comput Methods Eng 29(2):1199–1228

    Google Scholar 

  54. Ozoegwu CG, Eberhard P (2022) Closed-form models for the cutting forces of general-helix cylindrical milling tools. Proc Inst Mech Eng B J Eng Manuf. https://doi.org/10.1177/09544054221141130

    Article  Google Scholar 

  55. Ozoegwu CG, Eberhard P (2023) Multi-objective optimization of the helix shape of cylindrical milling tools. In: Altenbach H, Irschik H, Porubov AV (eds) Progress in continuum mechanics, chap. 18. Springer International Publishing, Cham, Switzerland, pp 303–320

    Google Scholar 

  56. Ozoegwu CG (2023) Upgraded closed-form cutting force models for general-helix cylindrical milling tools with application to cutting power and energy demand modeling. Proc Inst Mech Eng B J Eng Manuf. https://doi.org/10.1177/09544054231181158

    Article  Google Scholar 

  57. Altintas Y, Spence A, Tlusty J (1991) End milling force algorithms for CAD systems. Ann CIRP 40(1):31–34

    Google Scholar 

  58. Liu X, Cheng K, Webb D, Luo X (2002) Improved dynamic cutting force model in peripheral milling. Part I: theoretical model and simulation. Int J Adv Manuf Technol 20:631–638

    Google Scholar 

  59. Budak E (2006) Analytical models for high performance milling. Part I: cutting forces, structural deformations and tolerance integrity. Int J Mach Tools Manuf 46:1478–1488

    Google Scholar 

  60. Moufki A, Le Coz G, Dudzinski D (2017) End-milling of inconel 718 superalloy—an analytical modelling. Proc CIRP 58:358–363

    Google Scholar 

  61. Chen Y, Li H, Wang J (2018) Predictive modelling of cutting forces in end milling of titanium alloy Ti6Al4V. Proc Inst Mech Eng B J Eng Manuf 232(9):1523–1534

    Google Scholar 

  62. Tehranizadeh F, Budak E (2017) Design of serrated end mills for improved productivity. Proc CIRP 58:493–498

    Google Scholar 

  63. Ozoegwu CG, Eberhard P (2022) Geometric definition, rapid prototyping, and cutting force analysis of cylindrical milling tools with arbitrary helix angle variations. Proc Inst Mech Eng B J Eng Manuf 236(9):1232–1246

    Google Scholar 

  64. Engin S, Altintas Y (2001) Mechanics and dynamics of general milling cutters. Part I: helical end mills. Int J Mach Tools Manuf 41(15):2195–2212

    Google Scholar 

  65. Song Q, Liu Z, Ju G, Wan Y (2019) A generalized cutting force model for five-axis milling processes. Proc Inst Mech Eng B J Eng Manuf 233(1):3–17

    Google Scholar 

  66. Koenigsberger F, Sabberwal A (1961) An investigation into the cutting force pulsations during milling operations. Int J Mach Tool Design Res 1(3):15–33

    Google Scholar 

  67. Ryu SH, Lee HS, Chu CN (2003) The form error prediction in side wall machining considering tool deflection. Int J Mach Tools Manuf 43:1405–1411

    Google Scholar 

  68. Chiang HN, Junz Wang JJ (2011) Generating mechanism and formation criteria of kinked surface in peripheral end milling. Int J Mach Tools Manuf 51(10):816–830

    Google Scholar 

  69. Law KM, Geddam A (2001) Prediction of contour accuracy in the end milling of pockets. J Mater Process Technol 113(1–3):399–405

    Google Scholar 

  70. López de Lacalle LN, Lamikiz A, Sánchez JA, Salgado MA (2007) Toolpath selection based on the minimum deflection cutting forces in the programming of complex surfaces milling. Int J Mach Tools Manuf 47(2):388–400

    Google Scholar 

  71. Morelli L, Grossi N, Scippa A, Campatelli G (2021) Extended classification of surface errors shapes in peripheral end-milling operations. J Manuf Process 71:604–624

    Google Scholar 

  72. Pytel A, Singer FL (1987) Strength of materials. Harper & Row, New York

    Google Scholar 

  73. Nemes JA, Asamoah-Attiah S, Budak E, Kops L (2001) Cutting load capacity of end mills with complex geometry. CIRP Ann 50(1):65–68

    Google Scholar 

  74. Kivanc EB, Budak E (2004) Structural modeling of end mills for form error and stability analysis. Int J Mach Tools Manuf 44(11):1151–1161

    Google Scholar 

  75. Dotcheva M, Millward H (2005) The application of tolerance analysis to the theoretical and experimental evaluation of a CNC corner-milling operation. J Mater Process Technol 170(1):284–297

    Google Scholar 

  76. Salgado MA, López de Lacalle LN, Lamikiz A, Muñoa J, Sánchez JA (2005) Evaluation of the stiffness chain on the deflection of end-mills under cutting forces. Int J Mach Tools Manuf 45(6):727–739

    Google Scholar 

  77. Uriarte L, Herrero A, Zatarain M, Santiso G, Lopéz de Lacalle LN, Lamikiz A, Albizuri J (2007) Error budget and stiffness chain assessment in a micromilling machine equipped with tools less than 0.3mm in diameter. Precis Eng 31(1):1–12

    Google Scholar 

  78. Seo T-I, Cho M-W (1999) Tool trajectory generation based on tool deflection effects in flat- end milling process (I)—tool path compensation strategy. KSME Int J I3(10):738–751

    Google Scholar 

  79. Wang WP (1988) Solid modeling for optimizing metal removal of three-dimensional NC end milling. J Manuf Syst 7(1):57–65

    Google Scholar 

  80. Vichare P, Nassehi A, Newman S (2009) A unified manufacturing resource model for representation of computerized numerically controlled machine tools. Proc Inst Mech Eng B J Eng Manuf 223:463–483

    Google Scholar 

  81. Shirase K, Altintas Y (1996) Cutting force and dimensional surface error generation in peripheral milling with variable pitch helical end mills. Int J Mach Tools Manuf 36(5):567–584

    Google Scholar 

  82. Larue A, Anselmetti B (2003) Deviation of a machined surface in flank milling. Int J Mach Tools Manuf 43:129–138

    Google Scholar 

  83. Kops L, Vo DT (1990) Determination of the equivalent diameter of an end mill based on its compliance. CIRP Ann 39(1):93–96

    Google Scholar 

  84. Aydin M, Ucar M, Cengiz A, Kurt M (2015) Identification of static surface form errors from cutting force distribution in flat-end milling processes. J Braz Soc Mech Sci Eng 37:1001–1013

    Google Scholar 

  85. Vazirian M, Movahhedy M, Akbari J (2009) Study of the effects of miniaturization on static and dynamic form errors in desktop milling machines. In: ASME international mechanical engineering congress and exposition (Lake Buena Vista, Florida), vol 4. American Society of Mechanical Engineers

  86. Xu J, Yan F, Wan X, Li Y, Zhu Q (2023) Surface topography model of ultra-high strength steel AF1410 based on dynamic characteristics of milling system. Processes 11(2):641

    Google Scholar 

  87. Choi J-G, Yang M-Y (1999) In-process prediction of cutting depths in end milling. Int J Mach Tools Manuf 39(5):705–721

    Google Scholar 

  88. Xu AP, Qu YX, Zhang DW, Huang T (2003) Simulation and experimental investigation of the end milling process considering the cutter flexibility. Int J Mach Tools Manuf 43(3):283–292

    Google Scholar 

  89. 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

    Google Scholar 

  90. Ryu SH (2012) An analytical expression for end milling forces and tool deflection using Fourier series. Int J Adv Manuf Technol 59(1–4):37–46

    Google Scholar 

  91. Sutherland JW, DeVor RE (1986) An improved method for cutting force and surface error prediction in flexible end milling systems. J Manuf Sci Trans ASME 108(4):269–279

    Google Scholar 

  92. Armarego EJA, Deshpande NP (1991) Computerized end-milling force predictions with cutting models allowing for eccentricity and cutter deflections. Ann CIRP 40(1):25–29

    Google Scholar 

  93. De Lacalle LN, Lamikiz A, Sánchez JA, Salgado MA (2004) Effects of tool deflection in the high-speed milling of inclined surfaces. Int J Adv Manuf Technol 24(9–10):621–631

    Google Scholar 

  94. Rao VS, Rao PVM (2006) Effect of workpiece curvature on cutting forces and surface error in peripheral milling. Proc Inst Mech Eng B J Eng Manuf 220(9):1399–1407

    Google Scholar 

  95. Lim EM, Menq CH (1995) The prediction of dimensional error for sculptured surface productions using the ball-end milling process. Part 2: surface generation model and experimental verification. Int J Mach Tools Manuf 35(8):1171–1185

    Google Scholar 

  96. Lim EM, Menq CH, Yen DW (1997) Integrated planning for precision machining of complex surfaces—III. Compensation of dimensional errors. Int J Mach Tools Manuf 37(9):1313–1326

    Google Scholar 

  97. Kim GM, Kim BH, Chu CN (2003) Estimation of cutter deflection and form error in ball-end milling processes. Int J Mach Tools Manuf 43:917–924

    Google Scholar 

  98. Yuan M, Wang X, Jiao L, Yi J, Liu S (2017) Prediction of dimension error based on the deflection of cutting tool in micro ball-end milling. Int J Adv Manuf Technol 93:825–837

    Google Scholar 

  99. Desai KA, Rao PVM (2012) On cutter deflection surface errors in peripheral milling. J Mater Process Technol 212(11):2443–2454

    Google Scholar 

  100. Wei ZC, Wang MJ, Cai YJ, Zhu JN, Wang L (2013) Form error estimation in ball-end milling of sculptured surface with z-level contouring tool path. Int J Adv Manuf Technol 65(1–4):363–369

    Google Scholar 

  101. Wei ZC, Wang MJ, Tang WC, Zhu JN, Xia GC (2013) Form error compensation in ball-end milling of sculptured surface with z-level contouring tool path. Int J Adv Manuf Technol 67(9–12):2853–2861

    Google Scholar 

  102. Bo L, Yanlong C, Wenhua C, Jun P (2017) Geometry simulation and evaluation of the surface topography in five-axis ball-end milling. Int J Adv Manuf Technol 93(5):1651–1667

    Google Scholar 

  103. Cai C, An Q, Ming W, Chen M (2021) Modelling of machined surface topography and anisotropic texture direction considering stochastic tool grinding error and wear in peripheral milling. J Mater Process Technol 292:117065

    Google Scholar 

  104. Nishida I, Okumura R, Sato R, Shirase K (2018) Cutting force and finish surface simulation of end milling operation in consideration of static tool deflection by using voxel model. Proc CIRP 77:574–577

    Google Scholar 

  105. Moges TM, Desai KA, Rao PV (2018) Modeling of cutting force, tool deflection, and surface error in micro-milling operation. Int J Adv Manuf Technol 98(9–12):2865–2881

    Google Scholar 

  106. Wang C, Ding P, Huang X, Gao T, Li C, Zhang C (2021) Reliability sensitivity analysis of ball-end milling accuracy. Int J Adv Manuf Technol 112(7):2051–2064

    Google Scholar 

  107. Denkena B, Schmidt C (2007) Experimental investigation and simulation of machining thin-walled workpieces. Prod Eng Res Dev 1(4):343–350

    Google Scholar 

  108. Houjun Q, Dawei Z, Bing Y, Yujun C (2009) Effect of part-cutter deflection on flexible milling force in high speed peripheral milling process. In: International technology and innovation conference 2009 (ITIC 2009), Xi’an, China, pp 1–5

  109. Zhang J, Lin B, Fei J, Huang T, Xiao J, Zhang X, Ji C (2018) Modeling and experimental validation for surface error caused by axial cutting force in end-milling process. Int J Adv Manuf Technol 99(1–4):327–335

    Google Scholar 

  110. Yu S (2010) Modeling methodology of flexible milling force for low-rigidity processing system during high speed milling. J Tianjin Univ 43(2):143–148

    Google Scholar 

  111. Yue C, Chen Z, Liang SY, Gao H, Liu X (2019) Modeling machining errors for thin-walled parts according to chip thickness. Int J Adv Manuf Technol 103(1–4):91–100

    Google Scholar 

  112. Chen Z, Yue C, Liang SY, Liu X, Li H, Li X (2020) Iterative form error prediction for side-milling of thin-walled parts. Int J Adv Manuf Technol 107(9):4173–4189

    Google Scholar 

  113. Li Y, Cheng X, Zheng G, Yan J, Liu H, Li X (2022) Dynamic modeling and in-process parametric compensation for fabricating micro straight thin walls by micromilling. J Market Res 18:2480–2493

    Google Scholar 

  114. Fei J, Lin B, Yan S, Ding M, Zhang X, Zhang J, Lan J (2017) Theoretical prediction and experimental validation of dynamic deformation during machining of thin-walled structure. In: ASME international mechanical engineering congress and exposition (Tampa, Florida), vol 2. American Society of Mechanical Engineers

  115. Llanos I, Robles A, Condón J, Arizmendi M, Beristain A (2023) Deflection error modeling during thin-wall machining. Proc CIRP 117:169–174

    Google Scholar 

  116. Dépincé P, Hascoët J-Y (2006) Active integration of tool deflection effects in end milling. Part 1. Prediction of milled surfaces. Int J Mach Tools Manuf 46(9):937–944

    Google Scholar 

  117. Dépincé P, Hascoët J-Y (2006) Active integration of tool deflection effects in end milling. Part 2. Compensation of tool deflection. Int J Mach Tools Manuf 46(9):945–956

    Google Scholar 

  118. Habibi M, Arezoo B, Vahebi Nojedeh M (2011) Tool deflection and geometrical error compensation by tool path modification. Int J Mach Tools Manuf 51(6):439–449

    Google Scholar 

  119. Habibi M, Tuysuz O, Altintas Y (2019) Modification of tool orientation and position to compensate tool and part deflections in five-axis ball end milling operations. J Manuf Sci Eng 141(3):031004

  120. Ong TS, Hinds BK (2003) The application of tool deflection knowledge in process planning to meet geometric tolerances. Int J Mach Tools Manuf 43(7):731–737

    Google Scholar 

  121. Landon Y, Segonds S, Lascoumes P, Lagarrigue P (2004) Tool positioning error (TPE) characterisation in milling. Int J Mach Tools Manuf 44(5):457–464

    Google Scholar 

  122. Rao VS, Rao PVM (2006) Tool deflection compensation in peripheral milling of curved geometries. Int J Mach Tools Manuf 46(15):2036–2043

    Google Scholar 

  123. Wei ZC, Wang MJ, Tang WC, Wang L (2012) Tool deflection error regularization and compensation in end milling of contour surfaces. Appl Mech Mater 217–219:1341–1345

    Google Scholar 

  124. Zuo X, Li B, Yang J, Jiang X (2013) Integrated geometric error compensation of machining processes on CNC machine tool. Proc CIRP 8:135–140

    Google Scholar 

  125. Brecher C, Wetzel A, Berners T, Epple A (2019) Increasing productivity of cutting processes by real-time compensation of tool deflection due to process forces. J Mach Eng 19(1):16–27

    Google Scholar 

  126. Chen Q, Maeng S, Li W, Zhou Z, Min S (2020) Geometric- and force-induced errors compensation and uncertainty analysis of rotary axis in 5-axis ultra-precision machine tool. Int J Adv Manuf Technol 109(3):841–856

    Google Scholar 

  127. Ma J-W, Zhang N, Chen S-Y, Su W-W, Hu G-Q (2018) Deformation analysing for thin-walled parts based on analysis of single-tooth or multi-tooth milling. Int J Mach Mach Mater 20(6):575–593

    Google Scholar 

  128. Smaoui M, Bouaziz Z, Zghal A, Dessein G, Baili M (2011) Simulation of the deflected cutting tool trajectory in complex surface milling. Int J Adv Manuf Technol 56(5):463–474

    Google Scholar 

  129. Ratchev S, Govender E, Nikov S, Phuah K, Tsiklos G (2003) Force and deflection modelling in milling of low-rigidity complex parts. J Mater Process Technol 143–144(1):796–801

    Google Scholar 

  130. Ratchev S, Nikov S, Moualek I (2004) Material removal simulation of peripheral milling of thin wall low-rigidity structures using FEA. Adv Eng Softw 35(8):481–491

    Google Scholar 

  131. Ratchev S, Huang W, Liu S, Becker AA (2004) Modelling and simulation environment for machining of low-rigidity components. J Mater Process Technol 153–154:67–73

    Google Scholar 

  132. Kang Y, Wang Z, Wu J, Jiang C (2006) Numerical prediction of static form errors in the end milling of thin-walled workpiece. In: 2006 International technology and innovation conference, no. 524 (Hangzhou, China). Institution of Engineering and Technology, pp 816–824

  133. Wan M, Zhang WH (2006) Calculations of chip thickness and cutting forces in flexible end milling. Int J Adv Manuf Technol 29(7–8):637–647

    Google Scholar 

  134. Wan MA, Zhang WH, Qin GH, Wang ZP (2008) Strategies for error prediction and error control in peripheral milling of thin-walled workpiece. Int J Mach Tools Manuf 48:1366–1374

    Google Scholar 

  135. Kang YG, Wang ZQ, Wu JJ, Jiang CY (2008) Efficient algorithms for calculations of the maximum surface form errors in peripheral milling. Appl Mech Mater 10–12:757–761

    Google Scholar 

  136. Huang T, Zhang X-M, Ding H (2017) Tool orientation optimization for reduction of vibration and deformation in ball-end milling of thin-walled impeller blades. Proc CIRP 58:210–215

    Google Scholar 

  137. Agarwal A, Desai KA (2020) Tool and workpiece deflection induced flatness errors in milling of thin-walled components. Proc CIRP 93:1411–1416

    Google Scholar 

  138. Agarwal A, Desai KA (2021) Rigidity regulation approach for geometric tolerance optimization in end milling of thin-walled components. J Manuf Sci Eng 143(11):111006

  139. Tang AJ, Ma HL, Liu ZQ (2013) Elastic-plastic deformation of milling thin wall part. Appl Mech Mater 345:321–324

    Google Scholar 

  140. Aijun T, Zhanqiang L, Hailong M (2007) Modeling and simulation of big deformations of thin walled plate in end milling process. In: 2007 IEEE international conference on automation and logistics (Jinan, China), pp 2384–2388

  141. Aijun T, Zhanqiang L (2008) Deformations of thin-walled plate due to static end milling force. J Mater Process Technol 206(1):345–351

    Google Scholar 

  142. Ma H, Duan H, Tang A (2010) Modeling and simulation of deformation of milling thin-walled part. In: 2010 2nd International Asia conference on informatics in control, automation and robotics (CAR 2010) (Wuhan, China), pp 433–436

  143. Tang AJ, Liu ZQ (2011) Experiments and simulation of elastic-plastic deformation in thin wall part milling. Adv Mater Res 314–316:482–486

    Google Scholar 

  144. Wang J, Ibaraki S, Matsubara A, Shida K, Yamada T (2015) FEM-based simulation for workpiece deformation in thin-wall milling. Int J Autom Technol 9(2):122–128

    Google Scholar 

  145. Shi J, Gao J, Song Q, Liu Z, Wan Y (2017) Dynamic deformation of thin-walled plate with variable thickness under moving milling force. Proc CIRP 58:311–316

    Google Scholar 

  146. Agarwal A, Desai KA (2020) Effect of workpiece curvature on axial surface error profile in flat end-milling of thin-walled components. Proc Manuf 48:498–507

    Google Scholar 

  147. Agarwal A, Desai KA (2022) Effect of component configuration on geometric tolerances during end milling of thin-walled parts. Int J Adv Manuf Technol 118(11):3617–3630

    Google Scholar 

  148. Kang Y-G, Wang Z-Q (2013) Two efficient iterative algorithms for error prediction in peripheral milling of thin-walled workpieces considering the in-cutting chip. Int J Mach Tools Manuf 73:55–61

    Google Scholar 

  149. Bao Y, Kang R, Dong Z, Zhu X, Wang C, Guo D (2018) Model for surface topography prediction in mirror-milling of aircraft skin parts. Int J Adv Manuf Technol 95(5):2259–2268

    Google Scholar 

  150. Altintas Y, Tuysuz O, Habibi M, Li ZL (2018) Virtual compensation of deflection errors in ball end milling of flexible blades. CIRP Ann 67(1):365–368

    Google Scholar 

  151. Liu S, Jin S, Zhang X-P, Chen K, Tian A, Xi L-F (2019) A coupled model for the prediction of surface variation in face milling large-scale workpiece with complex geometry. J Manuf Sci Eng 141(3):031009

    Google Scholar 

  152. Wimmer S, Hunyadi P, Zaeh MF (2019) A numerical approach for the prediction of static surface errors in the peripheral milling of thin-walled structures. Prod Eng Res Dev 13(3):479–488

    Google Scholar 

  153. Liu S, Zhang X, Jin S, Tian A, Chen K, Xi L (2019) Prediction of surface variation field in face milling via finite element model updating with considering force-deformation coupling. Int J Adv Manuf Technol 105(10):4193–4209

    Google Scholar 

  154. Agarwal A, Desai KA (2020) Predictive framework for cutting force induced cylindricity error estimation in end milling of thin-walled components. Precis Eng 66:209–219

    Google Scholar 

  155. Kahya M, Ozbayoglu M, Unver HO (2021) Precision and energy-efficient ball-end milling of Ti6Al4V turbine blades using particle swarm optimization. Int J Comput Integr Manuf 34(2):110–133

    Google Scholar 

  156. Wu G, Li G, Pan W, Wang X, Ding S (2020) A prediction model for the milling of thin-wall parts considering thermal-mechanical coupling and tool wear. Int J Adv Manuf Technol 107(11):4645–4659

    Google Scholar 

  157. Wan M, Zhang WH, Tan G, Qin GH (2008) Systematic simulation procedure of peripheral milling process of thin-walled workpiece. J Mater Process Technol 197(1–3):122–131

    Google Scholar 

  158. Huang W-W, Zhang Y, Zhang X-Q, Zhu L-M (2020) Wall thickness error prediction and compensation in end milling of thin-plate parts. Precis Eng 66:550–563

    Google Scholar 

  159. Li W, Wang L, Yu G (2021) Force-induced deformation prediction and flexible error compensation strategy in flank milling of thin-walled parts. J Mater Process Technol 297:117258

    Google Scholar 

  160. Wang L, Li W, Yu G (2023) Optimal deformation error compensation process in flank milling of thin-walled workpieces. Int J Adv Manuf Technol 126(9):4353–4367

    Google Scholar 

  161. Wan M, Zhang W, Qiu K, Gao T, Yang Y (2005) Numerical prediction of static form errors in peripheral milling of thin-walled workpieces with irregular meshes. J Manuf Sci Eng 127:13–22

    Google Scholar 

  162. Wan M, Zhang WH (2006) Efficient algorithms for calculations of static form errors in peripheral milling. J Mater Process Technol 171(1):156–165

    Google Scholar 

  163. Sun Y, Jiang S (2018) Predictive modeling of chatter stability considering force-induced deformation effect in milling thin-walled parts. Int J Mach Tools Manuf 135:38–52

    Google Scholar 

  164. Zhu L, Zhang X, Zheng G, Ding H (2009) Analytical expression of the swept surface of a rotary cutter using the envelope theory of sphere congruence. J Manuf Sci Eng 131(4):041017

    Google Scholar 

  165. Li ZL, Tuysuz O, Zhu LM, Altintas Y (2018) Surface form error prediction in five-axis flank milling of thin-walled parts. Int J Mach Tools Manuf 128:21–32

    Google Scholar 

  166. Li Z-L, Zhu L-M (2019) Compensation of deformation errors in five-axis flank milling of thin-walled parts via tool path optimization. Precis Eng 55:77–87

    Google Scholar 

  167. Komiya K, Kaneko J, Yokoyama T, Asano T, Higashino C, Horio K (2017) Development of high-speed processing method to evaluate elastic deformations of workpieces. Int J Autom Technol 11(6):971–977

    Google Scholar 

  168. Wang J, Quan L, Tang K (2020) A prediction method based on the voxel model and the finite cell method for cutting force-induced deformation in the five-axis milling process. Comput Methods Appl Mech Eng 367:113110

    MathSciNet  Google Scholar 

  169. Tsai J-S, Liao C-L (1999) Finite-element modeling of static surface errors in the peripheral milling of thin-walled workpieces. J Mater Process Technol 94:235–246

    Google Scholar 

  170. Smaoui M, Bouaziz Z, Zghal A, Baili M, Dessein G (2012) Compensation of a ball end tool trajectory in complex surface milling. Int J Mach Mach Mater 11(1):51–68

    Google Scholar 

  171. Qi H, Tian Y, Zhang D (2013) Machining forces prediction for peripheral milling of low-rigidity component with curved geometry. Int J Adv Manuf Technol 64(9):1599–1610

    Google Scholar 

  172. Wang MY, Chang HY (2003) A simulation shape error for end milling AL6061-T6. Int J Adv Manuf Technol 22(9–10):689–696

    Google Scholar 

  173. Gang L (2009) Study on deformation of titanium thin-walled part in milling process. J Mater Process Technol 209(6):2788–2793

    Google Scholar 

  174. Farina S, Thepsonti T, Ceretti E, Özel T (2011) Determination of specific forces and tool deflections in micro-milling of Ti-6Al-4V alloy using finite element simulations and analysis. AIP Conf Proc 1353(1):645–650

    Google Scholar 

  175. Bhattacharya A, Bera TK, Thakur A (2015) On cutter deflection profile errors in end milling: modeling and experimental validation. Mater Manuf Processes 30(8):1042–1059

    Google Scholar 

  176. Jalili Saffar R, Razfar MR, Zarei O, Ghassemieh E (2008) Simulation of three-dimension cutting force and tool deflection in the end milling operation based on finite element method. Simul Model Pract Theory 16(10):1677–1688

    Google Scholar 

  177. Wang L, Chen ZC (2014) A new CAD/CAM/CAE integration approach to predicting tool deflection of end mills. Int J Adv Manuf Technol 72(9):1677–1686

    Google Scholar 

  178. Kivanc EB, Budak E (2003) Development of analytical endmill deflection and dynamics models. In: ASME international mechanical engineering congress and exposition (Washington, DC). American Society of Mechanical Engineers

  179. Gök A, Demirci HI, Gök K (2016) Determination of experimental, analytical, and numerical values of tool deflection at ball end milling of inclined surfaces. Proc Inst Mech Eng E J Process Mech Eng 230(2):111–119

    Google Scholar 

  180. Zeroudi N, Fontaine M (2015) Prediction of tool deflection and tool path compensation in ball-end milling. J Intell Manuf 26(3):425–445

    Google Scholar 

  181. Mallek R, Smaoui M, Baili M, Dessein G, Bouaziz Z (2021) A developed static model for tool deflection in ball-end milling. In: Kharrat M, Baccar M, Dammak F (eds) Advances in mechanical engineering, materials and mechanics. Springer International Publishing, Cham, pp 160–167

    Google Scholar 

  182. Ratchev S, Liu S, Becker AA (2005) Error compensation strategy in milling flexible thin-wall parts. J Mater Process Technol 162–163:673–681

    Google Scholar 

  183. Ning H, Zhigang W, Chengyu J, Bing Z (2003) Finite element method analysis and control stratagem for machining deformation of thin-walled components. J Mater Process Technol 139(1):332–336

    Google Scholar 

  184. Ratchev S, Liu SA, Huang W, Becker AA (2004) Milling error prediction and compensation in machining of low-rigidity parts. Int J Mach Tools Manuf 44:1629–1641

    Google Scholar 

  185. Ratchev S, Liu S, Huang W, Becker AA (2006) An advanced FEA based force induced error compensation strategy in milling. Int J Mach Tools Manuf 46(5):542–551

    Google Scholar 

  186. Bera TC, Desai KA, Rao PV (2011) Error compensation in flexible end milling of tubular geometries. J Mater Process Technol 211(1):24–34

    Google Scholar 

  187. Du Z, Zhang D, Hou H, Liang SY (2017) Peripheral milling force induced error compensation using analytical force model and APDL deformation calculation. Int J Adv Manuf Technol 88(9):3405–3417

    Google Scholar 

  188. Soori M, Arezoo B, Habibi M (2014) Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines. J Manuf Syst 33(4):498–507

    Google Scholar 

  189. Liu S, Zheng L, Zhang ZH, Wen DH (2006) Optimal fixture design in peripheral milling of thin-walled workpiece. Int J Adv Manuf Technol 28(7):653–658

    Google Scholar 

  190. Liu S-G, Zheng L, Zhang Z-H, Li Z-Z, Liu D-C (2007) Optimization of the number and positions of fixture locators in the peripheral milling of a low-rigidity workpiece. Int J Adv Manuf Technol 33(7):668–676

    Google Scholar 

  191. Koike Y, Matsubara A, Nishiwaki S, Izui K, Yamaji I (2012) Cutting path design to minimize workpiece displacement at cutting point: milling of thin-walled parts. Int J Autom Technol 6(5):638–647

    Google Scholar 

  192. Koike Y, Matsubara A, Yamaji I (2013) Design method of material removal process for minimizing workpiece displacement at cutting point. CIRP Ann 62(1):419–422

    Google Scholar 

  193. Wang J, Ibaraki S, Matsubara A (2017) A cutting sequence optimization algorithm to reduce the workpiece deformation in thin-wall machining. Precis Eng 50:506–514

    Google Scholar 

  194. Diez E, Leal-Muñoz E, Perez H, Vizan A (2017) Dynamic analysis of a piezoelectric system to compensate for workpiece deformations in flexible milling. Mech Syst Signal Process 91:278–294

    Google Scholar 

  195. Liu S, Afazov S, Becker A, Ratchev S (2023) Machining error prediction scheme aided smart fixture development in machining of a Ti6Al4V slender part. Proc Inst Mech Eng B J Eng Manuf 237(10):1509–1517

    Google Scholar 

  196. Khusainov RM, Sabirov AR, Lozinsky VV (2021) Reduction of static elastic displacements during processing on vertical milling machines. In: Radionov AA, Gasiyarov VR (eds) Proceedings of the 6th international conference on industrial engineering (ICIE 2020). Springer International Publishing, Cham, pp 419–425

    Google Scholar 

  197. Hou Y, Zhang D, Zhang Y, Wu B (2021) The variable radial depth of cut in finishing machining of thin-walled blade based on the stable-state deformation field. Int J Adv Manuf Technol 113(1):141–158

    Google Scholar 

  198. Xiao LH, Tang L, Hu ZH, Kuang Z, Yuan JL (2011) The deformation analysis for flank milling of thin-walled rectangle plate based on flexible force model. Adv Mater Res 189–193:1555–1561

    Google Scholar 

  199. Li Y, Cheng X, Ling S, Zheng G (2021) On-line compensation for micromilling of high-aspect-ratio straight thin walls. Micromachines 12(6):603

    Google Scholar 

  200. Ma J, He G, Liu Z, Qin F, Chen S, Zhao X (2018) Instantaneous cutting-amount planning for machining deformation homogenization based on position-dependent rigidity of thin-walled surface parts. J Manuf Process 34:401–411

    Google Scholar 

  201. Gui H, Zhang L, Yan Y (2022) Adaptive tool path generation for flank milling of thin-walled parts based on force-induced deformation constraints. Int J Adv Manuf Technol 119(5):3631–3646

    Google Scholar 

  202. Soori M (2023) Deformation error compensation in 5-axis milling operations of turbine blades. J Braz Soc Mech Sci Eng 45(6):289

    Google Scholar 

  203. Salisbury JK (1980) Active stiffness control of a manipulator in cartesian coordinates. In: 1980 19th IEEE conference on decision and control including the symposium on adaptive processes (Albuquerque, New Mexico, USA), pp 95–100

  204. Chen S-F, Kao I (2000) Conservative congruence transformation for joint and cartesian stiffness matrices of robotic hands and fingers. Int J Robot Res 19(9):835–847

    Google Scholar 

  205. Alici G, Shirinzadeh B (2005) Enhanced stiffness modeling, identification and characterization for robot manipulators. IEEE Trans Rob 21(4):554–564

    Google Scholar 

  206. Dumas C, Caro S, Garnier S, Furet B (2011) Joint stiffness identification of six-revolute industrial serial robots. Robot Comput Integr Manuf 27(4):881–888

    Google Scholar 

  207. Cvitanic T, Nguyen V, Melkote SN (2020) Pose optimization in robotic machining using static and dynamic stiffness models. Robot Comput Integr Manuf 66:101992

    Google Scholar 

  208. Klimchik A, Furet B, Caro S, Pashkevich A (2015) Identification of the manipulator stiffness model parameters in industrial environment. Mech Mach Theory 90:1–22

    Google Scholar 

  209. Piras G, Cleghorn WL, Mills JK (2005) Dynamic finite-element analysis of a planar high-speed, high-precision parallel manipulator with flexible links. Mech Mach Theory 40(7):849–862

    Google Scholar 

  210. Klimchik A, Pashkevich A, Chablat D (2013) CAD-based approach for identification of elasto-static parameters of robotic manipulators. Finite Elem Anal Des 75:19–30

    MathSciNet  Google Scholar 

  211. Klimchik A, Pashkevich A, Chablat D (2019) Fundamentals of manipulator stiffness modeling using matrix structural analysis. Mech Mach Theory 133:365–394

    Google Scholar 

  212. Lin Y, Zhao H, Ding H (2018) Spindle configuration analysis and optimization considering the deformation in robotic machining applications. Robot Comput Integr Manuf 54:83–95

    Google Scholar 

  213. Wang W, Guo Q, Yang Z, Jiang Y, Xu J (2023) A state-of-the-art review on robotic milling of complex parts with high efficiency and precision. Robot Comput Integr Manuf 79:102436

    Google Scholar 

  214. Tyapin I, Kaldestad KB, Hovland G (2015) Off-line path correction of robotic face milling using static tool force and robot stiffness. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (Hamburg, Germany), pp 5506–5511

  215. Cortsen J, Petersen HG (2012) Advanced off-line simulation framework with deformation compensation for high speed machining with robot manipulators. In: 2012 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), (Kaohsiung, Taiwan), pp 934–939

  216. Denkena B, Lepper T (2015) Enabling an industrial robot for metal cutting operations. Proc CIRP 35:79–84

    Google Scholar 

  217. Sörnmo O, Olofsson B, Robertsson A, Johansson R (2012) Increasing time-efficiency and accuracy of robotic machining processes using model-based adaptive force control. IFAC Proc Vol 45(22):543–548

    Google Scholar 

  218. Ding Y, Zhu L (2018) Investigation on chatter stability of thin-walled parts considering its flexibility based on finite element analysis. Int J Adv Manuf Technol 94(9):3173–3187

    Google Scholar 

  219. Yan B, Zhu L (2019) Research on milling stability of thin-walled parts based on improved multi-frequency solution. Int J Adv Manuf Technol 102(1):431–441

    Google Scholar 

  220. Kersting P, Biermann D (2012) Modeling workpiece dynamics using sets of decoupled oscillator models. Mach Sci Technol 16(4):564–579

    Google Scholar 

  221. Liu Y, Zhao C, Cui N, Yan X, Chen Y, Liang H, Cai X, Shan Y, Bao K (2023) Research on the stability analysis of milling of thin-walled parts based on the dynamic characteristics. J Strain Anal Eng Design 58:316–331

    Google Scholar 

  222. Schmitz TL, Donalson RR (2000) Predicting high-speed machining dynamics by substructure analysis. CIRP Ann 49(1):303–308

    Google Scholar 

  223. Burns TJ, Schmitz TL (2005) A study of linear joint and tool models in spindle-holder-tool receptance coupling, In: International design engineering technical conferences and computers and information in engineering conference, vol 6 (Long Beach, California, USA)

  224. Schmitz TL, Duncan GS (2005) Three-component receptance coupling substructure analysis for tool point dynamics prediction. J Manuf Sci Eng 127(4):781–790

    Google Scholar 

  225. Lei Y, Hou T, Ding Y (2023) Prediction of the posture-dependent tool tip dynamics in robotic milling based on multi-task Gaussian process regressions. Robot Comput Integr Manuf 81:102508

    Google Scholar 

  226. Schmitz T, Betters E, Budak E, Yüksel E, Park S, Altintas Y (2023) Review and status of tool tip frequency response function prediction using receptance coupling. Precis Eng 79:60–77

    Google Scholar 

  227. Peigné G, Paris H, Brissaud D (2003) A model of milled surface generation for time domain simulation of high-speed cutting. Proc Inst Mech Eng B J Eng Manuf 217(7):919–930

    Google Scholar 

  228. Honeycutt A, Schmitz TL (2017) Surface location error and surface roughness for period-N milling bifurcations. J Manuf Sci Eng 139(6):061010

    Google Scholar 

  229. Schmitz TL, Couey J, Marsh E, Mauntler N, Hughes D (2007) Runout effects in milling: surface finish, surface location error, and stability. Int J Mach Tools Manuf 47:841–851

    Google Scholar 

  230. Zhenyu S, Luning L, Zhanqiang L (2015) Influence of dynamic effects on surface roughness for face milling process. Int J Adv Manuf Technol 80(9):1823–1831

    Google Scholar 

  231. Jin S, Liu S, Zhang X, Chen K (2019) A unified prediction model of 3D surface topography in face milling considering multi-error sources. Int J Adv Manuf Technol 102(1):705–717

    Google Scholar 

  232. Insperger T, Gradišek J, Kalveram M, Stépán G, Winert K, Govekar E (2006) Machine tool chatter and surface location error in milling processes. J Manuf Sci Trans ASME 128(4):913–920

    Google Scholar 

  233. Kiss AK, Bachrathy D (2015) Explicit model of cumulative surface location error for milling processes. In: 12th Hungarian conference on theoretical and applied mechanics (Miskolc, Hungary)

  234. Kiss AK, Bachrathy D, Stepan G (2016) Cumulative surface location error for milling processes based on tool-tip frequency response function. Proc CIRP 46:323–326

    Google Scholar 

  235. Wang D, Wang X, Liu Z, Gao P, Ji Y, Löser M, Ihlenfeldt S (2018) Surface location error prediction and stability analysis of micro-milling with variation of tool overhang length. Int J Adv Manuf Technol 99(1–4):919–936

    Google Scholar 

  236. Berglind L, Ozturk E (2019) Modelling of machining processes. Springer International Publishing, Cham, pp 57–93

    Google Scholar 

  237. Sun C, Kengne PLF, Barrios A, Mata S, Ozturk E (2019) Form error prediction in robotic assisted milling. Proc CIRP 82:491–496

    Google Scholar 

  238. Wang D (2021) The comprehensive analysis of milling stability and surface location error with considering the dynamics of workpiece. Doctoral thesis, Technische Universität Dresden

  239. Tang Y, Zhang J, Tian H, Liu H, Zhao W (2023) Optimization method of spindle speed with the consideration of chatter and forced vibration for five-axis flank milling. Int J Adv Manuf Technol 125(7):3159–3169

    Google Scholar 

  240. Tang Y, Zhang J, Hu W, Liu H, Zhao W (2023) Prediction of surface location error considering the varying dynamics of thin-walled parts during five-axis flank milling. Proc 11(1):242

    Google Scholar 

  241. Fei J, Lin B, Xiao J, Ding M, Yan S, Zhang X, Zhang J (2018) Investigation of moving fixture on deformation suppression during milling process of thin-walled structures. J Manuf Process 32:403–411

    Google Scholar 

  242. Yao Q, Luo M, Zhang D, Wu B (2018) Identification of cutting force coefficients in machining process considering cutter vibration. Mech Syst Signal Process 103:39–59

    Google Scholar 

  243. Henninger C, Eberhard P (2008) Analysis of dynamic stability for milling processes with varying workpiece dynamics. Proc Appl Math Mech 8(1):10367–10368

    Google Scholar 

  244. Baumann M, Eberhard P (2017) Interpolation-based parametric model order reduction for material removal in elastic multibody systems. Multibody Sys Dyn 39(1):21–36

    MathSciNet  Google Scholar 

  245. Wan X-J, Zhang Y, Huang X-D (2013) Investigation of influence of fixture layout on dynamic response of thin-wall multi-framed work-piece in machining. Int J Mach Tools Manuf 75:87–99

    Google Scholar 

  246. Kalinski KJ, Galewski MA (2015) Optimal spindle speed determination for vibration reduction during ball-end milling of flexible details. Int J Mach Tools Manuf 92:19–30

    Google Scholar 

  247. Surmann T, Enk D (2007) Simulation of milling tool vibration trajectories along changing engagement conditions. Int J Mach Tools Manuf 47(9):1442–1448

    Google Scholar 

  248. Biermann D, Kersting P, Surmann T (2010) A general approach to simulating workpiece vibrations during five-axis milling of turbine blades. CIRP Ann 59(1):125–128

    Google Scholar 

  249. Kersting P, Biermann D (2012) Modeling techniques for the prediction of workpiece deflections in NC milling. Proc CIRP 2:83–86

    Google Scholar 

  250. Kersting P, Biermann D (2014) Modeling techniques for simulating workpiece deflections in NC milling. CIRP J Manuf Sci Technol 7(1):48–54

    Google Scholar 

  251. Odendahl S, Kersting P (2013) Higher efficiency modeling of surface location errors by using a multi-scale milling simulation. Proc CIRP 9:18–22

    Google Scholar 

  252. Siebrecht T, Kersting P, Biermann D, Odendahl S, Bergmann J (2015) Modeling of surface location errors in a multi-scale milling simulation system using a tool model based on triangle meshes. Proc CIRP 37:188–192

    Google Scholar 

  253. Kiss AK, Bachrathy D, Stepan G (2019) Effects of varying dynamics of flexible workpieces in milling operations. J Manuf Sci Eng 142(1):011005

    Google Scholar 

  254. Tang ZT, Yu T, Xu LQ, Liu ZQ (2013) Machining deformation prediction for frame components considering multifactor coupling effects. Int J Adv Manuf Technol 68(1):187–196

    Google Scholar 

  255. Yuan X, Wang S, Mao X, Liu H, Liang Z, Guo Q, Yan R (2022) Forced vibration mechanism and suppression method for thin-walled workpiece milling. Int J Mech Sci 230:107553

    Google Scholar 

  256. Ozoegwu CG, Eberhard P (2020) Stability analysis of multi-discrete delay milling with helix effects using a general order full-discretization method updated with a generalized integral quadrature. Mathematics 8(6):1003

    Google Scholar 

  257. Schmitz TL, Bayly PV, Soons JA, Dutterer B (2001) Prediction of surface location error by time finite element analysis and Euler integration. In: Proceedings of the 17th annual ASPE meeting, October 20–25, vol 1, (St. Louis, MO), pp 132–137

  258. Mann BP, Bartow MJ, Young KA, Bayly PV, Schmitz TL (2003) Machining accuracy due to tool or workpiece vibrations. In: Proceedings of the ASME 2003 international mechanical engineering congress and exposition, (Washington, DC), ASME, pp 55–62

  259. Mann BP, Bayly PV, Davies MA, Halley JE (2004) Limit cycles, bifurcations, and accuracy of the milling process. J Sound Vib 277(1–2):31–48

    Google Scholar 

  260. Mann BP, Edes BT, Easley SJ, Young KA, Ma K (2008) Chatter vibration and surface location error prediction for helical end mills. Int J Mach Tools Manuf 48(3–4):350–361

    Google Scholar 

  261. Ding Y, Zhu L, Zhang X, Ding H (2011) On a numerical method for simultaneous prediction of stability and surface location error in low radial immersion milling. J Dyn Syst Measure Control Trans ASME 133(2):024503

    Google Scholar 

  262. Li Z, Jiang S, Sun Y (2019) Chatter stability and surface location error predictions in milling with mode coupling and process damping. Proc Inst Mech Eng B J Eng Manuf 233(3):686–698

    Google Scholar 

  263. Dai Y, Li H, Yao J, Liu S (2019) A novel approach with time-invariant transition matrix for surface location error prediction in low radial immersion milling. Int J Adv Manuf Technol 101(5–8):1267–1274

    Google Scholar 

  264. Niu J, Jia J, Sun Y, Guo D (2020) Generation mechanism and quality of milling surface profile for variable pitch tools considering runout. J Manuf Sci Eng 142(12):121001

    Google Scholar 

  265. Niu J, Jia J, Wang R, Xu J, Sun Y, Guo D (2021) State dependent regenerative stability and surface location error in peripheral milling of thin-walled parts. Int J Mech Sci 196:106294

    Google Scholar 

  266. Dai Y, Li H, Cao H, Huang Z, Yang C, Wang C (2022) A novel Gegenbauer wavelet-based approach for stability and surface location error analyses of milling process. J Dyn Syst Measure Control 144(10)

  267. Wang D, Löser M, Luo Y, Ihlenfeldt S, Wang X, Liu Z (2020) Prediction of cumulative surface location error at the contact zone of in-process workpiece and milling tool. Int J Mech Sci 177:105543

    Google Scholar 

  268. Dai Y, Li H, Peng D, Fan Z, Yang G (2022) A novel scheme with high accuracy and high efficiency for surface location error prediction. Int J Adv Manuf Technol 118(3–4):1317–1333

    Google Scholar 

  269. Shi DM, Huang T, Zhang XM, Ding H (2022) An explicit coupling model for accurate prediction of force-induced deflection in thin-walled workpiece milling. J Manuf Sci Trans ASME 144(8):081005–1

    Google Scholar 

  270. Hou T, Lei Y, Ding Y (2023) Pose optimization in robotic milling based on surface location error. J Manuf Sci Eng 145(8):084501

    Google Scholar 

  271. Ozoegwu CG, Eberhard P (2023) Closed-form time-domain solutions of arbitrary-DOF forced vibrations and of surface location error for general-helix cylindrical milling tools. J Vibr Eng Technol. https://doi.org/10.1007/s42417-023-01064-7

    Article  Google Scholar 

  272. Junz Wang JJ, Wang, Huang CY (2003) A pole/zero placement approach to reducing structure vibration in end milling. In: ASME international mechanical engineering congress and exposition (Washington, DC). American Society of Mechanical Engineers

  273. Junz Wang JJ, Liang SY, Book WJ (1994) Convolution analysis of milling force pulsation. J Manuf Sci Trans ASME 116(1):17–25

    Google Scholar 

  274. Kiran K, Rubeo M, Kayacan MC, Schmitz T (2017) Two degree of freedom frequency domain surface location error prediction. Precis Eng 48:234–242

    Google Scholar 

  275. Budak E, Altintas Y (1998) Analytical prediction of chatter stability in milling-part I: general formulation. J Dyn Syst Meas Control 120(1):22–30

    Google Scholar 

  276. Altintas Y (2012) Manufacturing automation, 2nd edn. Cambridge University Press, New York

    Google Scholar 

  277. Morelli L, Grossi N, Campatelli G, Scippa A (2022) Surface location error prediction in 2.5-axis peripheral milling considering tool dynamic stiffness variation. Precis Eng 76:95–109

    Google Scholar 

  278. Heisel U, Milberg J (1994) Vibrations and surface generation in slab milling. CIRP Ann 43(1):337–340

    Google Scholar 

  279. Lo C-C, Hsiao C-Y (1998) CNC machine tool interpolator with path compensation for repeated contour machining. Comput Aided Des 30(1):55–62

    Google Scholar 

  280. Han B, Ren CZ, Yang XY, Chen G (2012) Experiment study on deflection of aluminum alloy thin-wall workpiece in milling process. Mater Sci Forum 697–698:129–132

    Google Scholar 

  281. Nguyen HT, Wang H, Hu SJ (2012) Characterization of cutting force induced surface shape variation using high-definition metrology. In: Proceedings of the ASME 2012 international manufacturing science and engineering conference collocated with the 40th North American manufacturing research conference and in participation with the international conference on tribology materials and process (Notre Dame, Indiana, USA). ASME, pp 641–650

  282. Nguyen HT, Wang H, Hu SJ (2013) Characterization of cutting force induced surface shape variation in face milling using high-definition metrology. J Manuf Sci Eng 135(4):041014

    Google Scholar 

  283. Nguyen HT, Wang H, Hu SJ (2014) Modeling cutter tilt and cutter-spindle stiffness for machine condition monitoring in face milling using high-definition surface metrology. Int J Adv Manuf Technol 70(5):1323–1335

    Google Scholar 

  284. Li G, Du S, Wang B, Lv J, Deng Y (2021) High definition metrology-based quality improvement of surface texture in face milling of workpieces with discontinuous surfaces. J Manuf Sci Eng 144(3):031001

    Google Scholar 

  285. Nguyen HT, Wang H, Tai BL, Ren J, Jack Hu S, Shih A (2015) High-definition metrology enabled surface variation control by cutting load balancing. J Manuf Sci Eng 138(2):1

    Google Scholar 

  286. de Aguiar MM, Diniz AE, Pederiva R (2013) Correlating surface roughness, tool wear and tool vibration in the milling process of hardened steel using long slender tools. Int J Mach Tools Manuf 68:1–10

    Google Scholar 

  287. Diez E, Perez H, Marquez J, Vizan A (2015) Feasibility study of in-process compensation of deformations in flexible milling. Int J Mach Tools Manuf 94:1–14

    Google Scholar 

  288. Abele E, Schützer K, Bauer J, Pischan M (2012) Tool path adaption based on optical measurement data for milling with industrial robots. Prod Eng Res Dev 6(4):459–465

    Google Scholar 

  289. Barnfather JD, Goodfellow MJ, Abram T (2016) Development and testing of an error compensation algorithm for photogrammetry assisted robotic machining. Measurement 94:561–577

    Google Scholar 

  290. Huang N, Bi Q, Wang Y, Sun C (2014) 5-Axis adaptive flank milling of flexible thin-walled parts based on the on-machine measurement. Int J Mach Tools Manuf 84:1–8

    Google Scholar 

  291. Wang L, Ding H, Feng J, Wang S, Xiao A, Koch D (2010) Implementation of integrated manufacturing of free-form surfaces. In: 2010 International conference on digital manufacturing & automation, (Changcha, China), vol 1, pp 830–833

  292. Denkena B, Bergmann B, Kaiser S (2022) Close-to-process compensation of geometric deviations on implants based on optical measurement data. Proc CIRP 112:122–127

    Google Scholar 

  293. Khan AW, Chen W, Wu L (2009) Machine tools error characterization and compensation by on-line measurement of artifact. In: Ye S, Zhang G, Ni J (eds) 2009 International conference on optical instruments and technology: optoelectronic measurement technology and systems (Shanghai, China), vol 7511. International Society for Optics and Photonics, SPIE, p 75110K

  294. David C, Sagris D, Stergianni E, Tsiafis C, Tsiafis I (2018) Experimental analysis of the effect of vibration phenomena on workpiece topomorphy due to cutter runout in end-milling process. Machines 6(3):27

    Google Scholar 

  295. Bolar G, Mekonen M, Das A, Joshi SN (2018) Experimental investigation on surface quality and dimensional accuracy during curvilinear thin-wall machining. Mater Today Proc 5(2, Part 1):6461–6469

    Google Scholar 

  296. Guiassa R, Mayer JRR (2011) Predictive compliance based model for compensation in multi-pass milling by on-machine probing. CIRP Ann 60(1):391–394

    Google Scholar 

  297. Izamshah R, Zulhairy M, Kasim MS, Hadzley M, Amran M, Amri M, Sivaraos (2014) Cutter path strategies for shoulder milling of thin deflecting walls. Adv Mater Res 903:175–180

    Google Scholar 

  298. Ma W, He G, Han J, Xie Q (2020) Error compensation for machining of sculptured surface based on on-machine measurement and model reconstruction. Int J Adv Manuf Technol 106(7):3177–3187

    Google Scholar 

  299. Gopinath L, Jerome S, Gopalsamy B (2021) Mitigation of cutting point deviation by generating provisional corrugations during milling of thin walls. Mach Sci Technol 25(6):984–1009

    Google Scholar 

  300. Liu C, Li Y, Shen W (2018) A real time machining error compensation method based on dynamic features for cutting force induced elastic deformation in flank milling. Mach Sci Technol 22(5):766–786

    Google Scholar 

  301. Wang X, Li Z, Bi Q, Zhu L, Ding H (2019) An accelerated convergence approach for real-time deformation compensation in large thin-walled parts machining. Int J Mach Tools Manuf 142:98–106

    Google Scholar 

  302. Zha J, Villarrazo N, Martínez de Pisson G, Li Y, Zhang H, López de Lacalle LN (2023) An accuracy evolution method applied to five-axis machining of curved surfaces. Int J Adv Manuf Technol 125(7):3475–3487

    Google Scholar 

  303. Kolluru K, Axinte D (2013) Coupled interaction of dynamic responses of tool and workpiece in thin wall milling. J Mater Process Technol 213(9):1565–1574

    Google Scholar 

  304. Luo M, Liu D, Luo H (2016) Real-time deflection monitoring for milling of a thin-walled workpiece by using PVDF thin-film sensors with a cantilevered beam as a case study. Sensors 16(9):1470

    Google Scholar 

  305. Denkena B, Litwinski KM, Boujnah H (2016) Detection of tool deflection in milling by a sensory axis slide for machine tools. Mechatronics 34:95–99

    Google Scholar 

  306. Denkena B, Dahlmann D, Boujnah H (2017) Tool deflection control by a sensory spindle slide for milling machine tools. Proc CIRP 62:329–334

    Google Scholar 

  307. Cho MW, Seo TI (2002) Machining error compensation using radial basis function network based on CAD/CAM/CAI integration concept. Int J Prod Res 40(9):2159–2174

    Google Scholar 

  308. Cho M-W, Kim G-H, Seo T-I, Hong Y-C, Cheng HH (2006) Integrated machining error compensation method using OMM data and modified PNN algorithm. Int J Mach Tools Manuf 46(12):1417–1427

    Google Scholar 

  309. Du S-C, Huang D-L, Wang H (2015) An adaptive support vector machine-based workpiece surface classification system using high-definition metrology. IEEE Trans Instrum Meas 64(10):2590–2604

    Google Scholar 

  310. Pathak VK, Singh AK, Singh R, Chaudhary H (2017) A modified algorithm of particle swarm optimization for form error evaluation. TM Techn Messen 84(4):272–292

    Google Scholar 

  311. Denkena B, Dittrich M-A, Uhlich F (2016) Augmenting milling process data for shape error prediction. Proc CIRP 57:487–491

    Google Scholar 

  312. Dittrich M-A, Uhlich F, Denkena B (2019) Self-optimizing tool path generation for 5-axis machining processes. CIRP J Manuf Sci Technol 24:49–54

    Google Scholar 

  313. Bagci E, Yüncüolu EU (2017) The effects of milling strategies on forces, material removal rate, tool deflection, and surface errors for the rough machining of complex surfaces. Strojniski Vestnik/J Mech Eng 63(11):643–656

    Google Scholar 

  314. Lasemi A, Xue D, Gu P (2014) Tool path re-planning in free-form surface machining for compensation of process-related errors. Int J Prod Res 52(20):5913–5931

    Google Scholar 

  315. Lin Y-C, Wu K-D, Shih W-C, Hsu P-K, Hung J-P (2020) Prediction of surface roughness based on cutting parameters and machining vibration in end milling using regression method and artificial neural network. Appl Sci 10(11):3941

    Google Scholar 

  316. Bai L, Xu F, Chen X, Su X, Lai F, Xu J (2022) A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of thin-walled structural components. Front Mech Eng 17(3):32

    Google Scholar 

  317. Du S, Xi L (2019) Surface prediction. Springer Singapore, Singapore, pp 265–291

    Google Scholar 

  318. Lechniak Z, Werner A, Skalski K, Kedzior K (1998) Methodology of off-line software compensation for errors in the machining process on the CNC machine tool. J Mater Process Technol 76(1–3):42–48

    Google Scholar 

  319. Cho MW, Seo TI, Kwon HD (2003) Integrated error compensation method using OMM system for profile milling operation. J Mater Process Technol 136(1–3):88–99

    Google Scholar 

  320. Gok A, Gologlu C, Demirci HI (2013) Cutting parameter and tool path style effects on cutting force and tool deflection in machining of convex and concave inclined surfaces. Int J Adv Manuf Technol 69(5):1063–1078

    Google Scholar 

  321. Sheth S, George PM (2016) Experimental investigation and prediction of flatness and surface roughness during face milling operation of WCB material. Proc Technol 23:344–351

    Google Scholar 

  322. Mikó B, Rácz G (2019) Investigation of flatness and angularity in case of ball-end milling. In: Durakbasa NM, Gencyilmaz MG (eds) Proceedings of the international symposium for production research 2018. Springer International Publishing, Cham, pp 65–72

    Google Scholar 

  323. Lai JT, Yao XH, Yu WL, Fu JZ (2013) Machined NURBS surface description using on-machine probing data. Adv Mater Res 819:33–37

    Google Scholar 

  324. Lai J, Fu J, Wang Y, Shen H, Xu Y, Chen Z (2015) A novel method of efficient machining error compensation based on NURBS surface control points reconstruction. Mach Sci Technol 19(3):499–513

    Google Scholar 

  325. Lee WC, tzu Lee Y, Wei CC (2019) Automatic error compensation for free-form surfaces by using on-machine measurement data. Appl Sci 9(15):3073

    Google Scholar 

  326. Yao Y, Zhao H, Wang X, Zou S (2015) A method of quality control for globoidal cam machining based on multi-variable mirror compensation. In: 2015 IEEE 7th international conference on cybernetics and intelligent systems (CIS) and IEEE conference on robotics, automation and mechatronics (RAM) (Siem Reap, Cambodia), pp 137–141

  327. Lang A, Song Z, He G, Sang Y (2017) Profile error evaluation of free-form surface using sequential quadratic programming algorithm. Precis Eng 47:344–352

    Google Scholar 

  328. Biermann D, Sacharow A, Surmann T, Wagner T (2010) Direct free-form deformation of NC programs for surface reconstruction and form-error compensation. Prod Eng Res Dev 4(5):501–507

    Google Scholar 

  329. Guiassa R, Mayer JRR, Balazinski M, Engin S, Delorme F-E (2014) Closed door machining error compensation of complex surfaces using the cutting compliance coefficient and on-machine measurement for a milling process. Int J Comput Integr Manuf 27(11):1022–1030

    Google Scholar 

  330. Li J, Ren J, Ren M, Zheng Y, Le X (2022) Neural process enhanced machining error evaluation for coordinate measuring machines. IEEE Trans Instrum Meas 71:1–12

    Google Scholar 

  331. Wang G, Yang X, Wang Z (2018) On-line deformation monitoring of thin-walled parts based on least square fitting method and lifting wavelet transform. Int J Adv Manuf Technol 94(9):4237–4246

    Google Scholar 

  332. Poniatowska M (2015) Free-form surface machining error compensation applying 3D CAD machining pattern model. Comput Aided Des 62:227–235

    Google Scholar 

  333. Chen Y, Gao J, Deng H, Zheng D, Chen X, Kelly R (2013) Spatial statistical analysis and compensation of machining errors for complex surfaces. Precis Eng 37(1):203–212

    Google Scholar 

  334. Bi Q, Wang X, Wu Q, Zhu L, Ding H (2019) Fv-SVM-based wall-thickness error decomposition for adaptive machining of large skin parts. IEEE Trans Ind Inf 15(4):2426–2434

    Google Scholar 

  335. Chen Y, Tang H, Tang Q, Zhang A, Chen D, Li K (2018) Machining error decomposition and compensation of complicated surfaces by EMD method. Measurement 116:341–349

    Google Scholar 

  336. Chen Y, Xu J, Tang Q (2021) Decomposition of machining error for surfaces using complete ensemble empirical mode decomposition with adaptive noise. Int J Comput Integr Manuf 34(10):1049–1066

    Google Scholar 

  337. Hao X, Li Y, Deng T, Liu C, Xiang B (2019) Tool path transplantation method for adaptive machining of large-sized and thin-walled free form surface parts based on error distribution. Robot Comput Integr Manuf 56:222–232

    Google Scholar 

  338. Hou Y, Zhang D, Mei J, Zhang Y, Luo M (2019) Geometric modelling of thin-walled blade based on compensation method of machining error and design intent. J Manuf Process 44:327–336

    Google Scholar 

  339. Chen Y, Dong F (2013) Robot machining: recent development and future research issues. Int J Adv Manuf Technol 66(9):1489–1497

    Google Scholar 

  340. Ozoegwu CG (2020) Polynomial tensor-based stability identification of milling process: application to reduced thin-walled workpiece. In: Fehr J, Haasdonk B (eds) IUTAM symposium on model order reduction of coupled systems, Stuttgart, Germany, May 22–25, 2018. Springer International Publishing, Cham, pp 209–220

  341. Ozoegwu CG, Eberhard P (2021) Automated upgraded generalized full-discretization method: Application to the stability study of a thin walled milling process. In: Dixit U, Dwivedy S (eds) Mechanical sciences: the way forward, chap 4. Springer Nature, Singapore, pp 83–104

    Google Scholar 

  342. Liang SY, Zheng L (1998) Analysis of end milling surface error considering tool compliance. J Manuf Sci Eng 120(1):207–210

    Google Scholar 

  343. Izamshah R, Yuhazri M, Hadzley M, Ali MA, Sivarao S (2013) Effects of end mill helix angle on accuracy for machining thin-rib aerospace component. Appl Mech Mater 315:773–777

    Google Scholar 

  344. Susemihl S, Brillinger B, Stürmer S, Hansen H, Boehlmann C, Kothe S, Wollnack J, Hintze W (2017) Referencing strategies for high accuracy machining of large aircraft components with mobile robotic systems. SAE technical paper, p 2166

  345. Ozoegwu CG (2023) A generalized closed-form model of cutting energy for arbitrary-helix cylindrical milling tools and its applications. Proc Inst Mech Eng B J Eng Manuf. https://doi.org/10.1177/09544054231202084

    Article  Google Scholar 

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Funding

The described research was done while Chigbogu Ozoegwu visited the ITM at the University of Stuttgart from 2022 to 2023. This stay was funded by the Alexander von Humboldt Foundation, grant number NGA 1195081 GF-E. This support is highly appreciated.

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  1. Department of Mechanical Engineering, University of Nigeria, Nsukka, Nigeria

    Chigbogu Ozoegwu

  2. Institute of Engineering and Computational Mechanics, University of Stuttgart, Stuttgart, Germany

    Peter Eberhard

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Ozoegwu, C., Eberhard, P. A Literature Review on Prediction Methods for Forced Responses and Associated Surface Form/Location Errors in Milling. J. Vib. Eng. Technol. 12, 5905–5934 (2024). https://doi.org/10.1007/s42417-023-01227-6

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