Thin-walled blades are critical components in high-end equipment such as aero-engines. Due to their complex geometric profiles and low structural stiffness, they are prone to deformation and contour errors during machining, which directly affect equipment performance and service life. To effectively control such errors, this paper proposes an integrated methodology framework for machining deformation control of thin-walled blades, covering the complete process of “error detection-compensation-verification.” This approach integrates theoretical analysis with experimental validation, systematically analyzing deformation mechanisms and considering the influence of milling force-induced plastic deformation and tool elastic displacement. Building on this analysis, this study proposes a hierarchical compensation strategy utilizing point cloud registration technology, which involves local error compensation followed by global deviation compensation. Experimental results show that the proposed method can significantly improve blade machining accuracy: the average deviation was reduced from −0.0085 to −0.0013 mm, and the standard deviation decreased from 0.032 to 0.010 mm. The findings indicate that this method exhibits certain feasibility and effectiveness in suppressing machining deformation of thin-walled blades, providing theoretical reference and practical guidance for deformation control of high-precision curved-surface components.
LiWRenJShiK, et al. A method for monitoring machining errors of complex thin-walled parts based on the fusion of physical information and CNN. J Manuf Syst2025; 81: 208–221. https://doi.org/10.1016/j.jmsy.2025.05.017
2.
YinXZhaoSLiuY, et al. Evaluation of profile accuracy and surface integrity for Inconel 718 blade machined by ultrasonic peening milling. J Manuf Process2023; 104: 150–163. https://doi.org/10.1016/j.jmapro.2023.09.005
3.
BoPXueSXuX, et al. Initialization of cutting tools and milling paths for 5-axis CNC flank milling of freeform surfaces. Graph Models2025; 139: 101264. https://doi.org/10.1016/j.gmod.2025.101264
4.
MorelliLCaldiniFSanz-CalleM, et al. Static deflection of cantilever thin wall workpieces in peripheral milling: an analytical model. J Manuf Process2025; 143: 369–386. https://doi.org/10.1016/j.jmapro.2025.04.029
NiuXChenZJingL, et al. Effect of cryogenic treatment on residual stress and microstructure of 6061 aluminum alloy and optimization of parameters. Materials2024; 17: 4873. https://doi.org/10.3390/ma17194873
7.
HuangKYiYPHuangSQ, et al. Effect of quenching cooling rate on residual stress and microstructure evolution of 6061 aluminum alloy. J Central South Univ2024; 31: 2167–2180. https://doi.org/10.1007/s11771-024-5705-5
8.
ZhaoSZhangHPengF, et al. United optimization strategy of ultrasonic vibration assisted process and multiple parameters for machining deformation reduction. J Manuf Process2024; 131: 1942–1958. https://doi.org/10.1016/j.jmapro.2024.09.111
9.
LiZZengZYangY, et al. Research progress in machining technology of aerospace thin-walled components. J Manuf Process2024; 119: 463–482. https://doi.org/10.1016/j.jmapro.2024.03.111
10.
HouYZhangDMeiJ, et al. Geometric modelling of thin-walled blade based on compensation method of machining error and design intent. J Manuf Process2019; 44: 327–336. https://doi.org/10.1016/j.jmapro.2019.06.012
LiuYZhaoCCuiN, et al. Research on the stability analysis of milling of thin-walled parts based on the dynamic characteristics. J Strain Anal Eng Des2023; 58: 316–331. https://doi.org/10.1177/03093247221113231
13.
DariuszSAndrzejBKrzysztofK, et al. Device for contact measurement of turbine blade geometry in robotic grinding process. Sensors2020; 20: 7053. https://doi.org/10.3390/s20247053
14.
WangXShenYWangS, et al. A machine learning-based compensation method for inductosyn angle measurement errors. IFAC-PapersOnLine2025; 59: 805–810. https://doi.org/10.1016/j.ifacol.2025.11.251
15.
NiuCChenBFletcherS, et al. Learning-based robotic machining error prediction for high precision manufacturing. Robot Comput Manuf2026; 100: 103217. https://doi.org/10.1016/j.rcim.2025.103217
16.
LuWPaganiLZhouL, et al. Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling. Precis Eng2019; 56: 38–52. https://doi.org/10.1016/j.precisioneng.2018.09.002
17.
LengJLiJZhangQ, et al. Untrained neural networks in data-scarce smart manufacturing: a paradigm for data-efficient industrial intelligence. Comput Ind Eng2026; 213: 111744. https://doi.org/10.1016/j.cie.2025.111744
18.
ZhuLYanBWangY, et al. Inspection of blade profile and machining deviation analysis based on sample points optimization and NURBS knot insertion. Thin-Walled Struct2021; 162: 107540. https://doi.org/10.1016/j.tws.2021.107540
19.
GuevelFVipreyFEuzenatC, et al. Geometric error compensation through position feedback modification and comparison of correction strategies in 3- axis machine-tool. J Manuf Process2025; 150: 213–223. https://doi.org/10.1016/j.jmapro.2025.06.021
20.
ZhuSDingGQinS, et al. Integrated geometric error modeling, identification and compensation of CNC machine tools. Int J Mach Tools Manuf2011; 52: 24–29. https://doi.org/10.1016/j.ijmachtools.2011.08.011
21.
XiaCWangSMaC, et al. Crucial geometric error compensation towards gear grinding accuracy enhancement based on simplified actual inverse kinematic model. Int J Mech Sci2020; 169: 105319. https://doi.org/10.1016/j.ijmecsci.2019.105319
22.
CongHZhaJChenY. Efficient compensation method of 5-axis machine tools based on a novel representation of the volumetric error. Int J Adv Manuf Technol2023; 126: 1109–1119. https://doi.org/10.1007/s00170-023-11117-6
23.
EsmaeiliSMMayerJRR. CNC table-based compensation of inter-axis and linear axis scale gain errors for a five-axis machine tool from symbolic variational kinematics. CIRP Annals2021; 70: 439–442. https://doi.org/10.1016/j.cirp.2021.04.042
24.
Narendra ReddyTShanmugarajVVinodP, et al. Real-time thermal error compensation strategy for precision machine tools. Mater Today Proc2020; 22: 2386–2396. https://doi.org/10.1016/j.matpr.2020.03.363
25.
HuangYZhuLHaoY, et al. Error compensation for thin-walled blade machining based on analytical modeling and ensemble machine learning. Thin-Walled Struct2026; 219: 114285. https://doi.org/10.1016/j.tws.2025.114285
26.
HuangHZhaoHZhangC, et al. A novel high-frequency cutting force compensation model for micro-milling polyvinylidene fluoride thin films tri-axis force sensors based on machine learning algorithms. Precis Eng2025; 95: 188–202. https://doi.org/10.1016/j.precisioneng.2025.04.026
27.
ChenFJYinSHHuangH, et al. Profile error compensation in ultra-precision grinding of aspheric surfaces with on-machine measurement. Int J Mach Tools Manuf2010; 50: 480–486. https://doi.org/10.1016/j.ijmachtools.2010.01.001
KaltenbrunnerTKrücklHPSchnalzgerG, et al. Differences in evolution of temperature, plastic deformation and wear in milling tools when up-milling and down-milling Ti6Al4V. J Manuf Process2022; 77: 75–86. https://doi.org/10.1016/j.jmapro.2022.03.010
30.
WangZGHakoyamaTYoshikawaY. Plastic deformation of workpiece during unloading in plate compression. CIRP Annals Manuf Technol2021; 70: 223–226. https://doi.org/10.1016/j.cirp.2021.04.005
31.
WangGLiWLRaoF, et al. Multi-parameter optimization of machining impeller surface based on the on-machine measuring technique. Chin J Aeronaut2019; 32: 2000–2008. https://doi.org/10.1016/j.cja.2018.09.005
32.
HuangZ. Study on prediction of machining elastic deformation and error compensation for integral impeller. Beijing Jiaotong University, 2013.
33.
YeWGuoYLiangR, et al. Research on thermo-mechanical coupling deformation for the ball screw of machine tool spindle feed system. J Vib Eng Technol2020; 8: 443–454. https://doi.org/10.1007/s42417-019-00182-5
34.
CheJZhangYWangH, et al. An experimental and numerical study on the wear mechanism of cutters on workover bits under thermo-mechanical coupling. Geoenergy Sci Eng2023; 224: 211628. https://doi.org/10.1016/j.geoen.2023.211628
35.
LiuSZhangHZhaoL, et al. Coupled thermo-mechanical sticking-sliding friction model along tool-chip interface in diamond cutting of copper. J Manuf Process2021; 70: 578–592. https://doi.org/10.1016/j.jmapro.2021.09.012
36.
ZhaoBGuoXBieW, et al. Thermo-mechanical coupling effect on surface residual stress during ultrasonic vibration-assisted forming grinding gear. J Manuf Process2020; 59: 19–32. https://doi.org/10.1016/j.jmapro.2020.09.041
37.
KujiCChighizolaCRHillMR, et al. Experimental study on the effect of the milling condition of an aluminum alloy on subsurface residual stress. Int J Adv Manuf Technol2023; 127: 5487–5501. https://doi.org/10.1007/s00170-023-11921-0
38.
XuYYueCChenZ, et al. Finite element simulation of residual stress in milling of aluminum alloy with different passes. Int J Adv Manuf Technol2023; 127: 4199–4210. https://doi.org/10.1007/s00170-023-11795-2
39.
EggerCSimonNLüchingerM, et al. Experimental and numerical analysis of residual stresses induced by the manufacturing process of longitudinal welded tubes. J Strain Anal Eng Des2024; 59: 351–365. https://doi.org/10.1177/03093247241234988
40.
JiangSQiPHanL, et al. Navigation system for orchard spraying robot based on 3D LiDar SLAM with NDTICP point cloud registration. Comput Electron Agric2024; 220: 108870. https://doi.org/10.1016/j.compag.2024.108870
41.
WuLWangAWangK, et al. Adaptive optimization method for prediction and compensation of thin-walled parts machining deformation based on on-machine measurement. Sensors2024; 24: 613. https://doi.org/10.3390/s24020613
42.
SooriMArezooB. Dimensional, geometrical, thermal and tool deflection errors compensation in 5-axis CNC milling operations. Aust J Mech Eng2024; 22: 935–949. https://doi.org/10.1080/14484846.2023.2195149
43.
DenkenaBBergmannBSchumacherT. Anticipatory online compensation of tool deflection using a priori information from process planning. J Manuf Mater Process2021; 5: 90. https://doi.org/10.3390/jmmp5030090
44.
ZhangMGuKWengZ, et al. Residual stress evolution of 7050 aluminum alloy during thermal processing and its effects on processing deformation and mechanical properties. J Mater Eng Perform2024; 33: 11467–11483. https://doi.org/10.1007/s11665-023-08816-2
45.
ChenHTangPXieY, et al. Optimization of residual stress microstructure and mechanical properties in 7075 aluminum alloy via annealing. J Mater Eng Perform2025; 34: 27490–27503. https://doi.org/10.1007/s11665-025-11286-3
46.
YangHWangHHuangQ, et al. Aero-engine blade error distributions predictions using novel machine learning models. Int J Mech Sci2025; 295: 110262. https://doi.org/10.1016/j.ijmecsci.2025.110262
47.
GeGXiaoYLvJ, et al. A non-iterative compensation method for machining errors of thin-walled parts considering coupling effect of tool-workpiece deformation. Manuf Lett2024; 41: 287–295. https://doi.org/10.1016/j.mfglet.2024.09.034
