Abstract
Machining deformation compensation technology based on on-machine measurement has been widely used in the field of thin-walled part machining. However, few research has been conducted on sampling methods for the measurement of thin-walled parts. In this study, we considered the influence of machining deformation in thin-walled regions, established a machining deformation prediction model (MDPM) based on the finite element method (FEM), and applied it to the sampling optimization process. Furthermore, we proposed an adaptive sampling method based on the maximum corresponding point deviation (MCPD) at the measurement point interval of the non-uniform rational B-spline (NURBS) curve. The proposed method was compared with three commonly used sampling methods (uniform sampling, curvature-based sampling, and maximum deviation-based sampling). Sampling experiments were performed with one NURBS curve and two machined thin-walled parts. The experimental results show that the proposed method is superior to the three commonly used sampling strategies in terms of reconstruction accuracy, sampling efficiency, and result stability.
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References
Zhao X, Zheng L, Zhang Y (2022) Online first-order machining error compensation for thin-walled parts considering time-varying cutting condition. J Manuf Sci Eng. https://doi.org/10.1115/1.4051793
Ge G, Du Z, Feng X, Yang J (2020) An integrated error compensation method based on on-machine measurement for thin web parts machining. Precis Eng 63:206–213. https://doi.org/10.1016/j.precisioneng.2020.03.002
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. https://doi.org/10.1016/j.ijmachtools.2014.04.004
Guiassa R, Mayer JRR (2011) Predictive compliance based model for compensation in multi-pass milling by on-machine probing. CIRP Ann 60:391–394. https://doi.org/10.1016/j.cirp.2011.03.123
Yu M, Zhang Y, Li Y, Zhang D (2012) Adaptive sampling method for inspection planning on CMM for free-form surfaces. Int J Adv Manuf Technol 67:1967–1975. https://doi.org/10.1007/s00170-012-4623-0
Wang J, Jiang X, Blunt LA, Leach RK, Scott PJ (2012) Intelligent sampling for the measurement of structured surfaces. Meas Sci Technol. https://doi.org/10.1088/0957-0233/23/8/085006
Ascione R, Moroni G, Petrò S, Romano D (2013) Adaptive inspection in coordinate metrology based on kriging models. Precis Eng 37:44–60. https://doi.org/10.1016/j.precisioneng.2012.06.006
Rajamohan G, Shunmugam MS, Samuel GL (2011) Effect of probe size and measurement strategies on assessment of freeform profile deviations using coordinate measuring machine. Measurement 44:832–841. https://doi.org/10.1016/j.measurement.2011.01.020
He G, Sang Y, Pang K, Sun G (2018) An improved adaptive sampling strategy for freeform surface inspection on CMM. Int J Adv Manuf Technol 96:1521–1535. https://doi.org/10.1007/s00170-018-1612-y
Liu J, Zhao J, Yang X, Qu X, Wang X, Liu J (2018) A tangential approximation algorithm for measured data reduction of blade section curves. Measurement 128:504–515. https://doi.org/10.1016/j.measurement.2018.05.085
Cheng X, Liu X, Feng P, Zeng L, Jiang H, Sun Z, Zhang S (2022) Efficient adaptive sampling methods based on deviation analysis for on-machine inspection. Measurement. https://doi.org/10.1016/j.measurement.2021.110497
Lu K, Wang W, Wu Y, Wei Y, Chen Z (2013) An adaptive sampling approach for digitizing unknown free-form surfaces based on advanced path detecting. Procedia CIRP 10:216–223. https://doi.org/10.1016/j.procir.2013.08.034
Edgeworth R, Wilhelm RG (1999) Adaptive sampling for coordinate metrology. Precis Eng 23:144–154. https://doi.org/10.1016/S0141-6359(99)00004-5
Pedone P, Vicario G, Romano D (2009) Kriging-based sequential inspection plans for coordinate measuring machines. Appl Stoch Models Bus Ind. https://doi.org/10.1007/s00362-018-1030-0
Dumas A, Echard B, Gayton N, Rochat O, Dantan J-Y, Van Der Veen S (2013) AK-ILS: An Active learning method based on Kriging for the inspection of large surfaces. Precis Eng 37:1–9. https://doi.org/10.1016/j.precisioneng.2012.07.007
Poniatowska M (2012) Deviation model based method of planning accuracy inspection of free-form surfaces using CMMs. Measurement 45:927–937. https://doi.org/10.1016/j.measurement.2012.01.051
Piegl L (1991) On NURBS: a Survey. IEEE Comput Graphics Appl 11:55–71. https://doi.org/10.1109/38.67702
Shi F (2001) CAGD&NURBS. Highter Education Press
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. https://doi.org/10.1016/j.ijmachtools.2018.12.004
Qi H, Tian Y, Zhang D (2012) Machining forces prediction for peripheral milling of low-rigidity component with curved geometry. Int J Adv Manuf Technol 64:1599–1610. https://doi.org/10.1007/s00170-012-4126-z
Liu X, Ahmad F, Yamazaki K, Mori M (2005) Adaptive interpolation scheme for NURBS curves with the integration of machining dynamics. Int J Mach Tools Manuf 45:433–444. https://doi.org/10.1016/j.ijmachtools.2004.09.009
Chen X-D, Ma W, Xu G, Paul J-C (2010) Computing the Hausdorff distance between two B-spline curves. Comput-Aided Des 42:1197–1206. https://doi.org/10.1016/j.cad.2010.06.009
Hernández-Mederos V, Estrada-Sarlabous J (2003) Sampling points on regular parametric curves with control of their distribution. Comput Aided Geom Des 20:363–382. https://doi.org/10.1016/s0167-8396(03)00079-7
Jiang RS, Wang WH, Zhang DH, Wang ZQ (2016) A practical sampling method for profile measurement of complex blades. Measurement 81:57–65. https://doi.org/10.1016/j.measurement.2015.11.039
Lu L, Zhao S (2019) High-quality point sampling for B-spline fitting of parametric curves with feature recognition. J Comput Appl Math 345:286–294. https://doi.org/10.1016/j.cam.2018.04.008
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Wu, L., Wang, A., Xing, W. et al. Adaptive sampling method for thin-walled parts based on on-machine measurement. Int J Adv Manuf Technol 122, 2577–2592 (2022). https://doi.org/10.1007/s00170-022-09962-y
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DOI: https://doi.org/10.1007/s00170-022-09962-y