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The International Journal of Advanced Manufacturing Technology

Erschienen in:

15.02.2024 | Critical Review

Robotic grinding based on point cloud data: developments, applications, challenges, and key technologies

verfasst von: Xinlei Ding, Jinwei Qiao, Na Liu, Zhi Yang, Rongmin Zhang

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 7-8/2024

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Abstract

Robotic grinding based on point cloud data is considered an alternative solution for efficient and intelligent machining of complex components by virtue of its flexibility, intelligence, and cost efficiency, particularly in comparison with the current mainstream manufacturing modes. Over the past two decades, the development of robotic grinding techniques based on point cloud data has evolved from an independent measurement and machining operation to an integrated “measurement-machining” approach. The total grinding cycle time was reduced by 43% compared to manual grinding. Currently, the average measurement error of the robotic grinding system based on point cloud data is about 0.06 mm, the average machining error is 0.1 mm, and the average roughness of the surface is 0.286 µm, which can meet the requirement of complex components. The relevant research in the field of robotic grinding based on point cloud data in the past 20 years was organized in this paper. Then technical difficulties, specifications, and breakthrough advances of robotic grinding were summarized. Online measurement and path planning were analyzed on robotic grinding for complex components. Finally, some research interests and potential application areas were proposed to improve the accuracy, quality, and application range.

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Nächster Artikel A review on machining Ti–5Al–5V–5Mo–3Cr alloy using defined geometry tools

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Liang HG, Qiao JW (2022) Analysis of current situation, demand and development trend of casting grinding technology. Micromachines 13(10):1577. https://doi.org/10.3390/mi13101577CrossRef

Klimchik A, Ambiehl A, Garnier S, Furet B, Pashkevich A (2017) Efficiency evaluation of robots in machining applications using industrial performance measure. Robot Comput-Integr Manuf 48:12–29. https://doi.org/10.1016/j.rcim.2016.12.005

Li WL, Yin ZP, Wu JY, Xiong YL (2009) 3D face recognition based on local/global shape description. In: 2009 International conference on information technology and computer science. IEEE, pp 417–420, https://doi.org/10.1109/ITCS.2009.90

Li WL, Yin ZP, Huang YA, Xiong YL (2011) Automatic registration for 3D shapes using hybrid dimensionality-reduction shape descriptions. Pattern Recognit 44(12):2926–2943. https://doi.org/10.1016/j.patcog.2011.02.005CrossRef

Li L, Cao XY, Sun J (2018) Three-dimensional point cloud registration based on normal vector angle. J Indian Soc Remote Sens 47:585–593CrossRef

Zhang JY, Yao YX, Deng BL (2022) Fast and robust iterative closest point. IEEE Trans Pattern Anal Mach Intell 44(7):3450–3466. https://doi.org/10.1109/TPAMI.2021.3054619CrossRef

Song LZ, Lin DY, Peng XF, Li ZF (2021) Two-stage point cloud registration for 3D measurement of large workpieces. In: 2021 16th International conference on computer science & Education (ICCSE). IEEE, pp 500–505, https://doi.org/10.1109/ICCSE51940.2021.9569505

Yan SJ, Xu XH, Yang ZY, Zhu DH, Ding H (2019) An improved robotic abrasive belt grinding force model considering the effects of cut-in and cut-off. J Manuf Process 37:496–508. https://doi.org/10.1016/j.jmapro.2018.12.029

Rafieian F, Hazel B, Liu Z (2014) Regenerative instability of impact-cutting material removal in the grinding process performed by a flexible robot arm. Procedia CIRP 14:406–411. https://doi.org/10.1016/j.procir.2014.03.099

10.

Xu XH, Chu Y, Zhu DH, Yan S, Ding H (2020) Experimental investigation and modeling of material removal characteristics in robotic belt grinding considering the effects of cut-in and cut-off. Int J Adv Manuf Technol 106:1161–1177CrossRef

11.

Zhu DH, Xu XH, Yang ZY, Zhuang KJ, Yan SJ, Ding H (2018) Analysis and assessment of robotic belt grinding mechanisms by force modeling and force control experiments. Tribol Int 120:93–98. https://doi.org/10.1016/j.triboint.2017.12.043

12.

Xu XH, Zhu DH, Zhang HY, Yan SJ, Ding H (2019) Application of novel force control strategies to enhance robotic abrasive belt grinding quality of aero-engine blades. Chin J Aeronaut 32(10):2368–2382. https://doi.org/10.1016/j.cja.2019.01.023

13.

Lin HI, Dubey V (2019) Design of an adaptive force controlled robotic polishing system using adaptive fuzzy-PID. Intelligent autonomous systems 15:825–836CrossRef

14.

Gracia L, Solanes JE, Muñoz-Benavent P, Valls Miro J, Perez-Vidal C, Tornero J (2018) Adaptive sliding mode control for robotic surface treatment using force feedback. Mechatronics 52:102–118. https://doi.org/10.1016/j.mechatronics.2018.04.008

15.

Tian FJ, Lv C, Li ZG, Liu GB (2016) Modeling and control of robotic automatic polishing for curved surfaces. CIRP J Manuf Sci Technol 14:55–64. https://doi.org/10.1016/j.cirpj.2016.05.010

16.

Mohammad AEK, Hong J, Wang DW (2018a) Design of a force-controlled end-effector with low-inertia effect for robotic polishing using macro-mini robot approach. Robot Comput-Integr Manuf 49:54–65. https://doi.org/10.1016/j.rcim.2017.05.011

17.

Mohammad AEK, Hong J, Wang DW (2018b) Design of a force-controlled end-effector with low-inertia effect for robotic polishing using macro-mini robot approach. Robot Comput-Integr Manuf 49:54–65. https://doi.org/10.1016/j.rcim.2017.05.011

18.

Zhu DH, Feng XZ, Xu XH, Yang ZY, Li WL, Yan SJ, Ding H (2020) Robotic grinding of complex components: a step towards efficient and intelligent machining - challenges, solutions, and applications. Robot Comput-Integr Manuf 65:101908. https://doi.org/10.1016/j.rcim.2019.101908

19.

Pervez MR, Ahamed MH, Ahmed MA, Takrim SM, Dario P (2022) Autonomous grinding algorithms with future prospect towards smart manufacturing: a comparative survey. J Manuf Syst 62:164–185. https://doi.org/10.1016/j.jmsy.2021.11.009

20.

Chen C, Wang Y, Gao ZT, Peng FY, Tang XQ, Yan R, Zhang YK (2022) Intelligent learning model-based skill learning and strategy optimization in robot grinding and polishing

21.

Kishore K, Sinha MK, Singh A, Archana Gupta MK, Korkmaz ME (2022) A comprehensive review on the grinding process: advancements, applications and challenges. Proc Inst Mech Eng C J Mech Eng Sci 236(22):10923–10952. https://doi.org/10.1177/09544062221110782CrossRef

22.

Huang H, Gong ZM, Chen XQ, Zhou L (2003) Smart robotic system for 3D profile turbine vane airfoil repair. Int J Adv Manuf Technol 21(4):275–283. https://doi.org/10.1007/s001700300032CrossRef

23.

Chen XQ, Lin WJ, Ng BTJ (2009) Neutral line-based robust profile reconstruction for adaptive machining of turbine blade tip welds. Int J Manuf Res 4:236–251CrossRef

24.

Zeng H, Li HZ, Chen XQ, Goh K, Aendenroomer A, Li XC (2004) Excess welding area localisation and profile modelling on distorted surface in turbine component repairing process. In: Proceedings of the IEEE-ISIE 2004, Vols 1 and 2, pp 241–246

25.

Huang H, Gong ZM, Chen XQ, Zhou L (2002) Robotic grinding and polishing for turbine-vane overhaul. J Mater Process Technol 127(2):140–145. https://doi.org/10.1016/S0924-0136(02)00114-0CrossRef

26.

Li WL, Xie H, Zhang G, Yan SJ, Yin ZP (2016) 3-D shape matching of a blade surface in robotic grinding applications. IEEE-ASME Trans Mechatron 21(5):2294–2306. https://doi.org/10.1109/TMECH.2016.2574813CrossRef

27.

Li WL, Yin ZP, Huang YA, Xiong YL (2011) Three-dimensional point-based shape registration algorithm based on adaptive distance function. IET Comput Vis 5(1):68–76. https://doi.org/10.1049/iet-cvi.2009.0032MathSciNetCrossRef

28.

29.

Li W, Yin Z, Huang Y, Xiong Y (2010) Tool-path generation based on angle-based flattening. Proc Inst Mech Eng B J Eng Manuf 224(10):1503–1509. https://doi.org/10.1243/09544054JEM1832CrossRef

30.

Long LW, Ping YZ, Lun XY (2009) Adaptive distance function and its application in free-form surface localization. In: 2009 International conference on information and automation, pp 19–23

31.

Xie H, Li WL, Yin ZP, Ding H (2019) Variance-minimization iterative matching method for free-form surfaces—part I: theory and method. IEEE Trans Autom Sci Eng 16(3):1181–1191. https://doi.org/10.1109/TASE.2018.2875154CrossRef

32.

Xie H, Li WL, Yin ZP, Ding H (2019) Variance-minimization iterative matching method for free-form surfaces—part II: experiment and analysis. IEEE Trans Autom Sci Eng 16(3):1192–1204. https://doi.org/10.1109/TASE.2018.2875145CrossRef

33.

Xie H, Li WL, Zhu DH, Yin ZP, Ding H (2020) A systematic model of machining error reduction in robotic grinding. IEEE ASME Trans Mechatron 25(6):2961–2972. https://doi.org/10.1109/TMECH.2020.2999928CrossRef

34.

Zhang GF, Wang JW, Cao F, Li Y, Chen XQ (2016) 3d curvature grinding path planning based on point cloud data. In: 2016 12th IEEE/ASME international conference on mechatronic and embedded systems and applications (MESA), pp 1–6. https://doi.org/10.1109/MESA.2016.7587150

35.

Li Y, Cao F, Wang JW, Zhang GF, Chen XQ, Chen SB (2016) Modeling grinding process for difficult-to-machine materials using artificial neural network. In: 2016 IEEE 11th conference on industrial electronics and applications (ICIEA), pp 267–271. https://doi.org/10.1109/ICIEA.2016.7603591

36.

Cao F, Li Y, Zhang GF, Wang JW, Chen XQ, Zhao YZ (2016) Novel humanoid dual-arm grinding robot. In: 2016 12th IEEE/ASME international conference on mechatronic and embedded systems and applications (MESA), pp 1–6. https://doi.org/10.1109/MESA.2016.7587185

37.

Xu XH, Zhu DH, Wang JS, Yan SJ, Ding H (2018) Calibration and accuracy analysis of robotic belt grinding system using the ruby probe and criteria sphere. Robot Comput-Integr Manuf 51:189–201. https://doi.org/10.1016/j.rcim.2017.12.006CrossRef

38.

Xu XH, Zhu DH, Zhang HY, Yan SJ, Ding H (2017) TCP-based calibration in robot-assisted belt grinding of aero-engine blades using scanner measurements. Int J Adv Manuf Technol 90(1–4):635–647. https://doi.org/10.1007/s00170-016-9331-8CrossRef

39.

40.

41.

Xu XH, Yang YF, Pan GF, Zhu DH, Yan SJ (2018) A robotic belt grinding force model to characterize the grinding depth with force control technology. In: Intelligent robotics and applications. Springer International Publishing, pp 287–298

42.

Zhou K, Ding HH, Wang RX, Yang JY, Guo J, Liu QY, Wang WJ (2020) Experimental investigation on material removal mechanism during rail grinding at different forward speeds. Tribol Int 143:106040. https://doi.org/10.1016/j.triboint.2019.106040

43.

Wang ZY, Zhu DH (2019) An accurate detection method for surface defects of complex components based on support vector machine and spreading algorithm. Measurement 147:106886. https://doi.org/10.1016/j.measurement.2019.106886CrossRef

44.

Zhang HY, Li L, Zhao JB (2019) Robot automation grinding process for nuclear reactor coolant pump based on reverse engineering. Int J Adv Manuf Technol 102(1–4):879–891. https://doi.org/10.1007/s00170-018-03230-8CrossRef

45.

Wang XF, Zhang XQ, Ren XK, Li LF, Feng HJ, He YB, Chen HB, Chen XQ (2020) Point cloud 3D parent surface reconstruction and weld seam feature extraction for robotic grinding path planning. Int J Adv Manuf Technol 107(1–2):827–841. https://doi.org/10.1007/s00170-020-04947-1CrossRef

46.

Feng HJ, Ren XK, Li LF, Zhang XQ, Chen HB, Chai Z, Chen XQ (2021) A novel feature-guided trajectory generation method based on point cloud for robotic grinding of freeform welds. Int J Adv Manuf Technol 115(5–6):1763–1781. https://doi.org/10.1007/s00170-021-07095-2CrossRef

47.

Ge JM, Deng ZH, Li ZY, Li W, Liu T, Zhang H, Nie JX (2021) An efficient system based on model segmentation for weld seam grinding robot. Int J Adv Manuf Technol 121:7627–7641CrossRef

48.

Wang G, Li WL, Jiang C, Zhu DH, Li ZW, Xu W, Zhao H, Ding H (2022) Trajectory planning and optimization for robotic machining based on measured point cloud. IEEE Trans Robot 38(3):1621–1637. https://doi.org/10.1109/TRO.2021.3108506CrossRef

49.

Li JF, Zhu JH, Guo YK, Lin XD, Duan KL, Wang YS, Tang Q (2008) Calibration of a portable laser 3-D scanner used by a robot and its use in measurement. Opt Eng 47(1). https://doi.org/10.1117/1.2829766

50.

Li WL, Xie H, Zhang G, Yan SJ, Yin ZP (2016) Hand-eye calibration in visually-guided robot grinding. IEEE T Cybern 46(11):2634–2642. https://doi.org/10.1109/TCYB.2015.2483740CrossRef

51.

Xie H, Li WL, Jiang C, Zhu DH, Yin ZP, Ding H (2021) Pose error estimation using a cylinder in scanner-based robotic belt grinding. Int J Adv Manuf Technol 26(1):515–526. https://doi.org/10.1109/TMECH.2020.3038237CrossRef

52.

Ren YJ, Yin SB, Zhu JG (2012) Calibration technology in application of robot-laser scanning system. Opt Eng 51(11). https://doi.org/10.1117/1.OE.51.11.114204

53.

Diao SP, Chen XD, Wu L (2019) Calibration method of vision measurement system for ceramic billet grinding robot. J Eng-JOE 7:4656–4666. https://doi.org/10.1049/joe.2018.5096CrossRef

54.

Liu XJ, Madhusudanan H, Chen WY, Li DH, Ge J, Ru CH, Sun Y (2021) Fast eye-in-hand 3-D scanner-robot calibration for low stitching errors. IEEE Trans Ind Electron 68(9):8422–8432. https://doi.org/10.1109/TIE.2020.3009568CrossRef

55.

Xie H, Li WL, Liu H (2022) General geometry calibration using arbitrary free-form surface in a vision-based robot system. IEEE Trans Ind Electron 69(6):5994–6003. https://doi.org/10.1109/TIE.2021.3090716CrossRef

56.

Xin MT, Li B, Wei X, Zhao Z (2021) Rapid registration method by using partial 3D point clouds. Optik 246:167764. https://doi.org/10.1016/j.ijleo.2021.167764CrossRef

57.

Wang BB, Xie JC, Wang XW, Liu SG, Liu YM (2021) A new method for measuring the attitude and straightness of hydraulic support groups based on point clouds. Arab J Sci Eng 46(12):11739–11757. https://doi.org/10.1007/s13369-021-05689-2CrossRef

58.

Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (fpfh) for 3D registration. In: 2009 IEEE international conference on robotics and automation, pp 3212–3217. https://doi.org/10.1109/ROBOT.2009.5152473

59.

Guo N, Zhang BH, Zhou J, Zhan K, Lai S (2020) Pose estimation and adaptable grasp configuration with point cloud registration and geometry understanding for fruit grasp planning. Comput Electron Agric 179:105818. https://doi.org/10.1016/j.compag.2020.105818CrossRef

60.

Li W, Cheng HT, Zhang XH (2021) Efficient 3D object recognition from cluttered point cloud. Sensors 21(17):5850. https://doi.org/10.3390/s21175850CrossRef

61.

Fischler MA, Bolles RC (1987) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in Computer Vision. Morgan Kaufmann, San Francisco (CA), pp 726–740, https://doi.org/10.1016/B978-0-08-051581-6.50070-2

62.

Xu ZH, Xu ES, Zhang ZX, Wu LX (2019) Multiscale sparse features embedded 4-points congruent sets for global registration of TLS point clouds. IEEE Geosci Remote Sens Lett 16(2):286–290. https://doi.org/10.1109/LGRS.2018.2872353CrossRef

63.

Mellado N, Aiger D, Mitra NJ (2014) Super 4pcs fast global pointcloud registration via smart indexing. Comput Graph Forum 33(5):205–215. https://doi.org/10.1111/cgf.12446CrossRef

64.

Sun JL, Zhang RF, Du S, Zhang LQ, Liu Y (2020) Global adaptive 4-points congruent sets registration for 3D indoor scenes with robust estimation. IEEE Access 8:7539–7548. https://doi.org/10.1109/ACCESS.2020.2963984CrossRef

65.

Li SK, Lu RD, Liu JY, Guo L (2021) Super edge 4-points congruent sets-based point cloud global registration. Remote Sens 13(16). https://doi.org/10.3390/rs13163210

66.

Aiger D, Mitra NJ, Cohen-Or D (2008) 4-points congruent sets for robust pairwise surface registration. ACM Trans Graph 27(3):1–10. https://doi.org/10.1145/1360612.1360684CrossRef

67.

Besl P, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256. https://doi.org/10.1109/34.121791CrossRef

68.

Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell PAMI-9(5):698–700. https://doi.org/10.1109/TPAMI.1987.4767965

69.

Stoyanov T, Magnusson M, Andreasson H, Lilienthal AJ (2012) Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations. Int J Robot Res 31(12, SI):1377–1393. https://doi.org/10.1177/0278364912460895

70.

Pathak K, Birk A, Vaskevicius N, Poppinga J (2010) Fast registration based on noisy planes with unknown correspondences for 3-D mapping. IEEE Trans Robot 26(3):424–441. https://doi.org/10.1109/TRO.2010.2042989CrossRef

71.

Magnusson M, Lilienthal A, Duckett T (2007) Scan registration for autonomous mining vehicles using 3D-NDT. J Field Robot 24(10):803–827. https://doi.org/10.1002/rob.20204CrossRef

72.

Shi XY, Peng JJ, Li JP, Yan PT, Gong HY (2019) The iterative closest point registration algorithm based on the normal distribution transformation. Procedia Comput Sci 147:181–190. https://doi.org/10.1016/j.procs.2019.01.219

73.

Rister B, Horowitz MA, Rubin DL (2017) Volumetric image registration from invariant keypoints. IEEE Trans Image Process 26(10):4900–4910. https://doi.org/10.1109/TIP.2017.2722689MathSciNetCrossRef

74.

Zhong Y (2009) Intrinsic shape signatures: a shape descriptor for 3d object recognition. In: 2009 IEEE 12th International conference on computer vision workshops, ICCV Workshops, pp 689–696. https://doi.org/10.1109/ICCVW.2009.5457637

75.

Segal AV, Hähnel D, Thrun S (2009) Generalized-ICP. In: Robotics: science and systems, pp 435

76.

Yang JL, Li HD, Campbell D, Jia YD (2016) Go-ICP: a globally optimal solution to 3D ICP point-set registration. IEEE Trans Pattern Anal Mach Intell 38(11):2241–2254. https://doi.org/10.1109/TPAMI.2015.2513405CrossRef

77.

Zhang JY, Yao YX, Deng BL (2022) Fast and robust iterative closest point. IEEE Trans Pattern Anal Mach Intell 44(7):3450–3466. https://doi.org/10.1109/TPAMI.2021.3054619CrossRef

78.

Kharidege A, Ting D, Yajun Z (2017) A practical approach for automated polishing system of free-form surface path generation based on industrial arm robot. Int J Adv Manuf Technol 93(9–12):3921–3934. https://doi.org/10.1007/s00170-017-0726-yCrossRef

79.

Yilmaz O, Gindy N, Gao J (2010) A repair and overhaul methodology for aeroengine components. Robot Comput Integr Manuf 26(2):190–201. https://doi.org/10.1016/j.rcim.2009.07.001CrossRef

80.

Gao XY, Zhang SY, Qiu LM, Liu XJ, Wang ZL, Wang Y (2020) Double B-spline curve-fitting and synchronization-integrated feedrate scheduling method for five-axis linear-segment toolpath. Appl Sci-Basel 10(9):3158. https://doi.org/10.3390/app10093158CrossRef

81.

Boryga M, Graboś A (2009) Planning of manipulator motion trajectory with higher-degree polynomials use. Mech Mach Theory 44(7):1400–1419. https://doi.org/10.1016/j.mechmachtheory.2008.11.003

82.

Ge JM, Deng ZH, Li ZY, Li W, Lv LS, Liu T (2021) Robot welding seam online grinding system based on laser vision guidance. Int J Adv Manuf Technol 116:1737–1749CrossRef

Titel: Robotic grinding based on point cloud data: developments, applications, challenges, and key technologies
verfasst von: Xinlei Ding
Jinwei Qiao
Na Liu
Zhi Yang
Rongmin Zhang
Publikationsdatum: 15.02.2024
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 7-8/2024
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-024-13094-w

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