Skip to main content
SpringerLink
Log in

A review of recent advances in robotic belt grinding of superalloys

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Superalloys are widely used in aerospace and energy fields by virtue of superior physical properties, especially in high-temperature environments. However, superalloys have poor machinability and are typical difficult-to-machine materials. Robotic belt grinding is an effective and popular method for finish machining superalloys, offering significant advantages such as high material removal capacity, low heat input, and fewer workpiece damages. Moreover, robots can be well integrated with multi sensors, such as infrared radiation cameras, force sensors, microphones, and high-speed cameras. These sensors help to realize real-time monitoring of grinding processes, thus benefiting the grinding quality control. Despite many developments in recent decades, there is a lack of comprehensive review of robotic belt grinding of superalloys, particularly advanced techniques and methods to achieve precision profile finishing and desired properties. Therefore, after introducing a typical intelligent robotic grinding system, this paper reviews five important aspects that need further research for robotic belt grinding of superalloys: typical tools and grinding parameters selection, surface integrity analysis and control, tool condition motoring, material removal, and finishing control, as well as the force distribution and thermal analysis. The typical applications, potentials, and limitations are also introduced. As an emerging technology, artificial intelligence (AI) is imperative to realize intelligent robotic belt grinding. Hence, this paper pays more attention on AI-based models of grinding processes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39
Fig. 40
Fig. 41
Fig. 42

Similar content being viewed by others

References

  1. Wang Z, Zou L, Duan L et al (2021) Study on passive compliance control in robotic belt grinding of nickel-based superalloy blade. J Manuf Process 68:168–179. https://doi.org/10.1016/j.jmapro.2021.07.020

    Article  Google Scholar 

  2. Klotz T, Delbergue D, Bocher P et al (2018) Surface characteristics and fatigue behavior of shot peened Inconel 718. Int J Fatigue 110:10–21. https://doi.org/10.1016/j.ijfatigue.2018.01.005

    Article  Google Scholar 

  3. Yang Z, Chu Y, Xu X et al (2021) Prediction and analysis of material removal characteristics for robotic belt grinding based on single spherical abrasive grain model. Int J Mech Sci 190:106005. https://doi.org/10.1016/J.IJMECSCI.2020.106005

    Article  Google Scholar 

  4. Sarıkaya M, Gupta MK, Tomaz I et al (2021) A state-of-the-art review on tool wear and surface integrity characteristics in machining of superalloys. CIRP J Manuf Sci Tech 35:624–658. https://doi.org/10.1016/J.CIRPJ.2021.08.005

    Article  Google Scholar 

  5. Beranoagirre A, de Lacalle LNL (2013) Grinding of gamma TiAl intermetallic alloys. Procedia Eng 63:489–498. https://doi.org/10.1016/j.proeng.2013.08.182

    Article  Google Scholar 

  6. Martell JJ, Liu CR, Shi J (2014) Experimental investigation on variation of machined residual stresses by turning and grinding of hardened AISI 1053 steel. Int J Adv Manuf Tech 74:1381–1392. https://doi.org/10.1007/s00170-014-6089-8

    Article  Google Scholar 

  7. De Bartolomeis A, Newman ST, Jawahir IS et al (2021) Future research directions in the machining of Inconel 718. J Mater Process Tech 297:117260. https://doi.org/10.1016/j.jmatprotec.2021.117260

    Article  Google Scholar 

  8. Zhu D, Feng X, Xu X et al (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

    Article  Google Scholar 

  9. Zhong Z-W (2020) Advanced polishing, grinding and finishing processes for various manufacturing applications: a review. Mater Manuf Process 35:1279–1303. https://doi.org/10.1080/10426914.2020.1772481

    Article  Google Scholar 

  10. Feng H, Ren X, Li L et al (2021) A novel feature-guided trajectory generation method based on point cloud for robotic grinding of freeform welds. Int J Adv Manuf Technol 115:1763–1781

    Article  Google Scholar 

  11. Wang X, Zhang X, Ren X et al (2020) Point cloud 3D parent surface reconstruction and weld seam feature extraction for robotic grinding path planning. Int J Adv Manuf Tech 107:827–841. https://doi.org/10.1007/s00170-020-04947-1

    Article  Google Scholar 

  12. Zhu W-L, Beaucamp A (2020) Compliant grinding and polishing: a review. Int J Mach Tools Manuf 158:103634. https://doi.org/10.1016/j.ijmachtools.2020.103634

    Article  Google Scholar 

  13. Souza AM, da Silva EJ (2019) Global strategy of grinding wheel performance evaluation applied to grinding of superalloys. Precis Eng 57:113–126. https://doi.org/10.1016/j.precisioneng.2019.03.013

    Article  Google Scholar 

  14. Bhowmik S, Naik R (2018) Selection of abrasive materials for manufacturing grinding wheels. Mater Today Proc 5:2860–2864. https://doi.org/10.1016/j.matpr.2018.01.077

    Article  Google Scholar 

  15. Maity SR, Chakraborty S (2013) Grinding wheel abrasive material selection using fuzzy TOPSIS method. Mater Manuf Process 28:408–417. https://doi.org/10.1080/10426914.2012.700159

    Article  Google Scholar 

  16. Teicher U, Künanz K, Ghosh A, Chattopadhyay AB (2008) Performance of diamond and CBN single-layered grinding wheels in grinding titanium. Mater Manuf Process 23:224–227. https://doi.org/10.1080/10426910701860541

    Article  Google Scholar 

  17. Xi X, Yu T, Ding W, Xu J (2018) Grinding of Ti2AlNb intermetallics using silicon carbide and alumina abrasive wheels: tool surface topology effect on grinding force and ground surface quality. Precis Eng 53:134–145. https://doi.org/10.1016/j.precisioneng.2018.03.007

    Article  Google Scholar 

  18. Dai C, Ding W, Xu J et al (2017) Investigation on size effect of grain wear behavior during grinding nickel-based superalloy Inconel 718. Int J Adv Manuf Tech 91:2907–2917. https://doi.org/10.1007/s00170-016-9907-3

    Article  Google Scholar 

  19. Ding W, Zhu Y, Zhang L et al (2015) Stress characteristics and fracture wear of brazed CBN grains in monolayer grinding wheels. Wear 332:800–809. https://doi.org/10.1016/j.wear.2014.12.008

    Article  Google Scholar 

  20. Wang J, Xu J, Zhang X et al (2018) An investigation of surface corrosion behavior of Inconel 718 after robotic belt grinding. Materials 11:2440. https://doi.org/10.3390/ma11122440

    Article  Google Scholar 

  21. Shi Y, Wang Z, Xu S et al (2019) Study on the grindability of nano-vitrified bond CBN grinding wheel for nickel-based alloy. Int J Adv Manuf Tech 100:1913–1921. https://doi.org/10.1007/s00170-018-2807-y

    Article  Google Scholar 

  22. Klocke F, Soo SL, Karpuschewski B et al (2015) Abrasive machining of advanced aerospace alloys and composites. CIRP Ann 64(2):581–604. https://doi.org/10.1016/j.cirp.2015.05.004

    Article  Google Scholar 

  23. Aurich JC, Linke B, Hauschild M et al (2013) Sustainability of abrasive processes. CIRP Ann 62:653–672. https://doi.org/10.1016/j.cirp.2013.05.010

    Article  Google Scholar 

  24. Wang J, Xu J, Wang X et al (2019) A comprehensive study on surface integrity of nickel-based superalloy Inconel 718 under robotic belt grinding. Mater Manuf Process 34:61–69. https://doi.org/10.1080/10426914.2018.1512137

    Article  Google Scholar 

  25. Li Z, Ding W, Liu C, Su H (2018) Grinding performance and surface integrity of particulate-reinforced titanium matrix composites in creep-feed grinding. Int J Adv Manuf Tech 94:3917–3928. https://doi.org/10.1007/s00170-017-1159-3

    Article  Google Scholar 

  26. Hood R, Cooper P, Aspinwall DK et al (2015) Creep feed grinding of γ-TiAl using single layer electroplated diamond superabrasive wheels. CIRP J Manuf Sci Tech 11:36–44. https://doi.org/10.1016/j.cirpj.2015.07.001

    Article  Google Scholar 

  27. Bhaduri D, Soo SL, Aspinwall DK et al (2017) Ultrasonic assisted creep feed grinding of gamma titanium aluminide using conventional and superabrasive wheels. CIRP Ann 66:341–344. https://doi.org/10.1016/j.cirp.2017.04.085

    Article  Google Scholar 

  28. Zhao JY, Fu YC, Xu JH et al (2014) Forces and chip morphology of Nickel-based superalloy Inconel 718 during high speed grinding with single grain. Key Eng Mater 589:209–214. https://doi.org/10.4028/www.scientific.net/KEM.589-590.209

  29. Li Q, Xu J, Su H, Lei W (2015) Fabrication and performance of monolayer brazed CBN wheel for high-speed grinding of superalloy. Int J Adv Manuf Tech 80:1173–1180. https://doi.org/10.1007/s00170-015-7125-z

    Article  Google Scholar 

  30. Ding W, Zhang L, Li Z et al (2017) Review on grinding-induced residual stresses in metallic materials. Int J Adv Manuf Tech 88:2939–2968. https://doi.org/10.1007/s00170-016-8998-1

    Article  Google Scholar 

  31. Balan ASS, Kullarwar T, Vijayaraghavan L, Krishnamurthy R (2017) Computational fluid dynamics analysis of MQL spray parameters and its influence on superalloy grinding. Mach Sci Tech 21:603–616. https://doi.org/10.1080/10910344.2017.1365889

    Article  Google Scholar 

  32. Paul S, Singh AK, Ghosh A (2017) Grinding of Ti-6Al-4V under small quantity cooling lubrication environment using alumina and MWCNT nanofluids. Mater Manuf Process 32:608–615. https://doi.org/10.1080/10426914.2016.1257797

    Article  Google Scholar 

  33. Mukhopadhyay M, Kundu PK, Das S (2018) Experimental investigation on enhancing grindability using alkaline-based fluid for grinding Ti-6Al-4V. Mater Manuf Process 33:1775–1781. https://doi.org/10.1080/10426914.2018.1476759

    Article  Google Scholar 

  34. Field M, Kahles JF (1964) The surface integrity of machined and ground high strength steels. DMIC Report 210:54–77

    Google Scholar 

  35. Jawahir IS, Brinksmeier E, M’saoubi R et al (2011) Surface integrity in material removal processes: recent advances. CIRP Ann 60:603–626. https://doi.org/10.1016/j.cirp.2011.05.002

    Article  Google Scholar 

  36. Yao CF, Jin QC, Huang XC et al (2013) Research on surface integrity of grinding Inconel718. Int J Adv Manuf Tech 65:1019–1030. https://doi.org/10.1007/s00170-012-4236-7

    Article  Google Scholar 

  37. Brinksmeier E, Aurich JC, Govekar E et al (2006) Advances in modeling and simulation of grinding processes. CIRP Ann 55:667–696. https://doi.org/10.1016/j.cirp.2006.10.003

    Article  Google Scholar 

  38. Segreto T, Karam S, Simeone A, Teti R (2013) Residual stress assessment in Inconel 718 machining through wavelet sensor signal analysis and sensor fusion pattern recognition. Procedia CIRP 9:103–108. https://doi.org/10.1016/j.procir.2013.06.176

    Article  Google Scholar 

  39. Umbrello D, Ambrogio G, Filice L, Shivpuri R (2008) A hybrid finite element method–artificial neural network approach for predicting residual stresses and the optimal cutting conditions during hard turning of AISI 52100 bearing steel. Mater Des 29:873–883. https://doi.org/10.1016/j.matdes.2007.03.004

    Article  Google Scholar 

  40. Feng L, Xuekun L, Yiming R (2018) Active control of the residual stress in Incone1718 grinding assisted by the strengthen induction heating. J Mech Eng 54:216–226. https://doi.org/10.1051/mfreview/2019016

    Article  Google Scholar 

  41. Pei-Zhuo W, Zhan-Shu H, Yuan-xi Z, Shu-Sen Z (2017) Control of grinding surface residual stress of inconel 718. Procedia Eng 174:504–511. https://doi.org/10.1016/j.proeng.2017.01.174

    Article  Google Scholar 

  42. Seidel MW, Zösch A, Härtel K (2018) Grinding burn inspection: tools for supervising and objectifying of the testing process. Forsch Ingenieurwes 82:253–259. https://doi.org/10.1007/s10010-018-0270-4

    Article  Google Scholar 

  43. Lasaosa A, Gurruchaga K, Arizti F, Martinez-De-Guerenu A (2017) Induction hardened layer characterization and grinding burn detection by magnetic Barkhausen noise analysis. J Nondestr Eval 36:27. https://doi.org/10.1007/s10921-016-0388-y

    Article  Google Scholar 

  44. Aguiar PR, Serni PJA, Bianchi EC, Dotto FRL (2004) In-process grinding monitoring by acoustic emission. In: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, pp V–405. https://doi.org/10.1109/ICASSP.2004.1327133

  45. Yang Z, Yu Z, Xie C, Huang Y (2014) Application of Hilbert-Huang transform to acoustic emission signal for burn feature extraction in surface grinding process. Measurement 47:14–21. https://doi.org/10.1016/j.measurement.2013.08.036

    Article  Google Scholar 

  46. Ribeiro DMS, Aguiar PR, Fabiano LFG et al (2017) Spectra measurements using piezoelectric diaphragms to detect burn in grinding process. IEEE Trans Instrum Meas 66:3052–3063. https://doi.org/10.1109/TIM.2017.2731038

    Article  Google Scholar 

  47. Liu Y, Warkentin A, Bauer R, Gong Y (2013) Investigation of different grain shapes and dressing to predict surface roughness in grinding using kinematic simulations. Precis Eng 37:758–764. https://doi.org/10.1016/j.precisioneng.2013.02.009

    Article  Google Scholar 

  48. Zhao C, Li J, Wang W (2020) Forming mechanisms based simulation and prediction of grinding surface roughness for abrasive belt rail grinding. Procedia CIRP 87:503–508. https://doi.org/10.1016/j.procir.2020.02.077

    Article  Google Scholar 

  49. Cheng K, Huo D (2013) Micro-cutting: fundamentals and applications. John Wiley & Sons, Chichester, pp 14, 19–26, 293. https://doi.org/10.1002/9781118536605

  50. Zhao T, Shi Y, Lin X et al (2014) Surface roughness prediction and parameters optimization in grinding and polishing process for IBR of aero-engine. Int J Adv Manuf Tech 74:653–663. https://doi.org/10.1007/s00170-014-6020-3

    Article  Google Scholar 

  51. Ding N, Yu WZ (2015) Surface roughness prediction model based on AE in grinding. Appl Mech Mater 701:150–153. https://doi.org/10.4028/www.scientific.net/AMM.701-702.150

  52. Lipiński D, Bałasz B, Rypina Ł (2018) Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation. Int J Adv Manuf Tech 94:1335–1347. https://doi.org/10.1007/s00170-017-0949-y

    Article  Google Scholar 

  53. Huang X, Chai Z, Cao F et al (2022) Isotropic etching polishing of belt ground Inconel 718 to improve surface strengthening and quality. Surf Coat Tech 436:128292. https://doi.org/10.1016/j.surfcoat.2022.128292

    Article  Google Scholar 

  54. Du S, Jiang Z, Zhang D et al (2015) Microstructure of plastic deformation layer on grinding surface of GH4169 alloy. J Mech Eng 51:63–68

    Article  Google Scholar 

  55. Wu C, Guo W, Li R et al (2020) Thermal effect on oxidation layer evolution and phase transformation in grinding of Fe-Ni super alloy. Mater Lett 275:128072. https://doi.org/10.1016/j.matlet.2020.128072

    Article  Google Scholar 

  56. Zishan D, Beizhi L, Steven LY (2016) Material phase transformation at high heating rate during grinding. Mach Sci Tech 20:290–311. https://doi.org/10.1080/10910344.2016.1168929

    Article  Google Scholar 

  57. Duscha M, Eser A, Klocke F et al (2011) Modeling and simulation of phase transformation during grinding. Adv Mater Res 223:743–753. https://doi.org/10.4028/www.scientific.net/AMR.223.743

  58. Foeckerer T, Zaeh MF, Zhang OB (2013) A three-dimensional analytical model to predict the thermo-metallurgical effects within the surface layer during grinding and grind-hardening. Int J Heat Mass Transf 56:223–237. https://doi.org/10.1016/j.ijheatmasstransfer.2012.09.029

    Article  Google Scholar 

  59. Arunachalam N, Vijayaraghavan L (2014) Assessment of grinding wheel conditioning process using machine vision. In: 2014 International Conference on Prognostics and Health Management. IEEE, pp 1–5. https://doi.org/10.1109/ICPHM.2014.7036382

  60. Lipiński D, Kacalak W, Tomkowski R (2014) Methodology of evaluation of abrasive tool wear with the use of laser scanning microscopy. Scanning 36:53–63. https://doi.org/10.1002/sca.21088

    Article  Google Scholar 

  61. Huang X, Ren X, Yu H et al (2023) Partitioned abrasive belt condition monitoring based on a unified coefficient and image processing. J Intell Manuf 1–19. https://doi.org/10.1007/s10845-023-02083-7

  62. Guinea D, Ruiz A, Barrios LJ (1991) Multi-sensor integration—an automatic feature selection and state identification methodology for tool wear estimation. Comput Ind 17:121–130. https://doi.org/10.1016/0166-3615(91)90025-5

    Article  Google Scholar 

  63. Zeng H, Chen X (2002) Acoustic emission sensing and signal processing for machining monitoring and control. Advanced Automation Techniques in Adaptive Material Processing 09:91–124. https://doi.org/10.1142/9789812777775_0004

  64. Alexandre FA, Lopes WN, LofranoDotto FR et al (2018) Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model. Int J Adv Manuf Tech 96:67–79. https://doi.org/10.1007/s00170-018-1582-0

    Article  Google Scholar 

  65. Martins CHR, Aguiar PR, Frech A, Bianchi EC (2013) Tool condition monitoring of single-point dresser using acoustic emission and neural networks models. IEEE Trans Instrum Meas 63:667–679. https://doi.org/10.1109/TIM.2013.2281576

    Article  Google Scholar 

  66. Moia DFG, Thomazella IH, Aguiar PR et al (2015) Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks. J Braz Soc Mech Sci 37:627–640. https://doi.org/10.1007/s40430-014-0191-6

    Article  Google Scholar 

  67. Yang Z, Yu Z (2012) Grinding wheel wear monitoring based on wavelet analysis and support vector machine. Int J Adv Manuf Tech 62:107–121. https://doi.org/10.1007/s00170-011-3797-1

    Article  Google Scholar 

  68. Hosokawa A, Mashimo K, Yamada K, Ueda T (2004) Evaluation of grinding wheel surface by means of grinding sound discrimination. JSME Int J, Ser C 47:52–58. https://doi.org/10.1299/jsmec.47.52

    Article  Google Scholar 

  69. Zhang X, Chen H, Xu J et al (2018) A novel sound-based belt condition monitoring method for robotic grinding using optimally pruned extreme learning machine. J Mater Process Tech 260:9–19. https://doi.org/10.1016/j.jmatprotec.2018.05.013

    Article  Google Scholar 

  70. Chen J, Chen H, Xu J et al (2018) Acoustic signal-based tool condition monitoring in belt grinding of nickel-based superalloys using RF classifier and MLR algorithm. Int J Adv Manuf Tech 98:859–872. https://doi.org/10.1007/s00170-018-2270-9

    Article  Google Scholar 

  71. Gao K, Chen H, Zhang X et al (2019) A novel material removal prediction method based on acoustic sensing and ensemble XGBoost learning algorithm for robotic belt grinding of Inconel 718. Int J Adv Manuf Tech 105:217–232. https://doi.org/10.1007/s00170-019-04170-7

    Article  Google Scholar 

  72. Lezanski P (2001) An intelligent system for grinding wheel condition monitoring. J Mater Process Tech 109:258–263. https://doi.org/10.1016/S0924-0136(00)00808-6

    Article  Google Scholar 

  73. Wu D, Jennings C, Terpenny J et al (2017) A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J Manuf Sci Eng 139(7):071018. https://doi.org/10.1115/1.4036350

    Article  Google Scholar 

  74. Pandiyan V, Caesarendra W, Tjahjowidodo T, Tan HH (2018) In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. J Manuf Process 31:199–213. https://doi.org/10.1016/j.jmapro.2017.11.014

    Article  Google Scholar 

  75. Li L, Ren X, Feng H et al (2021) A novel material removal rate model based on single grain force for robotic belt grinding. J Manuf Process 68:1–12. https://doi.org/10.1016/j.jmapro.2021.05.029

    Article  Google Scholar 

  76. Zhu W-L, Yang Y, Li HN et al (2019) Theoretical and experimental investigation of material removal mechanism in compliant shape adaptive grinding process. Int J Mach Tools Manuf 142:76–97. https://doi.org/10.1016/j.ijmachtools.2019.04.011

    Article  Google Scholar 

  77. Zhang Y, Li C, Ji H et al (2017) Analysis of grinding mechanics and improved predictive force model based on material-removal and plastic-stacking mechanisms. Int J Mach Tools Manuf 122:81–97. https://doi.org/10.1016/j.ijmachtools.2017.06.002

    Article  Google Scholar 

  78. Ren X, Huang X, Feng H et al (2021) A novel energy partition model for belt grinding of Inconel 718. J Manuf Process 64:1296–1306. https://doi.org/10.1016/j.jmapro.2021.02.052

    Article  Google Scholar 

  79. Gahr K-HZ (1981) Formation of wear debris by the abrasion of ductile metals. Wear 74:353–373. https://doi.org/10.1016/0043-1648(81)90173-3

    Article  Google Scholar 

  80. Kopac J, Krajnik P (2006) High-performance grinding—a review. J Mater Process Tech 175:278–284. https://doi.org/10.1016/j.jmatprotec.2005.04.010

    Article  Google Scholar 

  81. Zhang L, Tanaka H (1998) Atomic scale deformation in silicon monocrystals induced by two-body and three-body contact sliding. Tribol Int 31:425–433. https://doi.org/10.1016/S0301-679X(98)00064-4

    Article  Google Scholar 

  82. Jin XL, Zhang LC (2012) A statistical model for material removal prediction in polishing. Wear 274:203–211. https://doi.org/10.1016/j.wear.2011.08.028

    Article  Google Scholar 

  83. Wu S, Kazerounian K, Gan Z, Sun Y (2014) A material removal model for robotic belt grinding process. Mach Sci Tech 18:15–30. https://doi.org/10.1080/10910344.2014.863623

    Article  Google Scholar 

  84. He QW, Yang X, Wu XH et al (2017) Research on material removal of belt polishing for blade complex surface. Curr Trends Computer Sci Mech Automat 2:319–333. https://doi.org/10.1515/9783110584998-035

    Article  Google Scholar 

  85. Cheng K, Shao Y, Bodenhorst R, Jadva M (2017) Modeling and simulation of material removal rates and profile accuracy control in abrasive flow machining of the integrally bladed rotor blade and experimental perspectives. Transact ASME: J Manuf Sci Eng 139(12):121020. https://doi.org/10.1115/1.4038027

    Article  Google Scholar 

  86. Jo W, Lee SB, Lee S et al (2016) A study of material removal characteristics by friction monitoring system of sapphire wafer in single side DMP. Tribol Lubr 32:56–60. https://doi.org/10.9725/kstle.2016.32.2.56

    Article  Google Scholar 

  87. Zhang X, Cabaravdic M, Kneupner K, Kuhlenkoetter B (2004) Real-time simulation of robot controlled belt grinding processes of sculptured surfaces. Int J Adv Robot Syst 1:12. https://doi.org/10.5772/5627

    Article  Google Scholar 

  88. Zhang X, Kuhlenkötter B, Kneupner K (2005) An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding. Int J Mach Tools Manuf 45:641–648. https://doi.org/10.1016/j.ijmachtools.2004.10.006

    Article  Google Scholar 

  89. Unune DR, Mali HS (2016) Artificial neural network–based and response surface methodology–based predictive models for material removal rate and surface roughness during electro-discharge diamond grinding of Inconel 718. Proc Inst Mech Eng B J Eng Manuf 230:2082–2091. https://doi.org/10.1177/0954405415619347

    Article  Google Scholar 

  90. Yang A, Han Y, Pan Y et al (2017) Optimum surface roughness prediction for titanium alloy by adopting response surface methodology. Results Phys 7:1046–1050. https://doi.org/10.1016/j.rinp.2017.02.027

    Article  Google Scholar 

  91. Wang Y, Huang X, Ren X et al (2022) In-process belt-image-based material removal rate monitoring for abrasive belt grinding using CatBoost algorithm. Int J Adv Manuf Tech 123:2575–2591. https://doi.org/10.1007/s00170-022-10341-w

  92. Jin M, Lee J, Tsagarakis NG (2016) Model-free robust adaptive control of humanoid robots with flexible joints. IEEE T Ind Electron 64:1706–1715. https://doi.org/10.1109/TIE.2016.2588461

    Article  Google Scholar 

  93. Hou Z, Zhu Y (2013) Controller-dynamic-linearization-based model free adaptive control for discrete-time nonlinear systems. IEEE Trans Industr Inform 9:2301–2309. https://doi.org/10.1109/TII.2013.2257806

    Article  Google Scholar 

  94. Hou Z, Jin S (2013) Model free adaptive control: theory and applications. CRC press, Boca Raton, pp 207–239. https://doi.org/10.1201/b15752

  95. Song Y, Liang W, Yang Y (2012) A method for grinding removal control of a robot belt grinding system. J Intell Manuf 23:1903–1913. https://doi.org/10.1007/s10845-011-0508-6

    Article  Google Scholar 

  96. Parenti P, Leonesio M, Bianchi G (2016) Model-based adaptive process control for surface finish improvement in traverse grinding. Mechatronics 36:97–111

    Article  Google Scholar 

  97. Song K, Xiao G, Chen S et al (2023) A new force-depth model for robotic abrasive belt grinding and confirmation by grinding of the Inconel 718 alloy. Robot Comput Integr Manuf 80:102483. https://doi.org/10.1016/j.mechatronics.2016.04.001

    Article  Google Scholar 

  98. Zhu D, Luo S, Yang L et al (2015) On energetic assessment of cutting mechanisms in robot-assisted belt grinding of titanium alloys. Tribol Int 90:55–59. https://doi.org/10.1016/j.triboint.2015.04.004

    Article  Google Scholar 

  99. Suresh G, Vasu V, Raghavendra G (2018) Optimization of input parameters on erosion wear rate of PTFE/HNT filled nanocomposites. Mater Today Proc 5:1462–1469. https://doi.org/10.1016/j.matpr.2017.11.234

    Article  Google Scholar 

  100. Jiang J, Ge P, Sun S et al (2016) From the microscopic interaction mechanism to the grinding temperature field: an integrated modelling on the grinding process. Int J Mach Tools Manuf 110:27–42. https://doi.org/10.1016/j.ijmachtools.2016.08.004

    Article  Google Scholar 

  101. Cebula A, Taler J, Ocłoń P (2018) Heat flux and temperature determination in a cylindrical element with the use of Finite Volume Finite Element Method. Int J Therm Sci 127:142–157. https://doi.org/10.1016/j.ijthermalsci.2018.01.022

    Article  Google Scholar 

  102. Markopoulos AP, Karkalos NE, Manolakos DE (2016) Molecular dynamics study of abrasive grain morphology and orientation in nanometric grinding. Key Eng Mater 686:7–12. https://doi.org/10.4028/www.scientific.net/KEM.686.7

  103. Palanikumar K, Latha B, Senthilkumar VS, Davim JP (2013) Application of artificial neural network for the prediction of surface roughness in drilling GFRP composites. Mater Sci Forum 766:21–36. https://doi.org/10.4028/www.scientific.net/MSF.766.21

  104. Park JW, Cho HU, Chung CW et al (2012) Modeling and grinding large sculptured surface by robotic digitization. J Mech Sci Tech 26:2087–2091. https://doi.org/10.1007/s12206-012-0520-6

    Article  Google Scholar 

  105. Brewe DE, Hamrock BJ (1977) Simplified solution for elliptical-contact deformation between two elastic solids. J Tribol 99(4):485–487. https://doi.org/10.1115/1.3453245

    Article  Google Scholar 

  106. Wang YJ, Huang Y, Chen YX, Yang ZS (2016) Model of an abrasive belt grinding surface removal contour and its application. Int J Adv Manuf Tech 82:2113–2122. https://doi.org/10.1007/s00170-015-7484-5

    Article  Google Scholar 

  107. Wu S, Kazerounian K, Gan Z, Sun Y (2013) A simulation platform for optimal selection of robotic belt grinding system parameters. Int J Adv Manuf Tech 64:447–458. https://doi.org/10.1007/s00170-012-4030-6

    Article  Google Scholar 

  108. Schroder A, Blum H, Rademacher A, Kleemann H (2011) Mixed FEM of higher order for contact problems with friction. Int J Numer Anal Model 8:302–323. https://doi.org/10.1080/10652469.2010.511211

    Article  MathSciNet  MATH  Google Scholar 

  109. Weinert K, Blum H, Kuhlenkötter B et al (2007) New methods for calculating the force distribution within belt grinding processes. Prod Eng 1:285–289. https://doi.org/10.1007/s11740-007-0054-4

    Article  Google Scholar 

  110. Blum H, Suttmeier F-T (2000) An adaptive finite element discretisation for a simplified Signorini problem. Calcolo 37:65–77. https://doi.org/10.1007/s100920070008

    Article  MathSciNet  MATH  Google Scholar 

  111. Zhang X, Kneupner K, Kuhlenkötter B (2006) A new force distribution calculation model for high-quality production processes. Int J Adv Manuf Tech 27:726–732. https://doi.org/10.1007/s00170-004-2229-x

    Article  Google Scholar 

  112. Malkin S, Guo C (2007) Thermal analysis of grinding. CIRP annals 56:760–782. https://doi.org/10.1016/j.cirp.2007.10.005

    Article  Google Scholar 

  113. Li HN, Axinte D (2017) On a stochastically grain-discretised model for 2D/3D temperature mapping prediction in grinding. Int J Mach Tools Manuf 116:60–76. https://doi.org/10.1016/j.ijmachtools.2017.01.004

    Article  Google Scholar 

  114. Shao Y, Fergani O, Li B, Liang SY (2016) Residual stress modeling in minimum quantity lubrication grinding. Int J Adv Manuf Tech 83:743–751. https://doi.org/10.1007/s00170-015-7527-y

    Article  Google Scholar 

  115. Ren X, Chai Z, Xu J et al (2020) A new method to achieve dynamic heat input monitoring in robotic belt grinding of Inconel 718. J Manuf Process 57:575–588. https://doi.org/10.1016/j.jmapro.2020.07.018

    Article  Google Scholar 

  116. Ramanath S, Shaw MC (1988) Abrasive grain temperature at the beginning of a cut in fine grinding. J Manuf Sci Eng 110(1):15–18. https://doi.org/10.1115/1.3187835

    Article  Google Scholar 

  117. Kohli S, Guo C, Malkin S (1995) Energy partition to the workpiece for grinding with aluminum oxide and CBN abrasive wheels. J Manuf Sci Eng 117(2):160–168. https://doi.org/10.1115/1.2803290

    Article  Google Scholar 

  118. Rowe WB, Pettit JA, Boyle A, Moruzzi JL (1988) Avoidance of thermal damage in grinding and prediction of the damage threshold. CIRP Ann 37:327–330. https://doi.org/10.1016/S0007-8506(07)61646-1

    Article  Google Scholar 

  119. Rowe WB, Morgan MN, Black SCE, Mills B (1996) A simplified approach to control of thermal damage in grinding. CIRP Ann 45:299–302. https://doi.org/10.1016/S0007-8506(07)63067-4

    Article  Google Scholar 

  120. Rowe WB, Black SCE, Mills B et al (1997) Grinding temperatures and energy partitioning. Proc R Soc London Series A: Math, Phys Eng Sci 453:1083–1104. https://doi.org/10.1098/rspa.1997.0061

    Article  Google Scholar 

  121. Wang S-B, Kou H-S (2004) Selections of working conditions for creep feed grinding. Part (I)–thermal partition ratios. Int J Adv Manuf Tech 23:700–706. https://doi.org/10.1007/s00170-003-1643-9

    Article  Google Scholar 

  122. Pang J, Li B, Liu Y, Wu C (2016) Heat flux distribution model in the cylindrical grinding contact area. Procedia Manuf 5:158–169. https://doi.org/10.1016/j.promfg.2016.08.015

    Article  Google Scholar 

  123. Yin G, Marinescu ID (2017) A heat transfer model of grinding process based on energy partition analysis and grinding fluid cooling application. J Manuf Sci Eng 12:121015. https://doi.org/10.1115/1.4037241

    Article  Google Scholar 

  124. Rowe WB, Black SCE, Mills B et al (1995) Experimental investigation of heat transfer in grinding. CIRP Ann 44:329–332. https://doi.org/10.1016/S0007-8506(07)62336-1

    Article  Google Scholar 

  125. Rowe WB, Jin T (2001) Temperatures in high efficiency deep grinding (HEDG). CIRP Ann 50:205–208. https://doi.org/10.1016/S0007-8506(07)62105-2

    Article  Google Scholar 

  126. Kim H-J, Kim N-K, Kwak J-S (2006) Heat flux distribution model by sequential algorithm of inverse heat transfer for determining workpiece temperature in creep feed grinding. Int J Mach Tools Manuf 46:2086–2093. https://doi.org/10.1016/j.ijmachtools.2005.12.007

    Article  Google Scholar 

  127. Li B, Zhu D, Pang J, Yang J (2011) Quadratic curve heat flux distribution model in the grinding zone. Int J Adv Manuf Tech 54:931–940. https://doi.org/10.1007/s00170-010-2990-y

    Article  Google Scholar 

  128. Ren X, Huang X, Chai Z et al (2021) A study of dynamic energy partition in belt grinding based on grinding effects and temperature dependent mechanical properties. J Mater Process Tech 294:117112. https://doi.org/10.1016/j.jmatprotec.2021.117112

    Article  Google Scholar 

  129. Liu C, Ding W, Li Z, Yang C (2017) Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm. Int J Adv Manuf Tech 89:2277–2285. https://doi.org/10.1007/s00170-016-9267-z

    Article  Google Scholar 

  130. Markopoulos AP, Kundrák J (2016) FEM/AI models for the simulation of precision grinding. Manuf Tech 16:384–390. https://doi.org/10.21062/ujep/x.2016/a/1213-2489/MT/16/2/384

  131. Miao Q, Lu M, Ding W et al (2023) Creep-feed grinding of single crystal nickel-base turbine blade fir-tree roots: tool wear, grinding force, temperature, and surface integrity. Int J Adv Manuf Tech 126:1453–1470. https://doi.org/10.1007/s00170-023-11188-5

    Article  Google Scholar 

  132. Ajmal KM, Yi R, Zhan Z et al (2022) A novel finishing approach for 3D printed inconel 718 by utilizing isotropic electrochemical etching. J Mater Process Tech 299:117356. https://doi.org/10.1016/j.jmatprotec.2021.117356

    Article  Google Scholar 

  133. Gäbler J, Pleger S (2010) Precision and micro CVD diamond-coated grinding tools. Int J Mach Tools Manuf 50:420–424. https://doi.org/10.1016/j.ijmachtools.2009.10.008

    Article  Google Scholar 

Download references

Funding

This work was supported by the Guangzhou Risong Intelligent Technology Holding Co., Ltd. China (grant number: 2020-L021). Author Xiaoqi Chen has received research support from the above company.

Author information

Author notes

  1. Xukai Ren and Xiaokang Huang contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Xukai Ren, Xiaokang Huang, Kaiyuan Gao, Luming Xu, Lufeng Li, Hengjian Feng, Xiaoqiang Zhang, Huabin Chen, Ze Chai & Xiaoqi Chen

  2. Shaoxing Special Equipment Testing Institute, Shaoxing, 312071, China

    Xukai Ren

  3. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, 511442, China

    Xiaoqi Chen

Authors
  1. Xukai Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaokang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaiyuan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luming Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lufeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hengjian Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaoqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Huabin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ze Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiaoqi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Xukai Ren, Xiaokang Huang, Kaiyuan Gao, Luming Xu, Lufeng Li, Hengjian Feng, and Xiaoqiang Zhang. The first draft of the manuscript was written by Xukai Ren and revised by Xiaokang Huang, Huabin Chen, Ze Chai, and Xiaoqi Chen. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Ze Chai or Xiaoqi Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, X., Huang, X., Gao, K. et al. A review of recent advances in robotic belt grinding of superalloys. Int J Adv Manuf Technol 127, 1447–1482 (2023). https://doi.org/10.1007/s00170-023-11574-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-11574-z

Keywords

Search

Navigation