Characterization of ultrasonic-assisted grinding surface via the evaluation of the autocorrelation function

  • Lin Li
  • Jinyuan TangEmail author
  • Yuqin Wen
  • Wen Shao


The grinding morphology of metal materials is mainly determined by the geometric interference of abrasive particles, which means the machining marks left by the manufacturing process are the key components of the surface structure. However, few researches have been focused on the spatial structure of the grinding morphology. A general form of areal autocorrelative function (AACF) was proposed to characterize the conventional and ultrasonic-assisted grinding surfaces. Firstly, the 3D surfaces under different machining conditions including the axial ultrasonic-assisted grinding, vertical ultrasonic-assisted grinding, and elliptical ultrasonic-assisted grinding were simulated based on the grinding kinematics analysis. Subsequently, the features of the corresponding autocorrelation functions were analyzed and the expression form was given. Finally, the conventional grinding and axial ultrasonic-assisted grinding tests were performed to validate the AACF form. The results showed that the expression form was generally consistent with both the AACFs of the simulated and measured surfaces. The AACF family proposed in this study may serve as an effective and novel way to describe the spatial characteristics of the grinding especially the ultrasonic-assisted grinding surfaces.


Ultrasonic-assisted grinding Surface topography Areal autocorrelative function 


Funding information

This work is supported by the National Natural Science Foundation of China (NSFC) through Grants No.51535012, 51705542, and U1604255.


  1. 1.
    Zhu Z, Dhokia VG, Nassehi A, Newman ST (2013) A review of hybrid manufacturing processes – state of the art and future perspectives. Int J Comput Integr Manuf 26(7):596–615CrossRefGoogle Scholar
  2. 2.
    Tawakoli T, Azarhoushang B (2008) Influence of ultrasonic vibrations on dry grinding of soft steel. Int J Mach Tools Manuf 48(14):1585–1591CrossRefGoogle Scholar
  3. 3.
    Denkena B, Friemuth T, Reichstein M, Tönshoff H K (2003) Potentials of different process kinematics in micro grinding. CIRP Ann Manuf Technol 52(1):463–466CrossRefGoogle Scholar
  4. 4.
    Yan YY, Zhao B, Liu JL (2009) Ultraprecision surface finishing of nano-ZrO2 ceramics using two-dimensional ultrasonic assisted grinding. Int J Adv Manuf Technol 43(5-6):462– 467CrossRefGoogle Scholar
  5. 5.
    Unyanin AN, Khusainov AS (2016) Study of forces during ultrasonic vibration assisted grinding. Procedia Eng 150:1000–1006CrossRefGoogle Scholar
  6. 6.
    Liang Z, Wu Y, Wang X, Zhao W (2010) A new two-dimensional ultrasonic assisted grinding (2D-UAG) method and its fundamental performance in monocrystal silicon machining. Int J Mach Tools Manuf 50(8):728–736CrossRefGoogle Scholar
  7. 7.
    Leach RK, Giusca CL, Haitjema H, Evans C, Jiang X (2015) Calibration and verification of areal surface texture measuring instruments. CIRP Ann 64(2):797–813CrossRefGoogle Scholar
  8. 8.
    Gropper D, Wang L, Harvey TJ (2016) Hydrodynamic lubrication of textured surfaces: A review of modeling techniques and key findings. Tribol Int 94:509–529CrossRefGoogle Scholar
  9. 9.
    Uddin MS, Liu YW (2016) Design and optimization of a new geometric texture shape for the enhancement of hydrodynamic lubrication performance of parallel slider surfaces. Biosurf Biotribol 2(2):59–69CrossRefGoogle Scholar
  10. 10.
    Gachot C, Rosenkranz A, Hsu SM, Costa HL (2017) A critical assessment of surface texturing for friction and wear improvement. Wear 372373:21–41CrossRefGoogle Scholar
  11. 11.
    Chen CS, Tang JY, Chen HF, Zhao B (2018) An active manufacturing method of surface micro structure based on ordered grinding wheel and ultrasonic-assisted grinding. Int J Adv Manuf Technol 97(5-8):1627–1635CrossRefGoogle Scholar
  12. 12.
    Leach R (2014) Characterisation of areal surface texture. Characterisation of Areal Surface Texture. Springer, BerlinGoogle Scholar
  13. 13.
    Chen HF, Tang JY, Shao W, Zhao B (2018) An investigation on surface functional parameters in ultrasonic-assisted grinding of soft steel. Int J Adv Manuf Technol 97(5-8):2697– 2702CrossRefGoogle Scholar
  14. 14.
    Wen YQ, Tang JY, Zhou W, Zhu CC (2019) Study on contact performance of ultrasonic-assisted grinding surface. Ultrasonics 91:193–200CrossRefGoogle Scholar
  15. 15.
    Liao DR, Shao W, Tang JY, Li JP (2018) An improved rough surface modeling method based on linear transformation technique. Tribol Int 119:786–794CrossRefGoogle Scholar
  16. 16.
    Pérez-Ràfols F, Almqvist A (2019) Generating randomly rough surfaces with given height probability distribution and power spectrum. Tribol Int 131:591–604CrossRefGoogle Scholar
  17. 17.
    Nayak PR (1971) Random process model of rough surfaces. Wear 26(3):398Google Scholar
  18. 18.
    Liao DR, Shao W, Tang JY, Li JP, Tao X (2018) Numerical generation of grinding wheel surfaces based on time series method. Int J Adv Manuf Technol 94(1-4):561–569CrossRefGoogle Scholar
  19. 19.
    Manesh KK, Ramamoorthy B, Singaperumal M (2010) Numerical generation of anisotropic 3d non-gaussian engineering surfaces with specified 3d surface roughness parameters. Wear 268(11):1371–1379CrossRefGoogle Scholar
  20. 20.
    Staufert G (1979) Description of roughness profiles by separating the random and periodic components[J]. Wear 57(1):185–194CrossRefGoogle Scholar
  21. 21.
    Loukjanov VI (1979) Evaluation of the autocorrelation functions used when investigating surface roughness[J]. ARCHIVE: J Mech Eng Sci 1959-1982 (vols 1-23) 21(2):105–113Google Scholar
  22. 22.
    Sun SY, Tang JY, Shao W, Chen CS, Liu YX (2019) Research on the matching relationship between ultrasonic-assisted grinding parameters and workpiece surface roughness. Int J Adv Manuf Technol 102(1-4):487–496CrossRefGoogle Scholar
  23. 23.
    Zhou WH, Tang JY, Chen HF, Zhu CC, Shao W (2018) A comprehensive investigation of plowing and grain-workpiece micro interactions on 3D ground surface topography. Int J Mech Sci 144:639–653CrossRefGoogle Scholar
  24. 24.
    Rom M, Brakhage KH, Barth S, Wrobel C, Mattfeld P, Klocke F (2018) Mathematical modeling of ceramic bond bridges in grinding wheels. Math Comput Simul 147:220–236MathSciNetCrossRefGoogle Scholar
  25. 25.
    Malkin S, Guo CS (2008) Grinding technology: theory and application of machining with abrasives. Industrial Press Inc.Google Scholar
  26. 26.
    Koshy P, Jain VK, Lal GK (1993) A model for the topography of diamond grinding wheels. Wear 169 (2):237–242CrossRefGoogle Scholar
  27. 27.
    Aich U, Banerjee S (2017) Characterizing topography of EDM generated surface by time series and autocorrelation function. Tribol Int 111:73–90CrossRefGoogle Scholar
  28. 28.
    Patir N (1978) A numerical procedure for random generation of rough surfaces. Wear 47(2):263–277CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Mechanical and Electrical EngineeringCentral South UniversityChangshaChina
  2. 2.State Key Laboratory of High Performance Complex ManufacturingCentral South UniversityChangshaChina

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