Study on surface generation in nano-cutting by large-scale molecular dynamics simulation

  • Feifei Xu
  • Fengzhou FangEmail author
  • Xiaodong Zhang


Large-scale molecular dynamics simulations are employed in investigating the nano-cutting process. A cutting tool with 1-μm nose radius and 20-nm edge radius is applied in nano-cutting of silicon with cutting distance attaining 400 nm. The mentioned cutting parameters are relatively larger than those used in the former research studies which would help to obtain the results with relatively high confidence coefficient. The results show that when the UCT is smaller or similar to the stagnation height, the extrusion accompanied with the side flow dominates the material removal mechanism. The generated side flow will deteriorate the generated surface roughness. When cutting at {111}<11-2> direction, bct5-Si phase forms and a part of them is left in the machined subsurface. When cutting at {100}<001> direction, the formed bct5-Si and S-II phases transform to the a-Si phase, when the cutting tool passes through.


Nano-cutting Phase transformation Side flow Stagnation region Recovery 


Funding information

The authors thank the support of Science Challenge Project (No. TZ2018006-0205-01), the National Key Research and Development Program of China (Grant Nos. 2016YFB1102200 and 2017YFA0701200), the National Natural Science Foundation (Grant No. 51805499), and the “111” project by the State Administration of Foreign Experts Affairs and the Ministry of Education of China (Grant No. B07014).


  1. 1.
    Xu FF, Fang FZ, Zhang XD (2017) Hard particle effect on surface generation in nano-cutting. Appl Surf Sci 425:1020–1027. CrossRefGoogle Scholar
  2. 2.
    Fang FZ (1998) Nano-turning of single crystal silicon. J Mater Process Technol 82(1–3):95–101. CrossRefGoogle Scholar
  3. 3.
    Fang FZ, Wu H, Liu YC (2005) Modelling and experimental investigation on nanometric cutting of monocrystalline silicon. Int J Mach Tools Manuf 45(15):1681–1686. CrossRefGoogle Scholar
  4. 4.
    Fang FZ, Xu FF (2018) Recent advances in micro/nano-cutting: effect of tool edge and material properties. Nanomanuf Metrol 1(1):4–31. CrossRefGoogle Scholar
  5. 5.
    Xu FF, Fang FZ, Zhang XD (2018) Effects of recovery and side flow on surface generation in nano-cutting of single crystal silicon. Comput Mater Sci 143:133–142. CrossRefGoogle Scholar
  6. 6.
    Pekelharing AJ, Gieszen CA (1971) Material side flow in finish turning. Annals of the CIRP 20(1):21–22Google Scholar
  7. 7.
    Grzesik W (1996) A revised model for predicting surface roughness in turning. Wear 194(1):143–148. CrossRefGoogle Scholar
  8. 8.
    Liu K, Melkote SN (2006) Effect of plastic side flow on surface roughness in micro-turning process. Int J Mach Tools Manuf 46(14):1778–1785. CrossRefGoogle Scholar
  9. 9.
    Zong WJ, Huang YH, Zhang YL, Sun T (2014) Conservation law of surface roughness in single point diamond turning. Int J Mach Tools Manuf 84:58–63. CrossRefGoogle Scholar
  10. 10.
    Schaal N, Kuster F, Wegener K (2015) Springback in metal cutting with high cutting speeds. Procedia CIRP 31:24–28CrossRefGoogle Scholar
  11. 11.
    Shi J, Wang YC, Yang XP (2013) Nano-scale machining of polycrystalline coppers - effects of grain size and machining parameters. Nanoscale Res Lett 8(1):500. CrossRefGoogle Scholar
  12. 12.
    Wang ZG, Chen JX, Wang GL, Bai QS, Liang YC (2017) Anisotropy of single-crystal silicon in nanometric cutting. Nanoscale Res Lett 12(1):300. CrossRefGoogle Scholar
  13. 13.
    Goel S, Martinez FD, Chavoshi SZ, Khatri N, Giusca C (2018) Molecular dynamics simulation of the elliptical vibration-assisted machining of pure iron. J Micromanuf 1(1):6–19. CrossRefGoogle Scholar
  14. 14.
    Goel S, Stukowski A, Luo XC, Agrawal A, Robert LR (2013) Anisotropy of single-crystal 3C–SiC during nanometric cutting. Model Simul Mater Sci Eng 21(6):065004CrossRefGoogle Scholar
  15. 15.
    Lai M, Zhang XD, Fang FZ, Bi MH (2017) Fundamental investigation on partially overlapped nano-cutting of monocrystalline germanium. Precis Eng 49:160–168. CrossRefGoogle Scholar
  16. 16.
    Alexander S (2010) Visualization and analysis of atomistic simulation data with OVITO–the open visualization tool. Model Simul Mater Sci Eng 18(1):015012CrossRefGoogle Scholar
  17. 17.
    Kim DE, Oh SI (2006) Atomistic simulation of structural phase transformations in monocrystalline silicon induced by nanoindentation. Nanotechnology 17(9):2259–2265CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Institute of Machinery Manufacturing TechnologyChina Academy of Engineering PhysicsMianyangChina
  2. 2.State Key Laboratory of Precision Measuring Technology & Instruments, Centre of MicroNano Manufacturing TechnologyTianjin UniversityTianjinChina
  3. 3.School of Mechanical & Materials Engineering, MNMT-DublinUniversity College DublinDublinIreland

Personalised recommendations