Optimization of Effective Parameters on the Nano-scale Cutting Process of Monocrystalline Copper Using Molecular Dynamic

Document Type : Original Research Paper


1 Mechanical Engineering Department, Arak University, Arak, Iran.

2 Mechanical Engineering Department, Arak University of Technology, Arak, Iran.


Machining in the scale of nanometer and investigating its behavior is premier in the field of machining. Molecular dynamics is a new robust tool to investigate controlling mechanisms in atomic scales, complex dislocation, and grain-boundary in severely deformed workpieces with the submicron dimensioning; consequently, process simulation was performed by molecular dynamics method. In this study, some useful parameters of tool geometry in orthogonal cutting of monocrystalline copper were investigated. With this end in view, relief angle, rake angle, and tooltip radius were considered as influential geometrical parameters of orthogonal cutting of monocrystalline copper. By using the Response Surface Method (RSM), the variation effect of input parameters was studied on the cutting output parameters like cutting force, temperature, and hydrostatic stress all in nanometer precision. Furthermore, with mathematical modeling using a second-order linear regression equation fitted to the process outputs, single objective and multi-objective optimization
of the cutting process was followed. 


[1] Q. Kang, X. Fang, L. Sun, J. Ding, Z. Jiang, Research on mechanism of nanoscale cutting with arc trajectory for monocrystalline silicon based on molecular dynamics simulation, Comput. Mater. Sci., 170 (2019) 109175.
[2] P.Z. Zhu, Y.Z. Hu, T.B. Ma, H. Wang, Study of AFM-based nanometric cutting process using molecular dynamics, Appl. Surf. Sci., 256(23) (2010) 7160-7165.
[3] H. Dai, H. Du, J. Chen, G. Chen, Investigation of tool geometry in nanoscale cutting single-crystal
copper by molecular dynamics simulation, Proceedings of the Institution of Mechanical Engineers, J.
Eng. Tribol. Part J, 233(8) (2019) 1208-1220.
[4] A. Sharma, D. Datta, R. Balasubramaniam, Molecular dynamics simulation to investigate the orientation effects on nanoscale cutting of single crystal copper, Comput. Mater. Sci., 153 (2018) 241-250.
[5] R. Komanduri, N. Chandrasekarans, L.M. Raf, Molecular dynamics simulation of the nanometric cutting of silicon, Philos. Mag. B, 81(12) (2001) 1989-2019.
[6] R. Komanduri, N. Chandrasekarans, L.M. Raf, Effect of tool geometry in nanometric cutting: a molecular dynamics simulation approach, Wear, 219(1) (1998) 84-97.
[7] R. Promyoo, H. El-Mounayri, X. Yang, Molecular dynamics simulation of nanometric cutting, Mach. Sci. Technol., 14(4) (2010) 423-439.
[8] Y.Y. Ye, R. Biswas, J.R. Morris, A. Bastawros, A. Chandra, Molecular dynamics simulation of nanoscale machining of copper, Nanotechnology, 14(3) (2003) 390-396.
[9] R. Komanduri, N. Chandrasekaran, M. Raff, M.D. Simulation of nanometric cutting of single crystal
aluminum-effect of crystal orientation and direction of cutting, Wear, 242(1-2) (2000) 60-88.
[10] Q.X. Pei,C. Lu, F.Z. Fang, H.Wu, Nanometric cutting of copper: A molecular dynamics study. Comput. Mater. Sci., 37(4) (2006) 434-441.
[11] C. Ji, J. Shi, Z. Liu,Y. Wang, Comparison of tool–chip stress distributions in nano-machining of monocrystalline silicon and copper, Int. J. Mech. Sci., 77 (2013) 30-39.
[12] B. Lin, S.Y. Yu, S.X. Wang, An experimental study on molecular dynamics simulation in nanometer grinding, J. Mater. Process. Technol., 138(1-3) (2003) 484-488.
[13] Z.J. Choong, D. Huo, P. Degenaar, A. O’Neill, Edge chipping minimisation strategy for milling of monocrystalline silicon: A molecular dynamics study, Appl. Surf. Sci., 486 (2019) 166-178.
[14] M.H.Wang, S.Y. You, F.N. Wang, Q. Liu, Effect of dynamic adjustment of diamond tools on nanocutting behavior of single-crystal silicon, Appl. Phys. A, 125(3) (2019) 176.
[15] R.K. Pandey, S. Panda, Multi-performance optimization of bone drilling using Taguchi method based on membership function, Measurement, 59 (2015) 9-13.
[16] S. Plimpton, Fast Parallel Algorithms for ShortRange Molecular Dynamics, J. Comput. Phys., 117(1) (1995) 1-19.
[17] A. Stukowski, Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool, Modell. Simul. Mater. Sci. Eng., 18(1) (2009) 015012.
[18] Y. Liu, B. Li, L. Kong, A molecular dynamics investigation into nanoscale scratching mechanism of polycrystalline silicon carbide, Comput. Mater. Sci., 148 (2018) 76-86.
[19] V. Tahmasbi, M. Ghoreishi, M. Zolfaghari, Investigation, sensitivity analysis, and multi-objective
optimization of effective parameters on temperature and force in robotic drilling cortical bone. Proc. Inst.  Mech. Eng. H: J. Eng. Med., 231(11) (2017) 1012-1024.
[20] A. Nekahi, K. Dehghani, Modeling the thermomechanical effects on baking behavior of low carbon steels using response surface methodology, Mater. Des., 31(8) (2010) 3845-3851.
[21] V. Tahmasbi, M. Ghoreishi, M. Zolfaghari, Sensitivity analysis of temperature and force in robotic bone drilling process using Sobol statistical method, Biotechnol. Biotechnol. Equip., 32(1) (2018) 130-141.
[22] V. Tahmasbi, M. Safari, J. Joudaki, Statistical modeling, Sobol sensitivity analysis and optimization of single-tip tool geometrical parameters in the cortical bone machining process. Proc. Inst. Mech. Eng. H: J. Eng. Med., 234(1) (2020) 28-38.
[23] D.C. Montgomery, Design and Analysis of Experiments, John Wiley and Sons Publisher, (2008).