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Applied Physics A

, Volume 115, Issue 4, pp 1263–1279 | Cite as

A numerical study of residual stress induced in machined silicon surfaces by molecular dynamics simulation

  • Yachao Wang
  • Jing ShiEmail author
  • Chunhui Ji
Article

Abstract

Residual stresses in machined surface are regarded as a critical factor affecting the quality and service life of components. However, little research has been conducted to reveal the formation of residual stresses as well as the relation between machining conditions and residual stresses at the nanometric scale. In this study, residual stresses in machined surfaces of monocrystalline silicon are computed based on molecular dynamics simulation. An orthogonal machining configuration is adopted, and diamond cutting tools are used. The numerical approach developed is able to reveal stress evolution during and after machining, as well as in-depth residual stress distributions. The results indicate that the material stresses are stabilized within a manageable amount of computation time, and the in-depth normal stress along the tool moving direction has a more dynamical and significant pattern compared with other stress components. Meanwhile, the effects of depth of cut and tool rake angle are investigated. It is found that the increase of depth of cut results in the decrease of maximum tensile residual stress on the machined surfaces and the increase of maximum compressive residual stress underneath the surface. Similar observations are observed when the tool rake angle changes from positive to negative. It is believed that the more negative tool rake angles or the larger depths of cut induce a more drastic phase transformation to the machined surfaces, and this makes the in-depth residual stress distributions more compressive.

Keywords

Residual Stress Molecular Dynamic Simulation Machine Surface Compressive Residual Stress Thrust Force 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.North Dakota State UniversityFargoUSA

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