Journal of Materials Research and Technology (Nov 2023)

FE modeling and simulation of the turning process considering the cutting induced hardening of workpiece materials

  • Jinxin Li,
  • Feng Jiang,
  • Ansheng Jin,
  • Tao Zhang,
  • Xigui Wang,
  • Shizhan Huang,
  • Xiangshen Zeng,
  • Hongfei Yao,
  • Dongwei Zhu,
  • Xian Wu,
  • Lan Yan,
  • Xuming Zha,
  • Bicheng Guo

Journal volume & issue
Vol. 27
pp. 4986 – 4996

Abstract

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The accuracy of the cutting simulation model greatly depends on the constitutive models, thermophysical models, and friction models. However, accurate modeling of physical and mechanical relationships is not enough. The physical and mechanical behavior of the machined surface from the last cut should be modelled in the FE model. In this study, the cutting simulation model of S316L stainless steel was established. The above model consists of two subsequent simulated cuts. The first simulated cut was used to obtain the machined surface with the residual stress, and the second simulated cut was subsequent with the first cut to obtain the actual simulated results. The constitutive model was obtained by the split Hopkinson pressure bar (SHPB) and high-temperature hardness experiments. The specific heat capacity and thermal conductivity models were developed by laser thermal conductivity experiments with various temperatures. The friction model between the workpiece and the tool was established by orthogonal cutting experiments. The simulated cutting forces of the first and second cut were extracted and compared with the experimental results to verify the accuracy of the simulation models. The results showed that the average error of cutting forces for the first cut is 28.33 %, but that for the second cut is 8.02 %, which verifies the accuracy of the two-subsequent cutting simulation model. Additionally, the significant differences in the simulated cutting forces between the first and second cutting depict that the residual stress cannot be ignored for the accuracy verification of cutting simulation models.

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