Identification of plasticity constants from orthogonal cutting and inverse analysis

Mathias Agmell, Aylin Ahadi, Jan-Eric Ståhl

Research output: Contribution to journalArticlepeer-review

40 Citations (SciVal)


The aim of this work is that from experimental determined cutting process parameters be able to predict the plasticity input constants to Finite Element Method (FEM) models. In the present study the Johnson-Cook constitutive model constants are determined on the basis of cutting process parameters in orthogonal cutting and by use of inverse analysis. Previously established links between Johnson-Cook constitutive model constants and cutting process parameters in the cutting process such as primary cutting force and chip compression ratio is used serve as a starting point in the inverse analysis. As a reference material AISI 4140 has been chosen in this study, which is a tempered steel. The Johnson-Cook constitutive model constants in the reference material are being changed within an interval of ±30 %. The inverse analysis is performed using a Kalman filter. The material model for the reference material is validated on the basis of the experimental results in previous work. The model showed to predict the cutting process parameters with a high level of accuracy. The predicted Johnson-Cook constitutive model constants in the present study achieve an error between simulated- and experimental cutting process parameters of maximum 2%. The method described in this study is not limited to identify Johnson-Cook constitutive model constants, but the method can also be used for other constitutive models. The same applies to the process itself and the selected cutting process parameters, but orthogonal cutting has been used to illustrate and validate this method.
Original languageEnglish
Pages (from-to)43-51
JournalMechanics of Materials
Publication statusPublished - 2014

Subject classification (UKÄ)

  • Applied Mechanics
  • Materials Engineering


  • Orthogonal cutting
  • Johnson Cook
  • Inverse analysis
  • Kalman filter


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