Inverse identification of Johnson-Cook material constants based on modified chip formation model and iterative gradient search using temperature and force measurements
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This paper presents an improved inverse identification method for Johnson-Cook model constants (J-C constants) using force and temperature data. Nowadays, J-C constants are identified by either experimental approaches with the complex and costly system, numerical approaches with high computational cost, or analytical approaches with available material properties. The previous model is developed based on a modified chip formation model and an exhaustive search method using temperature and force measurements. The current model is improved by replacing the exhaustive search method with an iterative gradient search method based on the Kalman filter algorithm. The modified chip formation model is used to predict machining forces. The iterative gradient search method is used to determine the J-C constants when the difference between predicted forces and experimental forces reached an acceptable low value. AISI 1045 steel and Al6082-T6 aluminum are chosen to test the proposed methodology. The determined J-C constants are validated by comparing to the documented values in the literature, which were obtained from Split-Hopkinson Pressure Bar tests and validated in published works. Good agreements are observed between identified J-C constants and documented values with an improved computational efficiency. The cutting temperatures are used as inputs in the modified chip formation model. Therefore, the workpiece material properties are not required to predict temperatures and forces, and thus are not required for determining J-C constants. Considering the modified chip formation model using temperatures as inputs, and the effective iterative gradient search method, this method has advantages of less mathematical complexity and high computational efficiency.
KeywordsInverse identification Johnson-Cook model constants Modified chip formation model Iterative gradient search Kalman filter
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This work was supported by the US National Science Foundation under Grant CMMI-1404827.
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