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Analytical temperature predictive modeling and non-linear optimization in machining

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Abstract

Different process parameters can alter the temperature during machining. Consequently, selecting process parameters that lead to a desirable cutting temperature would help to increase the tool life, decrease the tensile residual stress, and controls the microstructure evolution of the workpiece. An inverse computational methodology is proposed to design the process parameters for a specific cutting temperature. A physics-based analytical model is used to predict the temperature induced by cutting forces. The shear deformation and chip formation model is implemented to calculate machining forces as functions of process parameters, material properties, and etc. To calculate the temperature induced by the deformation in the shear zone, a moving point heat source approach is used. The proposed model uses an iterative non-linear regression to predict the cutting process parameters based on the desirable temperature which is assigned by the user. In order to achieve the cutting process parameters, an iterative gradient search is used to adaptively approach the specific temperature by the optimization of process parameters such that an inverse reasoning can be achieved. Experimental data are used to illustrate the implementation and validate the viability of the computational methodology.

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Authors and Affiliations

  1. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Elham Mirkoohi & Steven Y. Liang

  2. Carpenter Technology Corporation, Huntsville, AL, 35671, USA

    Peter Bocchini

Authors
  1. Elham Mirkoohi
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  2. Peter Bocchini
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  3. Steven Y. Liang
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Contributions

E.M. conceived and developed the proposed analytical model, extracted and analyzed the data, and wrote the paper. P. B provided general guidance. S.Y.L. provided general guidance and proofread the manuscript writing.

Corresponding author

Correspondence to Elham Mirkoohi.

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The authors declare that they have no conflict of interest.

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Mirkoohi, E., Bocchini, P. & Liang, S.Y. Analytical temperature predictive modeling and non-linear optimization in machining. Int J Adv Manuf Technol 102, 1557–1566 (2019). https://doi.org/10.1007/s00170-019-03296-y

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  • DOI: https://doi.org/10.1007/s00170-019-03296-y

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