Preprint Article Version 1 This version is not peer-reviewed

An Analytical Modeling for Designing the Process Parameters for Temperature Specifications in Machining

Version 1 : Received: 26 July 2018 / Approved: 27 July 2018 / Online: 27 July 2018 (03:44:21 CEST)

How to cite: Mirkoohi, E.; Bocchini, P.; Liang, S.Y. An Analytical Modeling for Designing the Process Parameters for Temperature Specifications in Machining. Preprints 2018, 2018070528 (doi: 10.20944/preprints201807.0528.v1). Mirkoohi, E.; Bocchini, P.; Liang, S.Y. An Analytical Modeling for Designing the Process Parameters for Temperature Specifications in Machining. Preprints 2018, 2018070528 (doi: 10.20944/preprints201807.0528.v1).

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 specific cutting temperature. A physics-based analytical model is used to predict the temperature induced by cutting forces. To calculate the temperature induced by the deformation in the shear zone, a moving point heat source approach is used. The shear deformation and chip formation model is implemented to calculate machining forces as functions of process parameters, material properties, and etc. The proposed model uses the analytical model to predict the cutting temperatures and applies a variance-based recursive method to guide the inverse analysis. 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.

Subject Areas

inverse analysis; temperature prediction; process parameters; cutting speed; depth of cut

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