Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests

Abstract

Accuracy of numerical models based in finite elements (FE), extensively used for simulation of cutting processes, depends strongly on the identification of proper material parameters. Experimental identification of the constitutive law parameters for simulation of cutting processes involves unsolved problems such as the complex testing techniques or the difficulty to reproduce the stress triaxiality state during cutting. This work proposes a methodology for the inverse identification of the material parameters from cutting test. Two hybrid approaches are compared. One of them based on FE and artificial neural networks (ANN). The other one based on FE and local polynomial regression (LPR). Firstly, a FE model is validated with experimental data. Then, ANN and LPR are trained with FE simulations. Finally, the estimated ANN and LPR models are used for the inverse identification of material parameters. This identification is solved as an optimization problem. The FE/LPR approach shows good performance, outperforming the FE/ANN approach.

Analysis of adiabatic shear banding in orthogonal cutting of Ti alloy

“…The Johnson-Cook's constitutive equation (with three different sets of material constants) was implemented in a numerical machining model and the results were compared with experimental data, establishing that fairly good numerical results could be obtained. Naturally, the goodness of results is only insured for cutting conditions for which the loading rates and stresses undergone by the work-material are in the range explored during the experimental characterization of the work-material constitutive law as shown for instance by Muñoz-Sanchez et al [10].…”

Modeling of the Feed-Motor Transient Current in End Milling by Using Varying-Coefficient Model

“…Valentinčič and Junkar [20] applied the nonparametric method to the electrical discharge machining process (EDM) and obtained a valuable result. Munoz-Sánchez et al [21] studied the inverse identification of material parameters using hybrid FEM/LPR method and FEM/ANN. Local polynomial regression (LPR) is also a kind of nonparametric regression.…”

The use of direct inverse maps to solve material identification problems: pitfalls and solutions

“…Notice that e includes both the stochastic noise and the model error. We refer to this approach as the (conventional) forward inverse method which is common practice for identifying material parameters (Aguir et al 2011 ;Aguir et al 2008 ;Asaadi and Heyns 2016 ;Bolzon and Talassi 2013 ;Brigham and Aquino 2007 ;Degroote et al 2012 ;Harb et al 2014 ;Munoz-Sánchez et al 2011 ;Song and Hashash 2015 ;Zhang et al 2014 ;Zhang et al 2013).…”

“…The sum of squared errors can then be computed from the surrogate responses. Popular strategies include artificial neural networks (Aguir et al 2011 ;Aguir et al 2008 ;Asaadi and Heyns 2016 ;Brigham and Aquino 2007 ;Hambli and Guerin 2003 ;Zhang et al 2013) or radial basis functions (RBF) (Bolzon and Talassi 2013 ;Munoz-Sánchez et al 2011). Fig.…”