Title: A combined experimental and numerical approach that eliminates the non-uniqueness associated with the Johnson-Cook parameters obtained using inverse methods

Abstract

Abstract Johnson-Cook constitutive model is a commonly used material model for machining simulations. The model includes five parameters that capture the initial yield stress, strain-hardening, strain-rate hardening, and thermal softening behavior of the material. These parameters are difficult to determine using experiments since the conditions observed during machining (such as high strain-rates of the order of $$10^5$$ ${10}^5$ /sec - $$10^6$$ ${10}^6$ /sec) are challenging to recreate in the laboratory. To address this problem, several researchers have recently proposed inverse approaches where a combination of experiments and analytical models are used to predict the Johnson-Cook parameters. The errors between the measured cutting forces, chip thicknesses and temperatures and those predicted by analytical models are minimized and the parameters are determined. In this work, it is shown that only two of the five Johnson-Cook parameters can be determined uniquely using inverse approaches. Two different algorithms, namely, Adaptive Memory Programming for Global Optimization (AMPGO) and Particle Swarm Optimization (PSO), are used for this purpose. The extended Oxley’s model is used as the analytical tool for optimization. For determining a parameter’s value, a large range for each parameter is provided as an input to the algorithms. The algorithms converge to several different sets of values for the five Johnson-Cook parameters when all the five parameters are considered as unknown in the optimization algorithm. All of these sets, however, yield the same chip shape and cutting forces in FEM simulations. Further analyses show that only the strain-rate and thermal softening parameters can be determined uniquely and the three parameters present in the strain-hardening term of the Johnson-Cook model cannot be determined uniquely using the inverse method. A combined experimental and numerical approach is proposed to eliminate this determine all parameters uniquely.

Authors:

Ojal, Nishant; Cherukuri, Harish P.; Schmitz, Tony L.; Devlugt, Kyle T.; Jaycox, Adam W.

Publication Date:

Tue Feb 22 00:00:00 EST 2022

Research Org.:

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Org.:

USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:

1846044

Alternate Identifier(s):

OSTI ID: 1873242

Report Number(s):

LLNL-JRNL-817236
Journal ID: ISSN 0268-3768; PII: 8640

Grant/Contract Number:  

AC52-07NA27344

Resource Type:

Published Article

Journal Name:

International Journal of Advanced Manufacturing Technology

Additional Journal Information:

Journal Name: International Journal of Advanced Manufacturing Technology Journal Volume: 120 Journal Issue: 3-4; Journal ID: ISSN 0268-3768

Publisher:

Springer Science + Business Media

Country of Publication:

United Kingdom

Language:

English

Subject:

42 ENGINEERING; Johnson-Cook constitutive mode; extended oxley model; adaptive memory programming for global optimization; particle swarm optimization; orthogonal machining; finite element analysis