Aviral Shrot

Inverse Identification of Johnson-Cook Material Parameters from Machining Simulations

A material model is a prerequisite to the modelling of machining processes. Owing to its versatility, the Johnson-Cook model is commonly used for machining simulations. Determination of the model parameters from experiments is challenging due to the large variations of strains, strain-rates and temperatures which lead to several problems. State-of-the-art experimental methods have to rely on data obtained from much lower strains and strain-rates than those encountered during machining. In this paper, an inverse method of identifying Johnson-Cook parameters from machining experiments is described. A fnite-element model of the machining process was created and a particular Johnson-Cook parameter set was taken from literature for the simulation. The Levenberg-Marquardt Algorithm was used to re-identify the material parameters by looking at the Chip-morphology and the Cutting force evolution. It is shown that the optimisation parameters and error function must be chosen carefully in order to achieve better solutions at lower computational expense.

Dynamic Behavior and Numerical Modeling of Titanium 15-3-3-3 Alloy

Metastable beta titanium alloys combine excellent mechanical properties with low density, and are therefore very attractive in many mechanically demanding applications. The high strength and hardness, however, cause several challenges in the machining of these materials, and the machining costs of titanium components can be significant compared to the overall costs of the component. The cutting conditions can be optimized using finite element simulations, leading to reduced machining costs and improved machining quality. However, the simulations of the rather complex machining processes need reliable material models. The models can only be generated when the mechanical behavior of the material is well understood. In this work, the mechanical response of Ti-15-3-3-3 alloy has been characterized in a wide range of strain rates and temperatures. The Johnson-Cook material model was fit to the measurement data, and the model was used to simulate orthogonal cutting of the material. The simulation results were further compared to cutting experiments at high cutting speeds. The current model is able to simulate the serrated chip formation frequently observed in machining of titanium alloys at high cutting speeds. Also, the simulated cutting forces match well with the experimentally obtained forces. However, the model needs to be further developed to match also the fine details of the chip, such as the chip curl and thickness of the individual serrations.

How To Identify Johnson‐Cook Parameters From Machining Simulations

The Johnson‐Cook material model is a robust material model which has demonstrated its usefulness in describing material behaviour over large ranges of strains, strain rates and temperatures. During machining the material in the shear zone undergoes strains of more than 200%, strain‐rates of the order of 106 per second or more and a temperature rise of several hundreds of degrees Celsius. The determination of the Johnson‐Cook parameters, which are needed to describe the material behaviour in the severe conditions found during machining, has proved to be challenging, even using the state‐of‐the‐art experimental methods. Recent experimental methods rely on data obtained from strains of around 50% and strain rates of the order of 103 per second. In this paper, an inverse method for determining the Johnson‐Cook parameters from machining simulations is described. To demonstrate the concept, a finite element model of orthogonal cutting is created and a particular Johnson‐Cook parameter set is used for the simulation. It has been shown earlier that multiple Johnson‐Cook parameter sets exist which give rise to almost indistinguishable chips and cutting forces for a single set of cutting parameters. In order to eliminate some of these different sets, machining simulations are carried out for two different rake angles. Using the Levenberg‐Marquardt optimisation algorithm, the original Johnson‐Cook parameter set is re‐identified. In order to achieve this, the chip morphology and the cutting force are used to construct the objective function for minimisation. To determine the direction of the steepest descent, the Jacobian matrix is determined numerically with respect to the Johnson‐Cook parameters.The Johnson‐Cook material model is a robust material model which has demonstrated its usefulness in describing material behaviour over large ranges of strains, strain rates and temperatures. During machining the material in the shear zone undergoes strains of more than 200%, strain‐rates of the order of 106 per second or more and a temperature rise of several hundreds of degrees Celsius. The determination of the Johnson‐Cook parameters, which are needed to describe the material behaviour in the severe conditions found during machining, has proved to be challenging, even using the state‐of‐the‐art experimental methods. Recent experimental methods rely on data obtained from strains of around 50% and strain rates of the order of 103 per second. In this paper, an inverse method for determining the Johnson‐Cook parameters from machining simulations is described. To demonstrate the concept, a finite element model of orthogonal cutting is created and a particular Johnson‐Cook parameter set is used for the simula...

Material Parameter Identification from Machining Simulations Using Inverse Techniques

Machining is a complex process during which the material undergoes large deformations at high strain-rates with large variations in temperatures. One of the difficulties faced during the simulation of machining is that of determining appropriate material parameters which are valid for such large ranges of strains, strain-rates and temperatures. An inverse method of material parameter identification from machining simulations is proposed in this paper. An error function is defined that takes into account the chip overlap error and the cutting force difference at different frames of observation. The two components are suitably weighted so that the contribution of each is rendered almost equally. A two stage optimisation process is employed for the minimisation of the error function where the Levenberg-Marquardt algorithm is used in the first stage for faster convergence and the Downhill Simplex algorithm in the second stage in order to navigate through the noisy error landscape. A wide range of cutting conditions is used and the method is shown to work also for non-adiabatic simulations. However, the converged parameter sets are found to be non-unique.