Dislocation Density Based Flow Stress Model Applied to the PFEM Simulation of Orthogonal Cutting Processes of Ti-6Al-4V

Rodríguez, Juan Manuel Corchado; Larsson, Simon; Carbonell, Josep Maria; Jonsén, Pär

doi:10.3390/ma13081979

Cited by 10 publications

(6 citation statements)

References 78 publications

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“…The reason for this is likely due to the constitutive model (Johnson-Cook) and the Coulomb friction law in the numerical simulations. The quality of the numerical predictions is expected to improve under consideration of more physical material models, which for example include effects of the dislocation density and globularization, see [34,35], or models which incorporate the effect of phase transformations towards higher temperatures [36]. On the other hand, more sophisticated friction models, e.g.…”

Section: Discussionmentioning

confidence: 99%

Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations

Klippel¹,

Sanchez²,

Isabel³

et al. 2022

Journal of Machine Engineering

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The prediction of machining processes is a challenging task and usually requires a large experimental basis. These experiments are time-consuming and require manufacturing and testing of different tool geometries at various process conditions to find optimum machining settings. In this paper, a machine learning model of the orthogonal cutting process of Ti6Al4V is proposed to predict the cutting and feed forces for a wide range of process conditions with regards to rake angle, clearance angle, cutting edge radius, feed and cutting speed. The model uses training data generated by virtual experiments, which are conducted using physical based simulations of the orthogonal cutting process with the smoothed particle hydrodynamics (SPH). The ML training set is composed of input parameters, and output process forces from 2500 instances of GPU accelerated SPH simulations. The resulting model provides fast process force predictions and can consider the cutter geometry in comparison to classical analytical approaches.

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Section: Discussionmentioning

confidence: 99%

Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations

Klippel¹,

Sanchez²,

Isabel³

et al. 2022

Journal of Machine Engineering

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“…In addition, the constitutive model used can be improved with models based on physics, for example, models based on dislocations [70][71][72]. These models have been successfully applied to the numerical simulation of orthogonal cutting [70][71][72], showing a better prediction of cutting and feed forces.…”

Section: Discussionmentioning

confidence: 99%

Modeling Grinding Processes—Mesh or Mesh-Free Methods, 2D or 3D Approach?

Sridhar

Prieto

Payrebrune

2022

JMMP

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The objectives of this study are mainly two: (1) to validate whether a single grain scratch process can be modeled in two dimensions under the assumption of plane strain, and (2) to select the best discretization approach to model a single grain scratch process. This paper first focuses on the simulation of the orthogonal cutting process (aluminum alloy A2024 T351) using two mesh-based discretization approaches, the pure Lagrangian method (LAG) and the arbitrary Lagrangian–Eulerian method (ALE), and two particle-based approaches, the particle finite element method (PFEM) and smooth particle hydrodynamics (SPH), for both positive and negative rake angles. Benchmarking of the orthogonal cutting models at a rake angle of γ=20∘ is performed with the results of the process forces (cutting and passive forces) of a turning experiment from the literature. It is shown that all models are able to predict the cutting forces, but not the passive force. The orthogonal cutting model is further extended to simulate the cutting mechanism with negative rake tool geometries typically found in grinding and single grit scratching processes. The effects of the negative rake angles on the discretization approaches are studied. The calculated process forces are also compared to the measurements of the single grit scratch process performed at our laboratory. The 2D orthogonal cutting models significantly overestimate the process forces. One of the reasons why the orthogonal 2D cutting model is inadequate is that it cannot describe the complex mechanisms of material removal such as rubbing, plowing, and cutting. To account for these phenomena in LAG, ALE, and SPH discretization approaches, a 3D scratch model is developed. When comparing the process forces of the 3D model with the experimental measurements, all three discretization approaches show good agreement. However, it can be seen that the ALE model most closely matches the process forces with the experimental results. Finally, the ALE 3D scratch model was subjected to sensitivity analysis by changing the cutting speed, the depth of cut and the tool geometry. The results clearly show that the ALE method not only predicts the process forces and form the trends observed in the scratching experiments, but also predicts the scratch topography satisfactorily. Hence, we conclude that a 3D model is necessary to describe a scratch process and that the ALE method is the best discretization method.

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“…The numerical models was solved using a in-house Matlab PFEM code developed by the authors. Preliminary version of the PFEM software have been reported in [ 48 , 50 , 55 ].…”

Section: Numerical Simulation Frameworkmentioning

confidence: 99%

Thermomechanical Simulation of Orthogonal Metal Cutting with PFEM and SPH Using a Temperature-Dependent Friction Coefficient: A Comparative Study

2023

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In this work, we apply the Particle Finite Element Method (PFEM) and Smoothed Particle Hydrodynamics (SPH) to simulate the orthogonal cutting chip formation of two workpiece materials, i.e., AISI 1045 steel and Ti6Al4V titanium alloy. A modified Johnson–Cook constitutive model is used to model the plastic behavior of the two workpiece materials. No damage or strain softening is included in the model. The friction between the workpiece and the tool is modeled following Coulomb’s law with a temperature-dependent coefficient. The accuracy of PFEM and SPH in predicting thermomechanical loads at various cutting speeds and depths against the experimental data are compared. The results show that both numerical methods can predict the rake face temperature of AISI 1045 with errors less than 34%. For Ti6Al4V, however, the temperature prediction errors are significantly higher than those of the steel alloy. Errors in force prediction were in the range of 10% to 76% for both methods, which compare very well with those reported in the literature. This investigation infers that the Ti6Al4V behavior under machining conditions is difficult to model on the cutting scale irrespective of the choice of numerical method.

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Dislocation Density Based Flow Stress Model Applied to the PFEM Simulation of Orthogonal Cutting Processes of Ti-6Al-4V

Cited by 10 publications

References 78 publications

Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations

Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations

Modeling Grinding Processes—Mesh or Mesh-Free Methods, 2D or 3D Approach?

Thermomechanical Simulation of Orthogonal Metal Cutting with PFEM and SPH Using a Temperature-Dependent Friction Coefficient: A Comparative Study

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