A novel approach for determining material constitutive parameters for a wide range of triaxiality under plane strain loading conditions

Abushawashi, Yalla; Xiao, Xinran; Astakhov, Viktor P.

doi:10.1016/j.ijmecsci.2013.05.007

Cited by 42 publications

(30 citation statements)

References 12 publications

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“…In ductile materials, the damage initiation model should be established based on the material ductility, thus the equivalent strain at fracture. The latter is sensitive to the state of stress, being the stress triaxiality and the Lode angle two parameters that affect this strain [20,21]. In the case of orthogonal cutting (planestrain condition), the equivalent strain at facture is only affected by the stress triaxiality [14].…”

Section: Lagrangian Formulation (Lag)mentioning

confidence: 99%

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On the Selection of Johnson-cook Constitutive Model Parameters for Ti-6Al-4V Using Three Types of Numerical Models of Orthogonal Cutting

2015

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Johnson-Cook constitutive model is still the most used model in metal cutting simulation, although several drawbacks reported in the literature. A high number of Johnson-Cook model parameters can be found in the literature for the same work material. One question that may arise is "What is the most suitable set of Johnson-Cook model parameters for a given material?". The present paper puts in evidence some issues related with the selection of these parameters from the literature. In this contribution, two sets of Johnson-Cook model parameters for Ti-6A-4V are evaluated, using three types of metal cutting models. These models are based on three different formulations: Lagrangian, Arbitrary Eulerian-Lagrangian (ALE) and Couple Lagrangian-Eulerian (CEL). This evaluation is based on the comparison between measured and predicted chip geometry, chip compression ratio, forces, plastic deformation and temperature distributions.

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Section: Lagrangian Formulation (Lag)mentioning

confidence: 99%

“…Modelling of metal cutting process without a proper material constitutive model including fracture induces unrealistic material behaviour. Consequently, chip morphology (e.g., segmentation) obtained by such incomplete models produces an unrealistic tooth chip with unlimited material stretching and hardening [20].…”

Section: Lagrangian Formulation (Lag)mentioning

confidence: 99%

On the Selection of Johnson-cook Constitutive Model Parameters for Ti-6Al-4V Using Three Types of Numerical Models of Orthogonal Cutting

2015

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“…Considering the energy flows occurred in a cutting tool, workpiece, and chip as well as their interactions during machining, P cutting can be partitioned into the power spent on: (1) plastic deformation of the layer being removed (P pd ); (2) tool-chip interface (P tc ); (3) tool-workpiece (P tw ) interface; and (4) formation of new surfaces (P fs ), as expressed in Equation (2) [11,28]. Here, it is obvious that the selection of a workpiece and a cutting tool with its subordinate insert influences P cutting because the interactions between them impose P tw and P tc in Equation (2).…”

Section: Identification Of Process Feature Datamentioning

confidence: 99%

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

et al. 2018

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Abstract:In the metal cutting industry, power consumption is an important metric in the analysis of energy efficiency since it relates to energy consumption of machine tools. Much of the research has developed predictive models that correlate process planning decisions with power consumption through theoretical and/or experimental modeling approaches. These models are created by using the theory of metal cutting mechanics and Design of Experiments. However, these models may lose their ability to predict results correctly outside the required assumptions and limited experimental conditions. Thus, they cannot accurately reflect a diversity of machining configurations; i.e., selections of machine tool, workpiece, cutting tool, coolant option, and machining operation for producing a part, which a machining shop has operated. This paper proposes a predictive modeling approach based on historical data collected from machine tool operations. The proposed approach can create multiple predictive models for power consumption, which can be applicable to the diverse machining configurations. It can create fine-grained models predictable up to the level of a numerical control program. It uses standard-based data interfaces such as STEP-NC and MTConnect to implement interoperable and comprehensive data representations. This paper also presents a case study to demonstrate the feasibility and effectiveness of the proposed approach.

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“…In ductile materials, the damage initiation model should be established based on the material ductility, thus the equivalent strain at fracture. The equivalent strain at fracture is sensitive to the state of stress, being the stress triaxility and the Lode angle two parameters that affect this strain (Abushawashi et al, 2013;Bai and Wierzbicki, 2008). In the case of plane-strain condition, which is the case for orthogonal cutting, the equivalent strain at facture is only affected by the stress triaxility (Abushawashi et al, 2011).…”

Section: Without the Calibration Proceduresmentioning

confidence: 99%

“…To conclude, modeling chip formation without a proper material model, that includes damage and fracture, results in unrealistic material behavior, where the material flow is somewhat unlimited with no material stiffness degradation. Moreover, the chip morphology ( e.g., segmentation) obtained by such incomplete models produces an unrealistic smooth chip with unlimited material stretching and hardening (Abushawashi et al, 2013).…”

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confidence: 99%

Evaluation of Present Numerical Models for Predicting Metal Cutting Performance And Residual Stresses

Outeiro

Umbrello

M’Saoubi³

et al. 2015

Machining Science and Technology

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Efforts on numerical modelling and simulation of metal cutting operations continue to increase due to the growing need for predicting the machining performance. A significant number of numerical methods, especially the Finite Element (FE) and the Mesh-free methods, are being developed and used to simulate the machining operations. However, the effectiveness of the numerical models to predict the machining performance depends on how accurate are them to represent the actual metal cutting process, and also on the quality and accuracy of the input data used in such models. This paper presents results from a recently conducted benchmark study, which involved evaluation of various numerical predictive models. This study had a major objective to evaluate the effectiveness of the current numerical predictive models for machining performance, focusing on surface integrity. Five representative work materials were carefully selected for this study from a range of most commonly used work materials, along with a wide range of cutting conditions usually found in the published literature. The differences between the predicted results obtained from several numerical models using different FE and Mesh-free codes are evaluated and compared with those obtained experimentally.

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A novel approach for determining material constitutive parameters for a wide range of triaxiality under plane strain loading conditions

Cited by 42 publications

References 12 publications

On the Selection of Johnson-cook Constitutive Model Parameters for Ti-6Al-4V Using Three Types of Numerical Models of Orthogonal Cutting

On the Selection of Johnson-cook Constitutive Model Parameters for Ti-6Al-4V Using Three Types of Numerical Models of Orthogonal Cutting

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

Evaluation of Present Numerical Models for Predicting Metal Cutting Performance And Residual Stresses

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