Constitutive model for metals with dynamic strain aging

Voyiadjis, George Z.; Song, Yooseob; Rusinek, Alexis

doi:10.1016/j.mechmat.2018.12.012

Cited by 42 publications

(28 citation statements)

References 30 publications

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“…In this study, a new modified JC model is developed based on the Voyiadjis-Abed-Rusinek [8] constitutive modeling approach. The modified JC model with the fracture modeling approach by Childs et al [26] has been implemented for the first time in the FE framework for the chip formation simulation.…”

Section: Discussionmentioning

confidence: 99%

“…A positive stress triaxiality condition, e.g., η = 1 characterizing tension, the flow stress is drastically reduced to zero, similar to necking in tensile testing. At temperatures above T E , the material is assumed to be healed and continue to flow plastically with the flow stress defined by σ, as shown in Equation (8). Between temperatures T S and T E , the flow stress curve is defined using the tanh function, as shown in Equation (11).…”

Section: Flow Stress Modification Factormentioning

confidence: 99%

“…The initial yield stress parameter A is modified similarly to the VAR model to incorporate the DSA influence, leading to yield stress increase with temperature increase at specific temperature ranges [8], as in Equations (3)-(6) and shown in Figure 1. Reported experimental investigations have shown that the dynamic strain aging temperature range is a function of strain rate [6,7,14].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, there are deformation mechanisms that are material specific, such as dynamic strain aging (DSA). Previous studies carried out in understanding the behavior of ferritic-pearlitic steel have shown that DSA is a function of temperature and strain rate [6][7][8][9]. Earlier studies have been uncertain about the need for the incorporation of DSA in machining process modeling [10,11].…”

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confidence: 99%

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Predicting Continuous Chip to Segmented Chip Transition in Orthogonal Cutting of C45E Steel through Damage Modeling

Devotta

Sivaprasad

Beňo

et al. 2020

Metals

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Machining process modeling has been an active endeavor for more than a century and it has been reported to be able to predict industrially relevant process outcomes. Recent advances in the fundamental understanding of material behavior and material modeling aids in improving the sustainability of industrial machining process. In this work, the flow stress behavior of C45E steel is modeled by modifying the well-known Johnson-Cook model that incorporates the dynamic strain aging (DSA) influence. The modification is based on the Voyiadjis-Abed-Rusinek (VAR) material model approach. The modified JC model provides the possibility for the first time to include DSA influence in chip formation simulations. The transition from continuous to segmented chip for varying rake angle and feed at constant cutting velocity is predicted while using the ductile damage modeling approach with two different fracture initiation strain models (Autenrieth fracture initiation strain model and Karp fracture initiation strain model). The result shows that chip segmentation intensity and frequency is sensitive to fracture initiation strain models. The Autenrieth fracture initiation strain model can predict the transition from continuous to segmented chip qualitatively. The study shows the transition from continuous chip to segmented chip for varying feed rates and rake angles for the first time. The study highlights the need for material testing at strain, strain rate, and temperature prevalent in the machining process for the development of flow stress and fracture models.

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Section: Discussionmentioning

confidence: 99%

Section: Flow Stress Modification Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Predicting Continuous Chip to Segmented Chip Transition in Orthogonal Cutting of C45E Steel through Damage Modeling

Devotta

Sivaprasad

Beňo

et al. 2020

Metals

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“…The proposed models include all three elements, i.e., σ PM I, II = σ ath + σ th + σ D . PM I was originally proposed by [16] and PM II is newly proposed in this work. The VA model and PM I are expected to present some limitations to capture the DSA effect accurately.…”

Section: Introductionmentioning

confidence: 99%

Constitutive Models for Dynamic Strain Aging in Metals: Strain Rate and Temperature Dependences on the Flow Stress

2020

Self Cite

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A new constitutive model for Q235B structural steel is proposed, incorporating the effect of dynamic strain aging. Dynamic strain aging hugely affects the microstructural behavior of metallic compounds, in turn leading to significant alterations in their macroscopic mechanical response. Therefore, a constitutive model must incorporate the effect of dynamic strain aging to accurately predict thermo-mechanical deformation processes. The proposed model assumes the overall response of the material as a combination of three contributions: athermal, thermally activated, and dynamic strain aging stress components. The dynamic strain aging is approached by two alternative mathematical expressions: (i) model I: rate-independent model; (ii) model II: rate-dependent model. The proposed model is finally used to study the mechanical response of Q235B steel for a wide range of loading conditions, from quasi-static loading ( ε ˙ = 0.001 s − 1 and ε ˙ = 0.02 s − 1 ) to dynamic loading ( ε ˙ = 800 s − 1 and ε ˙ = 7000 s − 1 ), and across a broad range of temperatures ( 93 K − 1173 K ). The results from this work highlight the importance of considering strain-rate dependences (model II) to provide reliable predictions under dynamic loading scenarios. In this regard, rate-independent approaches (model I) are rather limited to quasi-static loading.

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An Enhanced Johnson–Cook Model for Hot Compressed A356 Aluminum Alloy

Chen

Lin

et al. 2020

Adv Eng Mater

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The isothermal compression experiments with the strain rates of 0.01–10 s−1 and deformation temperature range of 300–420 °C are performed to investigate the hot deformation behavior of A356 aluminum alloy. Also, the complex deformation mechanisms are analyzed. It is found that, as the strain is gradually increased, the flow stress first rises, and then the stable stress appears without a tangible peak. The microstructures exhibit large elongated grains, and only a few small new grains appear under most deformation conditions. It is because the dynamic recovery (DRV) is the dominant softening mechanism. Based on the measured data, both the original Johnson–Cook (O–JC) model and modified Johnson–Cook model (M–JC) are built for the tested aluminum alloy. However, there are different deviations between the experimental and the predicted true stresses by O–JC model and M–JC model. Considering the obvious DRV features, an enhanced Johnson–Cook (EH–JC) model is proposed by introducing the stress–dislocation relation. The accuracy of the EH–JC model is validated because the correlation coefficient between the experimental and predicted results is as high as 0.997, whereas the average absolute relative error is merely 2.84%.

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Constitutive model for metals with dynamic strain aging

Cited by 42 publications

References 30 publications

Predicting Continuous Chip to Segmented Chip Transition in Orthogonal Cutting of C45E Steel through Damage Modeling

Predicting Continuous Chip to Segmented Chip Transition in Orthogonal Cutting of C45E Steel through Damage Modeling

Constitutive Models for Dynamic Strain Aging in Metals: Strain Rate and Temperature Dependences on the Flow Stress

An Enhanced Johnson–Cook Model for Hot Compressed A356 Aluminum Alloy

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