Strain rate effects on the mechanical properties of a Fe–Mn–Al alloy under dynamic impact deformations

Chiou, Su-Tang; Cheng, Wei-Chun; Lee, Woei-Shyan

doi:10.1016/j.msea.2004.09.055

Cited by 68 publications

(26 citation statements)

References 10 publications

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“…Alternatively, there is a way to relate the strain rate sensitivity with the thermal activation volume of the material, as previously reported by [35]. Therefore, the derivation of the thermal activation volume, taking into account the strain rate sensitivity, can be expressed as follows [11,35]:…”

Section: Strain Rate Sensitivity and Thermal Activation Volumementioning

confidence: 99%

Particle size – Dependent on the static and dynamic compression properties of polypropylene/silica composites

2013

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Section: Strain Rate Sensitivity and Thermal Activation Volumementioning

confidence: 99%

Particle size – Dependent on the static and dynamic compression properties of polypropylene/silica composites

2013

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“…Moreover, neural network models were used to predict the flow behaviors of 28CrMnMoV steel [19], glass fiber reinforced polymers [20], A356 aluminum alloy [21], and 42CrMo steel [22]. In addition, dynamic recrystallization (DRX) model [23], Zerilli-Amstrong (ZA) model [24,25], mechanical threshold stress plasticity (MTS) model [26], Cellular Automata (CA) model [27], and Bonder-Partom (BP) model [28] belong to the physics-based models. The study of V-4Cr-4Ti [29], medium carbon and vanadium microalloyed steels [30], and Mg-Al-Zn alloy [31] shows that the constitutive equation based on microscopic mechanism has good applicability, which can be used to characterize the relationship between flow stress and microstructure during high-temperature rheological process.…”

Section: Introductionmentioning

confidence: 99%

A Modified Johnson-Cook Model for Hot Deformation Behavior of 35CrMo Steel

Wang

Huang

Xiao

et al. 2017

Metals

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Abstract:In this work, a compression experiment of 35CrMo steel is carried out over a wide range of temperatures (1123-1423 K) and strain rates (0.1-10 s −1 ) to obtain further understandings of the flow behaviors. The results show that the strain hardening effect of 35CrMo steel is stronger than that of dynamic recrystallization at low temperature and high strain rate; on the contrary, the rheological curves show typical dynamic recrystallization characteristics at high temperature and low strain rate. This indicates that the strain hardening and recrystallization behavior of 35CrMo steel is affected by temperature, strain and strain rate, and its true stress-strain curves can be observed typical work hardening and dynamic softening features. A modified Johnson-Cook (JC) model is developed to predict the flow stress of the alloy. The results of the comparison show that the predicted values of the modified JC model are in good agreement with the experimental values.

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“…Some researchers prefer the ZA model to the JC model as the former not only incorporates the coupled effects of strain rate and temperature but also considers dislocation characteristics for particular structures. Even though parameters for the ZA model for the Ti6Al4V alloy and different steels [16][17][18][19][20][21][22][23] have been proposed in recent years, some materials constants for the ZA model are very difficult to validate as they require a stress at 0 K and the athermal stress of the materials. Also, it is not suggested to use the ZA model at temperatures above a half of the melting temperature of materials [21].…”

Section: Introductionmentioning

confidence: 99%

Constitutive Equations to Predict Stress‐Strain Behaviour for a β Titanium Alloy at High Strain Rates Over a Wide Range of Temperatures

Zhan

Gui

Kent

et al. 2016

Proceedings of the 13th World Conference on Titanium

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Stress-strain curves of the Ti-6Cr-5Mo-5V-4Al (Ti6554) alloy obtained from Split Hopkinson Pressure Bar (SHPB) tests over a wide range of temperatures (293-1173K) and strain rates (10 3 -10 4 s -1 ) were employed to fit parameters for the Johnson-Cook(JC) and the Zerilli-Armstrong(ZA) models, respectively. The JC model is able to capture the stress-strain behaviour of the Ti6554 alloy better than the ZA model. However, the limit of the JC model is that it can only predict the flow behaviour accurately in a narrow domain near the strain value which is fixed in the parameters fitting. Also the format of the JC model is inadequate to track the complex strain-hardening behaviour of the Ti6554 alloy.

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Strain rate effects on the mechanical properties of a Fe–Mn–Al alloy under dynamic impact deformations

Cited by 68 publications

References 10 publications

Particle size – Dependent on the static and dynamic compression properties of polypropylene/silica composites

Particle size – Dependent on the static and dynamic compression properties of polypropylene/silica composites

A Modified Johnson-Cook Model for Hot Deformation Behavior of 35CrMo Steel

Constitutive Equations to Predict Stress‐Strain Behaviour for a β Titanium Alloy at High Strain Rates Over a Wide Range of Temperatures

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