Additive manufacturing – A review of hot deformation behavior and constitutive modeling of flow stress

Motallebi, Reza; Savaedi, Zeinab; Mirzadeh, Hamed

doi:10.1016/j.cossms.2022.100992

Cited by 113 publications

(23 citation statements)

References 199 publications

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“…This indicates that underlying deformation mechanism may be glide and climb of dislocations. [26][27][28][29] The predicted activation energy (Q) ranges between 201 and 229 kJ/mol. This is higher than the self-diffusion activation energy of magnesium (135 kJ/mol).…”

Section: (A) and (B)mentioning

confidence: 99%

Analysis of hot tensile behaviour and prediction of thermal stresses during processing of AZ31 magnesium alloy

Divya

Penumakala

Kalyan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The use of magnesium alloys is rapidly increasing in automotive and aerospace industries. Accurate constitutive modelling of material at high temperature is a key input for numerical simulation of manufacturing processes. In the present work, hot tensile deformation behaviour of AZ31 alloy was studied by performing uniaxial tensile tests in the temperature range of 250°C–450°C and strain rates ranging from 0.1 to 0.001 s−1. The flow stress behaviour of AZ31 alloy was modelled using two rate-dependent inelastic models, viz. Garafalo law and extended Ludwik law. Various constants in these models were predicted as a function of strain rate and temperature. These rate dependent laws were integrated using Finite Element (FE) based algorithms for thermo-mechanical simulation. Evolution of thermal stresses in solid state quenching and static casting of AZ31 alloys were studied. Results reveal that both material models accurately predict the evolution of thermal stresses for high temperature quenching and casting processes.

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Section: (A) and (B)mentioning

confidence: 99%

Analysis of hot tensile behaviour and prediction of thermal stresses during processing of AZ31 magnesium alloy

Divya

Penumakala

Kalyan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…The high complexity of the non-linear behavior of flow stress at elevated temperatures and different strain rates for some alloys causes the JC model to fail to reach precise predictions from time to time [ 61 , 62 , 63 ]. The inaccurate predictions may be due to the fact that the JC model implements the three effects of hardening, strain rate, and thermal softening without any interaction between the three of them.…”

Section: Introductionmentioning

confidence: 99%

On the Prediction of the Flow Behavior of Metals and Alloys at a Wide Range of Temperatures and Strain Rates Using Johnson–Cook and Modified Johnson–Cook-Based Models: A Review

et al. 2023

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This paper reviews the flow behavior and mathematical modeling of various metals and alloys at a wide range of temperatures and strain rates. Furthermore, it discusses the effects of strain rate and temperature on flow behavior. Johnson–Cook is a strong phenomenological model that has been used extensively for predictions of the flow behaviors of metals and alloys. It has been implemented in finite element software packages to optimize strain, strain rate, and temperature as well as to simulate real behaviors in severe conditions. Thus, this work will discuss and critically review the well-proven Johnson–Cook and modified Johnson–Cook-based models. The latest model modifications, along with their strengths and limitations, are introduced and compared. The coupling effect between flow parameters is also presented and discussed. The various methods and techniques used for the determination of model constants are highlighted and discussed. Finally, future research directions for the mathematical modeling of flow behavior are provided.

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“…For its great algorithm to solve the problem of nonlinear regression and excellent prediction accuracy of material rheological behavior, machine learning provided a new idea and method for the establishment of material constitutive models. The current machine learning models for flow stress prediction include artificial neural network [15][16][17][18][19], support vector machine regression [20,21] and K-nearest neighbor regression [22], etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of high-temperature flow stress of HMn64–8–5–1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

Qiang

Zhang

Liu

et al. 2022

Mater. Res. Express

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An accurate constitutive model is essential for designing the process of hot precision forging and numerical simulation. Based on the isothermal compression tests of as-extruded HMn64-8-5-1.5 manganese brass alloy at the deformation temperature of 873-1073 K and strain rate of 0.01-10 s-1, the effect of the friction and deformation temperature rise on the flow stress during the hot compression process was analyzed, and the flow stress curves were corrected. Three constitutive models based on the modified Zerilli-Armstrong, Arrhenius, and a back-propagation neural network (BPNN) optimized by the grey wolf optimization (GWO) algorithm (GWO-BPNN) models were established to describe the high-temperature flow stress of this alloy. Meanwhile, the prediction ability of the three models was evaluated by the calculated values of mean absolute percentage error (MAPE) and root mean square error (RMSE). The values of MAPE for the modified Zerilli-Armstrong, Arrhenius, and GWO-BPNN models were computed to be, 3.139 %, 2.448 % and 1.265 %, and the values of RMSE were calculated to be 1.804, 1.482 and 0.467 MPa, respectively. The GWO-BPNN model was with the greatest prediction ability for the flow stress among these models. The GWO algorithm was introduced to optimize the initial weights and thresholds of the BPNN model, and it has good prediction accuracy and better stability. It can better describe the high-temperature flow behavior of HMn64-8-5-1.5 alloy.

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Additive manufacturing – A review of hot deformation behavior and constitutive modeling of flow stress

Cited by 113 publications

References 199 publications

Analysis of hot tensile behaviour and prediction of thermal stresses during processing of AZ31 magnesium alloy

Analysis of hot tensile behaviour and prediction of thermal stresses during processing of AZ31 magnesium alloy

On the Prediction of the Flow Behavior of Metals and Alloys at a Wide Range of Temperatures and Strain Rates Using Johnson–Cook and Modified Johnson–Cook-Based Models: A Review

Prediction of high-temperature flow stress of HMn64–8–5–1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

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