A Comparative Study on Artificial Neural Network, Phenomenological-Based Constitutive and Modified Fields–Backofen Models to Predict Flow Stress in Ti-4Al-3V-2Mo-2Fe Alloy

Shen, Jun; Hu, Lianxi; Sun, Yu; Wan, Zhipeng; Feng, Xiaoyun; Ning, Yongquan

doi:10.1007/s11665-019-04174-0

Cited by 22 publications

(6 citation statements)

References 33 publications

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“…ε and T. Thus, constitutive modelling has been widely applied in flow behaviour prediction [31][32][33][34][35][36][37][38][39][40] and forming simulation [41][42][43] studies. Constitutive models are classified into physically based constitutive models [27][28][29][30][31][32][33][34][35][36], phenomenological constitutive models [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], and machine learning-based modelling [53][54][55]. The optimal constitutive model should possess a moderate number of material parameters, which may be assessed via a few experimental data, and accurately predict the mechanical behaviour of materials over a wide range of rheological variables [50][51][52][53].…”

Section: Constitutive Modellingmentioning

confidence: 99%

“…ε and T without necessitating a comprehensive understanding of the rheological factors involved in the forming process [37][38][39][40][41][42]. These models are primarily derived through empirical fitting and regression analysis, making them particularly useful for modelling materials' flow behaviour and integrating with FE codes to replicate real-world forming processes under various conditions [43][44][45][46][47]. The Johnson-Cook (JC) model has gained popularity in various FE applications due to its fast computation speed, minimal computational demands, and straightforward formulation [48,49].…”

Section: Introductionmentioning

confidence: 99%

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Modelling the Flow Behaviour of Al Alloy Sheets at Elevated Temperatures Using a Modified Zerilli–Armstrong Model and Phenomenological-Based Constitutive Models

Abd El-Aty,

Xu,

Hou

et al. 2024

Materials

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The flow behaviour of AA2060 Al alloy under warm/hot deformation conditions is complicated because of its dependency on strain rates (ε˙), strain (ε), and deformation modes. Thus, it is crucial to reveal and predict the flow behaviours of this alloy at a wide range of temperatures (T) and ε˙ using different constitutive models. Firstly, the isothermal tensile tests were carried out via a Gleeble-3800 thermomechanical simulator at a T range of 100, 200, 300, 400, and 500 °C and ε˙ range of 0.01, 0.1, 1, and 10 s−1 to reveal the warm/hot flow behaviours of AA2060 alloy sheet. Consequently, three phenomenological-based constitutive models (L-MJC, S1-MJC, S2-MJC) and a modified Zerilli–Armstrong (MZA) model representing physically based constitutive models were developed to precisely predict the flow behaviour of AA2060 alloy sheet under a wide range of T and ε˙. The predictability of the developed constitutive models was assessed and compared using various statistical parameters, including the correlation coefficient (R), average absolute relative error (AARE), and root mean square error (RMSE). By comparing the results determined from these models and those obtained from experimentations, and confirmed by R, AARE, and RMSE values, it is concluded that the predicted stresses determined from the S2-MJC model align closely with the experimental stresses, demonstrating a remarkable fit compared to the S1-MJC, L-MJC, and MZA models. This is because of the linking impact between softening, the strain rate, and strain hardening in the S2-MJC model. It is widely known that the dislocation process is affected by softening and strain rates. This is attributed to the interactions that occurred between ε and ε˙ from one side and between ε, ε˙, and T from the other side using an extensive set of constants correlating the constitutive components of dynamic recovery and softening mechanisms.

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Section: Constitutive Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling the Flow Behaviour of Al Alloy Sheets at Elevated Temperatures Using a Modified Zerilli–Armstrong Model and Phenomenological-Based Constitutive Models

Abd El-Aty,

Xu,

Hou

et al. 2024

Materials

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“…Among these phenomenological models are widely used models. 143 In the phenomenological models, flow stress depends on the empirical observation using some mathematical functions; these models are suitable where the exact physical mechanism is not clear. However, these are applicable over a limited range of strain rates and temperatures.…”

Section: Constitutive Equationmentioning

confidence: 99%

Hot deformation behaviour of Ti alloys: A review on physical simulation and deformation mechanisms

Yadav

Saxena

Sehgal³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Titanium and its alloys, owing to their properties like high strength, toughness, corrosion resistance and thermal stability, are employable in various engineering and medical applications. The properties of titanium alloys depend upon the processing routes and their final microstructure. Therefore, the present review paper compiles the deformation behaviour of various alloys during hot deformation using a physical simulation. Subsequently, the flow stresses are analysed and utilized to develop the processing maps and the constitutive equations that are advantageous for predicting deformation mechanisms during the hot deformation. Specific features reported in the flow stress curves include work hardening, flow softening and steady-state behaviour. In certain cases, the yield point drop and oscillatory behaviour or serrations are also observed. Softening and oscillatory behaviour is an indicator of either dynamic recrystallization or flow-instability, whereas yield discontinuity signifies locking and unlocking of dislocations. In the processing maps, high power dissipation efficiency, η, reveals safe processing conditions, and the η value higher than 40% demonstrates dynamic recrystallization or globularization in the deformed microstructure, whereas the instability domain expresses shear band, flow localization, void formation and wedge cracks in the deformed microstructure. Amongst all the constitutive equations, the Arrhenius-type hyperbolic sine equation is the most suitable for Ti alloys for calculating flow stress and predicting dominant deformation mechanism using activation energy Q and stress exponent n.

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“…Mangala and Holm [ 24 ] predicted the hot spot stress of the alloy. Shen et al [ 25 ] developed the constitutive model to study the Ti-4Al-3V-2Mo-2Fe titanium alloy. Shen et al [ 26 ] studied a metamodeling method to simulate the constitutive relationship of the TC6 titanium alloy.…”

Section: Introductionmentioning

confidence: 99%

Hot Deformation and Constitutive Modeling of TC21 Titanium Alloy

Yi¹,

Yang²,

Xie³

2022

Materials

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Titanium alloys are extensively employed in the fabrication of various aviation structural parts, of which the most crucial processing step is hot working. In order to study the high-temperature deformation behavior of the TC21 titanium alloy, high-temperature tensile tests were performed. The results reveal that the flow stress of the material gradually decreases with an increased strain rate, and the stress increases rapidly with an increase in strain during the deformation of the alloy. Following this, flow stress gradually decreases. Flow stress decreases sharply, and the sample fractures when the appearance of necking and microvoids is observed. The Arrhenius and Radial basis function (RBF) neural network constitutive models are established in order to accurately describe the high-temperature deformation behavior of the material. In the modified Arrhenius model, strain rate indexes are expressed as a function of deformation temperature and strain rates; furthermore, the high prediction ability of the model was obtained. For the Radial basis function, the network parameters were obtained using the trial-and-error method. The established models could better forecast the flow stress of materials, and highly accurate results are obtained using the radial basis function model. The relationships between the stress index and the deformation activation energy with strain indicate that the primary deformation mechanism involves grain boundary slip and viscous slip of dislocations. The process of dynamic recrystallization primarily promotes the softening of the material.

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A Comparative Study on Artificial Neural Network, Phenomenological-Based Constitutive and Modified Fields–Backofen Models to Predict Flow Stress in Ti-4Al-3V-2Mo-2Fe Alloy

Cited by 22 publications

References 33 publications

Modelling the Flow Behaviour of Al Alloy Sheets at Elevated Temperatures Using a Modified Zerilli–Armstrong Model and Phenomenological-Based Constitutive Models

Modelling the Flow Behaviour of Al Alloy Sheets at Elevated Temperatures Using a Modified Zerilli–Armstrong Model and Phenomenological-Based Constitutive Models

Hot deformation behaviour of Ti alloys: A review on physical simulation and deformation mechanisms

Hot Deformation and Constitutive Modeling of TC21 Titanium Alloy

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