Constitutive model research on the hot deformation behavior of Ti6Al4V alloy under wide temperatures

Jiang, Fei; Fei, Liangyu; Jiang, Hong; Zhang, Yiming; Feng, Zhiyan; Zhao, Shengdun

doi:10.1016/j.jmrt.2023.01.021

Cited by 13 publications

(8 citation statements)

References 38 publications

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“…Graphical and statistical tools were used in this comparison, and detailed information about GWO‐RBF and GWO‐BPNN can be found elsewhere. [ 47,48 ] The resulting intelligent model parameters are shown in Table 4 . A comparison of the performance evaluation metrics for each model is shown in Table 5 .…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO₂–Crude Oil Minimum Miscibility Pressure

Li,

Bian

2024

Energy Tech

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The minimum miscibility pressure (MMP) is an important reference parameter in the study of CO2 oil drive systems. In response to the problems of time‐consuming and costly prediction of MMP by conventional experimental methods, an improved least squares support vector machine (LSSVM) model based on grey wolf optimizer (GWO) algorithm is proposed to predict the CO2–crude oil MMP. Based on Pearson correlation analysis, reservoir temperature, C5+ molecular weight, intermediate component mole fraction, and volatile component mole fraction are selected as independent variables of the model, and MMP is the dependent variable. A total of 51 MMP experimental data are collected, of which 35 are used to fine‐tune the model's parameters and 16 are used to verify the model's reliability. The high leverage point method is used to detect anomalies in all experimental data to check the reliability of the model, and the abnormality of only one piece of experimental data is identified. Finally, a comparison of the model with other intelligent models is found. The absolute relative deviation of the GWO‐LSSVM model is 2.24% for the training data and 3.48% for the test data, which provides high adaptability and accuracy.

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Section: Analysis Of Resultsmentioning

confidence: 99%

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO₂–Crude Oil Minimum Miscibility Pressure

Li,

Bian

2024

Energy Tech

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“…The strain rate sensitivity is presented by constant . Constants , , and are empirically presented as next 31 , 32 , 58 : where and are material constants, which can be determined using experimental data as it will be explained later.…”

Section: Methodsmentioning

confidence: 99%

“…Titanium-based alloys have complex hot deformation behavior; therefore, constitutive models sometimes fail to accurately predict the flow behavior. Accordingly, different modifications for many constitutive models have been presented to precisely predict the flow behavior of titanium-based alloys during hot deformation 28 – 31 .…”

Section: Introductionmentioning

confidence: 99%

“…However, the model does not take thermal softening into account; therefore, it gives accurate predictions for some alloys 32 – 34 and not accurate predictions for other alloys 35 , 36 . Therefore, some modifications for the FB model are presented that takes the thermal softening into account 31 , 37 , 38 . One of the well-known physically based models is the Zerilli-Armstrong (ZA) model 21 .…”

Section: Introductionmentioning

confidence: 99%

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Modified Fields-Backofen and Zerilli-Armstrong constitutive models to predict the hot deformation behavior in titanium-based alloys

Shokry

2024

Sci Rep

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This work presents modifications for two constitutive models for the prediction of the flow behavior of titanium-based alloys during hot deformation. The modified models are the phenomenological-based Fields-Backofen and the physical-based Zerilli-Armstrong. The modifications are derived and suggested by studying the hot deformation of titanium-based alloy Ti55531. The predictability of the modified models along with the original Fields-Backofen and another modified Zerilli-Armstong models is assessed and evaluated using the well-known statistical parameters correlation coefficient (R), Average Absolute Relative Error (AARE), and Root Mean Square Error (RMSE), for the Ti55531 alloy, and validated with other two different titanium-based alloys SP700 and TC4. The results show that the modified Fields-Backofen gives the best performance with R value of 0.996, AARE value of 3.34%, and RMSE value of 5.64 MPa, and the improved version of the modified Zerilli-Armstrong model comes in the second-best place with R value of 0.992, AARE value of 3.52%, and RMSE value of 9.15 MPa for the Ti55531 alloy.

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“…Titanium alloys have been widely utilized for many years due to their high corrosion resistance, high specific strength, and low density (about 60% of the density of steel) [1][2][3]. Forging is an advanced shaping process that can produce near-net-shape product and enhance the mechanical properties of alloys, and it is widely employed in aerospace and other related industries [4,5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of ductile cracking in the titanium alloy forging process

Fan,

Zhang,

Yuan

2024

Mater. Res. Express

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The control of surface cracking in the forming of titanium alloy forgings is a significant problem in the forging industry. For titanium alloys, the formation of surface cracks is related to temperature, strain rate, and stress state. This study selected the widely used medium to high strength titanium alloy Ti-6Al-4V in the field of forging as the research material, and designed six different shapes of specimens for high-temperature tensile and compression tests. The mechanisms underlying crack formation were analyzed at the microscopic level, and the critical fracture displacement of these tests was extracted. Moreover, their critical fracture strains were obtained through simulations, and a High-temperature damage model was established based on the DF2016 model. The research results showed that cracks through void at grain boundaries propagate and aggregate to form, leading to a fracture mechanism characterized by ductile fracture through micro-pore aggregation. Simulation results demonstrate that the established model accurately predicts the crack of forgings.

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Constitutive model research on the hot deformation behavior of Ti6Al4V alloy under wide temperatures

Cited by 13 publications

References 38 publications

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO₂–Crude Oil Minimum Miscibility Pressure

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO₂–Crude Oil Minimum Miscibility Pressure

Modified Fields-Backofen and Zerilli-Armstrong constitutive models to predict the hot deformation behavior in titanium-based alloys

Prediction of ductile cracking in the titanium alloy forging process

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Constitutive model research on the hot deformation behavior of Ti6Al4V alloy under wide temperatures

Cited by 13 publications

References 38 publications

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO2–Crude Oil Minimum Miscibility Pressure

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO2–Crude Oil Minimum Miscibility Pressure

Modified Fields-Backofen and Zerilli-Armstrong constitutive models to predict the hot deformation behavior in titanium-based alloys

Prediction of ductile cracking in the titanium alloy forging process

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Product

Resources

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Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO₂–Crude Oil Minimum Miscibility Pressure

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO₂–Crude Oil Minimum Miscibility Pressure