Modeling the hot deformation flow curves of API X65 pipeline steel

Rakhshkhorshid, M.

doi:10.1007/s00170-014-6447-6

Cited by 15 publications

(3 citation statements)

References 20 publications

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“…The detailed procedures to calculate the values of n, α, and Ԛ have been introduced in the previous investigations [45,46]. Based on the experimentally-measured true stresstrue strain curves, the deformation activation energy (Ԛ) at the strain of 0.8 can be evaluated as 399.3 kj•mol -1 .…”

Section: Flow Softening Behaviormentioning

confidence: 99%

Study of Flow Softening Mechanisms of a Nickel-Based Superalloy With Δ Phase

Lin

Chen

et al. 2016

Archives of Metallurgy and Materials

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The flow softening behaviors of a nickel-based superalloy with δ phase are investigated by hot compression tests over wide ranges of deformation temperature and strain rate. Electron backscattered diffraction (EBSD), optical microscopy (OM), and scanning electron microscopy (SEM) are employed to study the flow softening mechanisms of the studied superalloy. It is found that the flow softening behaviors of the studied superalloy are sensitive to deformation temperature and strain rate. At high strain rate and low deformation temperature, the obvious flow softening behaviors occur. With the increase of deformation temperature or decrease of strain rate, the flow softening degree becomes weaken. At high strain rate (1s−1), the flow softening is mostly induced by the plastic deformation heating and flow localization. However, at low strain rate domains (0.001-0.01s−1), the effects of deformation heating on flow softening are slight. Moreover, the flow softening at low strain rates is mainly induced by the discontinuous dynamic recrystallization and the dissolution of δ phase (Ni3Nb).

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Section: Flow Softening Behaviormentioning

confidence: 99%

Study of Flow Softening Mechanisms of a Nickel-Based Superalloy With Δ Phase

Lin

Chen

et al. 2016

Archives of Metallurgy and Materials

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“…There is an excellent t between the predicted and experimental ow curves in using the developed RBF model. This together with very low RMSE value showed the robustness of the developed RBF model to predict the best media culture of the tested (11).…”

Section: Introductionmentioning

confidence: 67%

Choosing the best embryogenesis medium in carrot by data mining technology

Ziarani¹,

Tohidfar²,

Mirjalili³

et al. 2020

Preprint

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Plant cell, tissue and organ culture (PCTOC) is extensively used to propagate faster and more plants, to produce virus-free plants, and secondary metabolites production as well. This requires the optimization of PCTOC conditions for each plant and final aim. Optimizing the micropropagation is time-consuming and costly, because it is different from the plant and even for each variety. In addition requires the optimization of the concentration and type of hormone and the type of explants for each variety in the stages of callogenesis, embryogenesis, shooting and rooting. Hence, today researchers have used Data Mining with using an artificial neural network (ANN) to predict the best conditions for tissue culture and saved time and money considerably. In this research, radial basis function (RBF) model was used to predict the best conditions for carrot tissue culture and the results showed that the highest and the least sensitivity were related to variety and percentage of Agar in liter, respectively. The results prediction of the RBF model showed that the percentage of embryogenesis was 62.5%, but the percentage of embryogenesis in laboratory obtain 75%. The results showed that the RBF model is a good model to predict the results.

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“…Thermo-mechanical processing is the technique to tailor the mechanical properties of steel, which is impossible to realize by thermal treatment or forming operation alone [16]. It is therefore a sophisticated combination of well-defined thermal and deformation path [17].…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis of flow behaviour, grain size prediction and experimental verification of hot rolled ultralow carbon niobium microalloyed steel

Khalifa

Megahed

El-Bitar

et al. 2021

Materialwissenschaft Werkst

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Niobium bearing ultralow carbon micro alloyed dual phase steel grade steel with chemical composition of 0.045 wt.% carbon, 0.04 wt.% niobium, 0.59 wt.% chromium, 0.9 wt.% manganese, 0.28 wt.% silicon is investigated. The design of thermomechanical treatment needs knowledge about the flow behaviour at high deformation temperatures, to be used for this steel in industrial plants. Therefore, this work aims to predict, for the investigated steel, the flow behaviour and mean flow stress. A phenomenological constitutive model is established to derive the flow stress using the hyperbolic sinusoidal Arrhenius mathematical model. The relation between stress and strain with Zener‐Hollomon parameter is studied. Mean flow stress has been figured out by measurements taken from compact slab production plant log data using a constitutive model. The constitutive model is further verified by multiple hits of flat compression setup mode at BährTTS820 physical simulator. The tests were performed under specific thermochemical schedule simulating compact slab production plant. The results show good agreement between the calculated flow stress, Non‐recrystallization temperature and experimentally measured values obtained from the physical simulation. Ferrite grain size is modelled and predicted then validated by experimental rolled specimens in a practical hot strip mill.

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Modeling the hot deformation flow curves of API X65 pipeline steel

Cited by 15 publications

References 20 publications

Study of Flow Softening Mechanisms of a Nickel-Based Superalloy With Δ Phase

Study of Flow Softening Mechanisms of a Nickel-Based Superalloy With Δ Phase

Choosing the best embryogenesis medium in carrot by data mining technology

Numerical analysis of flow behaviour, grain size prediction and experimental verification of hot rolled ultralow carbon niobium microalloyed steel

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