scholarly journals On the Zener–Hollomon Parameter, Multi-Layer Perceptron and Multivariate Polynomials in the Struggle for the Peak and Steady-State Description

Metals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1413 ◽  
Author(s):  
Petr Opěla ◽  
Petr Kawulok ◽  
Ivo Schindler ◽  
Rostislav Kawulok ◽  
Stanislav Rusz ◽  
...  
Keyword(s):  
Steady State ◽  
Flow Stress ◽  
Flow Curve ◽  
Multivariate Polynomials ◽  
Multi Layer Perceptron ◽  
Stress Evolution ◽  
Flow Curves ◽  
Hollomon Parameter ◽  
Wide Range ◽  
Stress Models

Description of flow stress evolution, specifically an approximation of a set of flow curves acquired under a wide range of thermomechanical conditions, of various materials is often solved via so-called flow stress models. Some of these models are associated with a description of significant flow-curve coordinates. It is clear, the more accurate the coordinates description, the more accurate the assembled model. In the presented research, Zener–Hollomon-based relations, multi-layer perceptron networks and multivariate polynomials are employed to describe the peak and steady-state coordinates of an Invar 36 flow curve dataset. Comparison of the utilized methods in the case of the studied alloy has showed that the suitable description is given by the multivariate polynomials although the Zener–Hollomon and perceptron networks also offer valuable results.

A Simple Constitutive Model for Prediction of Single-Peak Flow Curves Under Hot Working Conditions

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
Roxana Baktash ◽  
Hamed Mirzadeh
Keyword(s):  
Flow Stress ◽  
Strain Rate ◽  
Peak Flow ◽  
Rate Sensitivity ◽  
Peak Strain ◽  
Flow Curves ◽  
Hollomon Parameter ◽  
Proposed Model ◽  
Stress Coefficient ◽  
Hollomon Equation

The hot flow stress of a typical stainless steel was modeled by the Hollomon equation, a modified form of the Hollomon equation, and another modified form based on the Fields–Backofen equation. The coupled effect of the deformation temperature and strain rate was also taken into account in the proposed formulae by consideration of the Zener–Hollomon parameter or dependency of the constants on temperature. The modified Fields–Backofen equation was found to be appropriate for prediction of flow stress, in which the incorporation of peak strain and consideration of temperature dependencies of the strain rate sensitivity and the stress coefficient were found to be beneficial. Moreover, the simplicity of the proposed model justifies its applicability for expressing hot flow stress characterizing dynamic recrystallization (DRX).

Modeling the Flow Curve of Hot Deformed Austenite

2012 ◽  
Vol 715-716 ◽  
pp. 81-88 ◽  
Author(s):  
John J. Jonas ◽  
Xavier Quelennec ◽  
Lan Jiang
Keyword(s):  
Dynamic Recrystallization ◽  
Work Hardening ◽  
Flow Curve ◽  
Strain Rate Range ◽  
Critical Conditions ◽  
Flow Curves ◽  
Hollomon Parameter ◽  
Model Compression ◽  
Order Of Magnitude ◽  
Avrami Kinetics

A 0.02%C plain carbon and a 0.22%C TRIP steel were tested in compression in the temperature range 900°C to 1150°C and strain rate range 0.05s-1 to 1s-1. Thirty-two experimental flow curves were obtained in this way. The critical conditions for the initiation of dynamic recrystallization were determined by the double differentiation method. Using a dislocation density model to describe the austenite flow stress, the work hardening parameters r and h were derived and are used to model the flow curve in the absence of dynamic recrystallization. The latter was employed to calculate the fractional softening attributable to dynamic recrystallization. The kinetics of dynamic recrystallization are then described using Avrami kinetics. Finally, the dependences of the Avrami and work hardening parameters on Z, the Zener-Hollomon parameter, are used to model compression flow curves at strain rates an order of magnitude greater than the ones employed in the tests.

Flow Curve Modelling of an Austenitic Stainless Steel at High Temperatures Starting from the One-Parameter Model of Strain Hardening

2012 ◽  
Vol 706-709 ◽  
pp. 1361-1366 ◽  
Author(s):  
Giuliano Angella
Keyword(s):  
Stainless Steel ◽  
Flow Stress ◽  
Austenitic Stainless Steel ◽  
Strain Rate ◽  
Strain Hardening ◽  
Flow Curve ◽  
Flow Curves ◽  
Rate Dependent ◽  
The One ◽  
Voce Equation

The flow curves of an austenitic stainless steel deformed at temperatures 700-1000°C with strain rates 10-5-10-2s-1were modelled with the Voce equation. The parameters needed to draw the Voce equation, are the saturation stressσVthat defines the height of the flow curve, the critical strainεCthat defines the velocity to achieveσV, and the stressσo, namely the back-extrapolated flow stress to zero strain. A modified strain hardening analysis based on the one-parameter model was used to analyze the strain hardening rate dσ/dεvs. the flow stressσin order to obtainσVandεC. The modified approach was based on the assumption that the dislocation multiplication component of strain hardening was temperature and strain rate dependent through the thermal activation termsof flow stress. A parameters’ proportional toswas obtained from the strain hardening analysis and a relationship betweens’ and temperature and strain rate was found. Relationships betweenσV,σo,εCands’ were finally established and at this stage the Voce equation could reproduce the experimental flow curves at any imposed deformation conditions of temperature and strain rate.

scholarly journals Flow curve prediction of cold forging steel by artificial neural network model

2021 ◽  
Author(s):  
Fatih Kocatürk ◽  
M. Burak Toparli ◽  
Barış Tanrıkulu ◽  
Sezgin Yurtdaş ◽  
Doğuş Zeren ◽  
...  
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Strain Rate ◽  
Flow Curve ◽  
Material Model ◽  
Cold Forging ◽  
Ann Model ◽  
Flow Curves ◽  
Wide Range ◽  
Artificial Neural

A limited number of material models or flow curves are available in commercial finite element softwares at varying temperature and strain rate ranges for plasticity analysis. To obtain more realistic finite element results, flow curves at wide temperature and strain rate ranges are required. For this purpose, a material model for a medium carbon alloy steel material which is used for fastener production was prepared. Firstly, flow curves of the material were obtained at 4 temperatures (20, 100, 200, 400 °C) and 3 strain rates (1, 10, 50 s-1). Then, experimental data was used to construct an artificial neural networks model (ANN) for the material. 75% of the experimental data was used to train the model and the rest was employed for validation and verification. ANN model used in flow curve prediction was developed using the scikit-learn library on Python. Temperature, strain rate and strain were employed as input parameters and flow stress as output parameter in ANN model. In order to increase the accuracy of the ANN model, the number of hidden layers and the number of neurons were also optimized by mean squared error approach. As a result of studies, an ANN-based material model that can be used for wide range of temperature and strain rate values were developed based on the experimental data.

scholarly journals Quasi-Static Loading Responses and Constitutive Modeling of Al–Si–Mg alloy

Metals ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 838 ◽  
Author(s):  
Zhenglong Liang ◽  
Qi Zhang
Keyword(s):  
Flow Stress ◽  
Strain Rate ◽  
Flow Behavior ◽  
Peak Stress ◽  
Mg Alloy ◽  
Average Error ◽  
Flow Curves ◽  
Modified Model ◽  
Wide Range ◽  
Measured Flow

The flow behavior of the Al–Si–Mg alloy under uniaxial compression loading was investigated at different strain rates (10−3 s−1, 10−2 s−1,10−1 s−1, 100 s−1) at a wide range of temperatures (573 K, 623 K, 673 K, 723 K, 773 K). The results showed that the peak stress increase with the strain rate and decrease with the increase of temperature. According to the measured flow stress curves, a modified Johnson-Cook (J-C) constitutive model taking strain rate effect on thermo softening into account was proposed to delineate the flow behavior. The comparisons between the measured flow curves and the predicted ones showed them to be very close and the average error is 1.65%. The added experiments were also conducted for validating the modified model, and the predicted data well agreed with the measured flow stress curves. That indicated the modified Johnson-Cook model is reliable and can accurately delineate the flow behavior of Al–Si–Mg alloy.

Spectroscopic Studies of Asparaginyl-tRNA Synthetase from Entamoeba histolytica

2019 ◽  
Vol 26 (6) ◽  
pp. 435-448
Author(s):  
Priyanka Biswas ◽  
Dillip K. Sahu ◽  
Kalyanasis Sahu ◽  
Rajat Banerjee
Keyword(s):  
Circular Dichroism ◽  
Steady State ◽  
Entamoeba Histolytica ◽  
Protein Concentration ◽  
Trna Synthetase ◽  
Solvent Exposure ◽  
Steady State Fluorescence ◽  
State Process ◽  
Wide Range ◽  
Circular Dichroïsm

Background: Aminoacyl-tRNA synthetases play an important role in catalyzing the first step in protein synthesis by attaching the appropriate amino acid to its cognate tRNA which then transported to the growing polypeptide chain. Asparaginyl-tRNA Synthetase (AsnRS) from Brugia malayi, Leishmania major, Thermus thermophilus, Trypanosoma brucei have been shown to play an important role in survival and pathogenesis. Entamoeba histolytica (Ehis) is an anaerobic eukaryotic pathogen that infects the large intestines of humans. It is a major cause of dysentery and has the potential to cause life-threatening abscesses in the liver and other organs making it the second leading cause of parasitic death after malaria. Ehis-AsnRS has not been studied in detail, except the crystal structure determined at 3 Å resolution showing that it is primarily α-helical and dimeric. It is a homodimer, with each 52 kDa monomer consisting of 451 amino acids. It has a relatively short N-terminal as compared to its human and yeast counterparts. Objective: Our study focusses to understand certain structural characteristics of Ehis-AsnRS using biophysical tools to decipher the thermodynamics of unfolding and its binding properties. Methods: Ehis-AsnRS was cloned and expressed in E. coli BL21DE3 cells. Protein purification was performed using Ni-NTA affinity chromatography, following which the protein was used for biophysical studies. Various techniques such as steady-state fluorescence, quenching, circular dichroism, differential scanning fluorimetry, isothermal calorimetry and fluorescence lifetime studies were employed for the conformational characterization of Ehis-AsnRS. Protein concentration for far-UV and near-UV circular dichroism experiments was 8 µM and 20 µM respectively, while 4 µM protein was used for the rest of the experiments. Results: The present study revealed that Ehis-AsnRS undergoes unfolding when subjected to increasing concentration of GdnHCl and the process is reversible. With increasing temperature, it retains its structural compactness up to 45ºC before it unfolds. Steady-state fluorescence, circular dichroism and hydrophobic dye binding experiments cumulatively suggest that Ehis-AsnRS undergoes a two-state transition during unfolding. Shifting of the transition mid-point with increasing protein concentration further illustrate that dissociation and unfolding processes are coupled indicating the absence of any detectable folded monomer. Conclusion: This article indicates that GdnHCl induced denaturation of Ehis-AsnRS is a two – state process and does not involve any intermediate; unfolding occurs directly from native dimer to unfolded monomer. The solvent exposure of the tryptophan residues is biphasic, indicating selective quenching. Ehis-AsnRS also exhibits a structural as well as functional stability over a wide range of pH.

Effect of the Si and Al Content in Ferritic Electrical Steels on the Flow Behaviour and Dynamic Softening in Hot Rolling

2013 ◽  
Vol 762 ◽  
pp. 747-752
Author(s):  
Pablo Rodriguez-Calvillo ◽  
M. Perez-Sine ◽  
Jürgen Schneider ◽  
Harti Hermann ◽  
Jose María Cabrera ◽  
...  
Keyword(s):  
Hot Rolling ◽  
Flow Behaviour ◽  
Flow Curves ◽  
Electrical Steels ◽  
Al Content ◽  
Wide Range ◽  
Dynamic Softening ◽  
Hot Rolling Conditions ◽  
Hardening And Softening

FeSi steels with and without addition of Al are widely used as electrical steels. To improve the knowledge of the effects by the addition of Si and Al on the hardening and softening under hot rolling conditions, the behaviour of the flow curves in a wide range of temperatures and deformation velocities have been studied.

scholarly journals Identification of DC Thermal Steady-State Differential Inductance of Ferrite Power Inductors

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3854
Author(s):  
Salvatore Musumeci ◽  
Luigi Solimene ◽  
Carlo Stefano Ragusa
Keyword(s):  
Steady State ◽  
Input Voltage ◽  
Switching Frequency ◽  
Ohmic Losses ◽  
Steady State Temperature ◽  
Wide Range ◽  
Average Current ◽  
Power Inductors ◽  
Saturable Inductor ◽  
Thermal Steady State

In this paper, we propose a method for the identification of the differential inductance of saturable ferrite inductors adopted in DC–DC converters, considering the influence of the operating temperature. The inductor temperature rise is caused mainly by its losses, neglecting the heating contribution by the other components forming the converter layout. When the ohmic losses caused by the average current represent the principal portion of the inductor power losses, the steady-state temperature of the component can be related to the average current value. Under this assumption, usual for saturable inductors in DC–DC converters, the presented experimental setup and characterization method allow identifying a DC thermal steady-state differential inductance profile of a ferrite inductor. The curve is obtained from experimental measurements of the inductor voltage and current waveforms, at different average current values, that lead the component to operate from the linear region of the magnetization curve up to the saturation. The obtained inductance profile can be adopted to simulate the current waveform of a saturable inductor in a DC–DC converter, providing accurate results under a wide range of switching frequency, input voltage, duty cycle, and output current values.

SET-VALUED PERFORMANCE APPROXIMATIONS FOR THE QUEUE GIVEN PARTIAL INFORMATION

2020 ◽  
pp. 1-23
Author(s):  
Yan Chen ◽  
Ward Whitt
Keyword(s):  
Steady State ◽  
Decay Rate ◽  
Waiting Time ◽  
Partial Information ◽  
Upper And Lower Bounds ◽  
Interarrival Time ◽  
Limited Information ◽  
Wide Range ◽  
The Mean ◽  
Third Moment

In order to understand queueing performance given only partial information about the model, we propose determining intervals of likely values of performance measures given that limited information. We illustrate this approach for the mean steady-state waiting time in the $GI/GI/K$ queue. We start by specifying the first two moments of the interarrival-time and service-time distributions, and then consider additional information about these underlying distributions, in particular, a third moment and a Laplace transform value. As a theoretical basis, we apply extremal models yielding tight upper and lower bounds on the asymptotic decay rate of the steady-state waiting-time tail probability. We illustrate by constructing the theoretically justified intervals of values for the decay rate and the associated heuristically determined interval of values for the mean waiting times. Without extra information, the extremal models involve two-point distributions, which yield a wide range for the mean. Adding constraints on the third moment and a transform value produces three-point extremal distributions, which significantly reduce the range, producing practical levels of accuracy.

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