prediction of the flow stress of 00cr17ni14mo2 steel

prediction of the flow stress of 00cr17ni14mo2 steel

(PDF) FLOW STRESS PREDICTION OF AUSTENITIC STAINLESS STEEL

In this paper, a comparative study has been made on the capability of Johnson Cook (JC) model and the Artificial Neural Networks (ANN) model for representing the flow stress prediction at elevated

(PDF) Mechanical Properties of Hardened AISI 52100 Steel

Fig. 6 The engineering stress-strain curve for AISI 52100 steel 62 HRC at 600°C Journal of Manufacturing Science and Engineering FEBRUARY 2002, V ol. 124 Õ 5 (PDF) Modelling of Flow Stress and Prediction of The isothermal hot compression tests of 43CrNi steel was carried out at temperatures 800ºC to 1050ºC at an interval of 50ºC and at constant strain rates of 0.01, 0.1, 1.0 and 10 s-1 for total

A Review on Fatigue Life Prediction Methods for Metals

The crack dimension has been identified as a crucial factor by a number of authors, because short fatigue cracks (having a small length compared to the scale of local plasticity, or to the key microstructural dimension, or simply smaller than 1-2 mm) in metals grow at faster rate and lower nominal stress compared to large cracks [72, 73].2.1.1. An Evolutionary Hybrid Model for the Prediction of Flow May 28, 2006 · A new hybrid model combining evolutionary artificial neural network (EANN) and mathematical models (MM) is proposed to improve the prediction precision of flow stress of 45 steel. In EANN, the optimal parameters are obtained by chaotic particle swarm optimization (CPSO) algorithm.

An Experimental Investigation of the Influence of Strain

Results obtained from machining and conventional slow-speed compression tests are used to calculate the constants 1 and n in the empirical stress/strain equation = 1 *** e n over a range of strain-rates (10-3 to 2·8 times 10 4 /s) and temperatures (room temperature to 200°C) for a low carbon, free machining steel. The results are consistent with those obtained for a similar material Artificial Neural Networks Based Approaches for the Jun 14, 2017 · Vannucci M., Colla V., Iannino V. (2017) Artificial Neural Networks Based Approaches for the Prediction of Mean Flow Stress in Hot Rolling of Steel. In:Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence.

CN101032783A - Explosion welding technique of composite

The present invention belongs to the field of explosive welding of composite steel pipe of different metal materials, and is explosive welding process of composite stainless steel-carbon steel pipe. The present invention solves the technological problems of designing explosive cartridge and explosion controlling device, configuring upper and lower casing molds outside the base pipe damping Components, Packaging and Manufacturing Technology - Prediction of the Flow Stress of 00Cr17Ni14Mo2 Steel during Hot Deformation View Section, 141. A Feasible Method for a Class of Mathematical Problems in Manufacturing System

Constitutive modeling for elevated temperature flow

The stressstrain values of 42CrMo steel predicted by the proposed model well agree with experimental results, which confirmed that the revised deformation constitutive equation gives an accurate and precise estimate for the flow stress of 42CrMo steel. Previous article in issue; Next article in issue; PACS. 83.10.Gr. 81.40.Lm. Effects of Multiphase Flow on Internal CO2 Corrosion of Jun 11, 2012 · The focus in this paper is on the effects of multiphase flow on CO2 corrosion of mild steel pipelines. The significance of mass transfer in turbulent flow is discussed first:(i) when an increased rate of mass transfer of corrosive species, such as H+ ions, to the steel surface leads to an acceleration of the cathodic reactions and a higher corrosion rate and (ii) when an increased mass

Effects of the grain size and shape on the flow stress:a

1 Effects of the grain size and shape on the flow stress:a dislocation dynamics study M. Jianga,b, B. Devincrea, G. Monnetb a LEM, CNRS-ONERA, 29 Avenue de la Division Leclerc, BP 72, 92322 Chatillon, France b EDF-R&D, MMC, Avenue des Renardières, 77818 Moret-sur-Loing, France Abstract Dislocation dynamics simulation is used to investigate the effect of grain size and grain shape Flow stress prediction for B210P steel at hot working May 17, 2013 · Prediction of the flow stress is a significant step to optimize the hot working processes. In order to establish a proper deformation constitutive equation, the compressive deformation behavior of B210P steel was investigated at temperature from 950° to 1150° and strain rates from 0.1s 1 to 10s 1 on a Gleeble-2000 thermo-simulation machine. Based on the true stress-strain data from

Flow stress prediction for B210P steel at hot working

May 17, 2013 · Prediction of the flow stress is a significant step to optimize the hot working processes. In order to establish a proper deformation constitutive equation, the compressive deformation behavior of B210P steel was investigated at temperature from 950° to 1150° and strain rates from 0.1s 1 to 10s 1 on a Gleeble-2000 thermo-simulation machine. Based on the true stress-strain data from Flow stress prediction for B210P steel at hot working May 17, 2013 · Prediction of the flow stress is a significant step to optimize the hot working processes. In order to establish a proper deformation constitutive equation, the compressive deformation behavior of B210P steel was investigated at temperature from 950° to 1150° and strain rates from 0.1s 1 to 10s 1 on a Gleeble-2000 thermo-simulation machine. Based on the true stress-strain data from

Flow stress prediction for B210P steel at hot working

Prediction of the flow stress is a significant step to optimize the hot working processes. In order to establish a proper deformation constitutive equation, the compressive deformation behavior of B210P steel was investigated at temperature from 950° to 1150° and strain rates from 0.1s -1 to 10s -1 on a Gleeble-2000 thermo-simulation machine. Hot Forming Flow Stress Prediction of Steel 50A1300 by The researches of non-oriented silicon steel are mainly focused on the effect of main processing parameters on the microstructure and magnetic properties, but there have been few studied about its flow stress until now. In this paper, the hot deformation of non-oriented silicon steel 50A1300 is studied by thermal-mechanical simulation method.

Influence of Yield Stress Determination in Anisotropic

May 14, 2018 · In this study, a numerical sensitivity analysis of the springback prediction was performed using advanced strain hardening models. In particular, the springback in U-draw bending for dual-phase 780 steel sheets was investigated while focusing on the effect of the initial yield stress determined from the cyclic loading tests. The anisotropic hardening models could reproduce the flow stress PREDICTION MODEL OF THE FLOW STRESS FOR THE The degree of conformity predicted and observed values of the flow stress is 82,6 94,7%. Keywords:Steel with an arbitrary chemical composition, hot rolling sheet, flow stress, thermomechanical factors, factor of influence the phase transformations, correction factor by the strain and strain rate.

PREDICTION MODEL OF THE FLOW STRESS FOR THE

The degree of conformity predicted and observed values of the flow stress is 82,6 94,7%. Keywords:Steel with an arbitrary chemical composition, hot rolling sheet, flow stress, thermomechanical factors, factor of influence the phase transformations, correction factor by the strain and strain rate. PREDICTION MODEL OF THE FLOW STRESS FOR THE The degree of conformity predicted and observed values of the flow stress is 82,6 94,7%. Keywords:Steel with an arbitrary chemical composition, hot rolling sheet, flow stress, thermomechanical factors, factor of influence the phase transformations, correction factor by the strain and strain rate.

Prediction of 42CrMo steel flow stress at high temperature

Prediction of 42CrMo steel flow stress at high temperature and strain rate View 0 peer reviews of Prediction of 42CrMo steel flow stress at high temperature and strain rate on Publons COVID-19 :add an open review or score for a COVID-19 paper now to ensure the Prediction of High Temperature Flow Stress for AISI 4120 The flow stress values predicted by the developed constitutive equations show a good agreement with experimental results, which reveals that the developed constitutive equations could give an accurate and precise prediction for the high temperature flow behaviors of AISI 4120 steel.

Prediction of Steel Flow Stresses under Hot Working Conditions

and softening mechanisms on the flow stress curve modeling of ultra-low carbon steel at high temperatures. Journal of Materials Procesing Technology. 2003; 142:415-421. 2. Laasraoui A, Jonas JJ. Prediction of steel flow stresses at high temperatures and strain rates. Metallurgical Transactions A. 1991; 22A:1545-1558. 3.simulated Medina SF Prediction of steel flow stresses at high temperatures and The flow behavior of steels during deformation in the roll gap was simulated by means of single hit compression tests performed in the temperature range 800 °C to 1200 °C. Strain rates of 0.2 to 50 s1 were employed on selected low-carbon steels containing various combinations of niobium, boron, and copper. The stress/strain curves determined at the higher strain rates were corrected for

Prediction of steel flow stresses under hot working conditions

ABSTRACT. An austenitic stainless steel was deformed in torsion over a temperature range of 900-1200 °C using strain rates of 1, 5 and 10 s-1.The stress vs. strain curves determined were corrected for deformation heating and the flow stress was found to rise in the initial work-hardening regime, reaching a maximum before dropping to the steady state due to softening brought about by dynamic Prediction of steel flow stresses under hot working conditionsABSTRACT. An austenitic stainless steel was deformed in torsion over a temperature range of 900-1200 °C using strain rates of 1, 5 and 10 s-1.The stress vs. strain curves determined were corrected for deformation heating and the flow stress was found to rise in the initial work-hardening regime, reaching a maximum before dropping to the steady state due to softening brought about by dynamic

Prediction of stress-strain relationships in low-carbon

of stress (3-4), although the precise function may not be known; it is also known that strain is a function of stress. Therefore, a correlation should exist between damping and strain, and we should be able to predict stress-strain relations from corresponding stress-damping relations. The purpose of this investigation is to make such a prediction. Prediction of the Flow Stress of 00Cr17Ni14Mo2 Steel A mathematical regression model is proposed to describe the flow stress and the validation of the model is conducted also. The proposed model can be used to predict the corresponding flow stress-strain response of 00Cr17Ni14Mo2 stainless steel in elevated temperature for the numerical simulation and design of forming process.

Prediction of the Flow Stress of 00Cr17Ni14Mo2 Steel

With the experiment result as the training set, the flow stress of 00Cr17Ni14Mo2 steel during hot deformation is predicted using a BP artificial neural network. The architecture of the network includes three input parameters, one output parameter and two hidden layers with 5 neurons in first layer and 6 neurons in second layer. Prediction of the Flow Stress of a High Alloyed Austenitic [Show full abstract] the experiment result as the training set, the flow stress of 00Cr17Ni14Mo2 steel during hot deformation is predicted using a BP artificial neural network. The architecture of

Prediction of the flow stress and grain size of steel

Jun 15, 2001 · In Fig. 1, flow stresses of AISI-1045 at different conditions were calculated by the Hernandez equations and compared to experimental results .The values agree well when the temperature and strain rate are within the ranges 10001200°C and 110 s 1, respectively.It is noted that the predicted flow stress is higher than the experimental value as temperature becomes lower Prediction of the flow stress for 30 MnSi steel using To obtain the flow stress data under varying conditions of strain, strain rate and temperature, hot compression experiments are conducted on 30 MnSi steel specimens using a GLEEBLE 1500 thermal simulator. To more accurately predict flow stress, ELS-SVM-MM - the method combining evolutionary least squares-support vector machines (ELS-SVM) with mathematical models is proposed.

Prediction of the flow stress for 30 MnSi steel using

To obtain the flow stress data under varying conditions of strain, strain rate and temperature, hot compression experiments are conducted on 30 MnSi steel specimens using a GLEEBLE 1500 thermal simulator. To more accurately predict flow stress, ELS-SVM-MM - the method combining evolutionary least squares-support vector machines (ELS-SVM) with mathematical models is proposed. Prediction of the flow stress of high-speed steel during Jun 15, 2000 · The hot deformation behavior of T1 (W18Cr4V) high-speed steel was investigated by means of continuous compression tests performed on a Gleeble 1500 Thermomechanical simulator over a wide range of temperatures (9501150°C) with strain rates of 0.00110 s 1 and true strains of 00.7. The flow stress under the above-mentioned hot deformation conditions is predicted using a BP

Symmetry Free Full-Text Microstructure Evaluation and

In this work, the hot tensile test flow stress-strain data were utilized to construct the constitutive equation for describing AISI-1045 steel material hot deformation behavior, and the test conditions, such as deformation temperatures and strain rates were 750950 ° C and 0.051.0 s 1 , respectively. The Prediction of the Flow Stress of an Extra-Low Carbon Continuous cooling curves of an extra-low carbon steel under three cooling rates are measured. The flow stress of the steel is established in compression tests during which the temperature is continuously decreasing. The phase transformation temperatures are determined from the cooling rate curve.

Prediction of the Flow Stress of 00Cr17Ni14Mo2 Steel

With the experiment result as the training set, the flow stress of 00Cr17Ni14Mo2 steel during hot deformation is predicted using a BP artificial neural network.

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