Research on the dynamic recrystallization behavior of GCr15 steel

Yue, Chong-xiang; Zhang, Liwen; Liao, Shu-lun; Ji-bin, Pei; Gao, Hui-ju; Jia, Yuanwei; Lian, Xiaojie

doi:10.1016/j.msea.2007.11.123

Cited by 70 publications

(33 citation statements)

References 9 publications

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“…), aluminum alloy (beam, fixed frame), copper alloy (nozzle, heating wire), PMMA (hopper, support between hopper and the polarization motor). According to the literature [4,5,6], the main material properties as shown in Table 1 Contact surface type setting and grid optimization. The contact types in finite element software ANSYS is divided into the fixed contact, non-separation contact, non-friction contact, and friction contact [7,8,9,16].…”

Section: Finite Element Analysis Of Modelmentioning

confidence: 99%

Investigation and Analysis of Mechanical Platform Optimization for MEM Type 3D Printer

Huang¹,

Shi²,

Xu³

et al. 2018

dtcse

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Abstract. To improve the efficiency and precision of the MEM300 granule feeding 3D printer, the motion platform of printer was modelled, and the modal frequency was solved using the ANSYS software. The speed parameters of polarization motor was reasonably set according to the frequency range in modal analysis and the speed of polarization motor measured. Then the professional mechanical simulation software was used to sample the polarization motor speed, the resulting speed parameters were taken as the driving energy, the harmonic response analysis was performed for the entire motion platform. Based on the harmonic response analysis results of mechanical platform, the polarization motors in different speed models are replaced, and the entire mechanical movement was verified. The experiment found that the feeding efficiency of the printer system is not proportional to the rate of polarization motor, and the reasonable velocity is between 38 and 40r/s. When the energy of polarization motor is too high, the vibration in hopper and feeding column will be increased. The method provides a good solution to solve the stability of the mechanical structure.

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Section: Finite Element Analysis Of Modelmentioning

confidence: 99%

Investigation and Analysis of Mechanical Platform Optimization for MEM Type 3D Printer

Huang¹,

Shi²,

Xu³

et al. 2018

dtcse

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“…where σ is flow stress (MPa) for a given strain; Q is the activation energy of hot deformation (kJ·mol -1 ); R is the universal gas constant (8.31 J·mol -1 ·K -1 ); T is temperature (K); ε is strain rate (s -1 ); A is a structure factor; Z is Zener-Hollomon parameter considering the effects of temperature and strain rate 47 .…”

Section: Existing Improved Arrhenius-type Constitutive Model For Az80mentioning

confidence: 99%

“…Mean value (µ) is the average value of all the relative errors of test data, and N is the number of stress-strain samples of test dataset. The formula for µ -value is expressed by Equation 10 30, 43,47 . Standard deviation (w) reflects discrete degree of individual in the dataset.…”

Section: ( )mentioning

confidence: 99%

Modelling the Hot Flow Behaviors of AZ80 Alloy by BP-ANN and the Applications in Accuracy Improvement of Computations

et al. 2015

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Hot compressions of as-cast AZ80 magnesium alloy in a wide temperature range of 523-673 K and strain rate range of 0.01-10 s -1 with a height reduction of 60% were conducted by a Gleeble-1500 thermo-mechanical test simulator. The hot flow behaviors show highly non-linear intrinsic relationships with temperature, strain and strain rate. In order to model the complicated flow behaviors, error back-propagation algorithm, a representative method to minimize the target error, was selected to train the artificial neural network. A comparative study was made on the predictabilities of the improved Arrhenius-type and BP-ANN model by using two standard statistical parameters including correlation coefficient (R) and average absolute relative error (AARE). Comparison results show that the well-trained BP-ANN has higher prediction accuracy. Three highlight applications were presented. Firstly, the strain-stress data volume was expanded by BP-ANN predictions above experimental conditions. Secondly, the expanded data were applied in the simulations of isothermal compressions, and high simulation accuracy for the load-stroke curve was achieved. Thirdly, a three-dimensional (3D) interaction space of stress, strain, temperature and strain rate was constructed based on the intensive data, which supplies the stress data to arbitrary temperature, strain rate, and strain.

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“…Also, the effects of the temperatures and strain rate on the deformation behaviors can be represented by ZenerHollomon parameter, Z, in an exponent-type equation 18 .…”

Section: Constitutive Equation Of the Peak Flow Stressmentioning

confidence: 99%

The improved arrhenius model with variable parameters of flow behavior characterizing for the as-cast AZ80 magnesium alloy

Quan

Shi

et al. 2013

Mat. Res.

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In order to study the hot working behaviour of as-cast AZ80 magnesium alloy, a series of isothermal upsetting tests with height reduction of 60% were performed at the temperature range of 523-673 K and the strain rate range of 0.01-10 s -1 on a servo-hydraulic Gleeble-1500 machine simulator. Based on the regression analysis for Arrhenius type equation of flow stress, the activation energy of deformation was determined to be Q = 168.6606 KJ·mol -1 , and material constants A, n and α in the equation also were calculated respectively to predict the peak stress during hot deformation. A 7 th order polynomial was used to represent the influence of strain on these parameters (i.e Q, lnA, n and α). The Arrhenius equation was further developed by taking account of the effect of strain on the flow stress to describe the flow behavior during the whole experimental temperature and strain rate range. Then comparison between the predicted results and test results shows that the correlation coefficient and average absolute relative error is 0.99 and 6.63%, respectively. The flow stress predicted by developed equation can give an accurate and precise description of the flow behaviour for AZ80 magnesium alloy.

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Research on the dynamic recrystallization behavior of GCr15 steel

Cited by 70 publications

References 9 publications

Investigation and Analysis of Mechanical Platform Optimization for MEM Type 3D Printer

Investigation and Analysis of Mechanical Platform Optimization for MEM Type 3D Printer

Modelling the Hot Flow Behaviors of AZ80 Alloy by BP-ANN and the Applications in Accuracy Improvement of Computations

The improved arrhenius model with variable parameters of flow behavior characterizing for the as-cast AZ80 magnesium alloy

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