Artificial Neural Network Modeling to Evaluate and Predict the Deformation Behavior of ZK60 Magnesium Alloy During Hot Compression

Qin, Yinjiang; Pan, Qing‐Jiang; He, Yunbin; Li, W. B.; Liu, X. Y.; Fan, Xiaohui

doi:10.1080/10426910903124894

Cited by 45 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural networks can provide a fundamentally different approach to materials modeling and material processing control techniques than statistical or numerical methods. Recently, some efforts have been made to the applications of a new approach of ANN in the prediction of hot deformation behavior of materials, especially for complex and nonlinear systems [14,[26][27][28][29][30][31][32]. Chai et al [26] developed a back propagation ANN model to predict the flow stress of XC45 steel at elevated temperature.…”

Section: Introductionmentioning

confidence: 99%

A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy

Ashtiani

Shahsavari

2016

Journal of Alloys and Compounds

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy

Ashtiani

Shahsavari

2016

Journal of Alloys and Compounds

View full text Add to dashboard Cite

“…Toros and Ozturk [16] used the ANN modeling to identify the material flow curves of strain hardened 5083-H111 and 5754-O Al-Mg alloys. Qin et al [26] performed ANN modeling to evaluate and predict the deformation behavior of ZK60 magnesium alloy during hot compression. Zhou et al [27] used ANN model by taking extrusion ratio, ram speed, shape complexity, and ram displacement as the input variables and the extrusion load and exit temperature as the output parameters for a specific AZ31B magnesium extrusion alloy shape.…”

Section: Introductionmentioning

confidence: 99%

Extrusion load prediction of gear-like profile for different die geometries using ANN and FEM with experimental verification

Bingöl

Ayer

Altinbalik

2014

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This paper deals with the extrusion of gear-like profiles and uses of finite element method (FEM) and artificial neural network (ANN) to predict the extrusion load. In the study, gear-like components has been manufactured by forward extrusion for the AA1070 aluminum alloy and the process was simulated by using a DEFORM-3D software package to establish a database in order to provide the data for ANN modeling. Serious experiments were performed for only one die set and four teeth gear profile to obtain data for comparing with DEFORM-3D results. After verifying a highly appropriate FEM simulation with the experiment at the same conditions, Results were enhanced for different die lengths, extrusion ratios, and two extra teeth number as three and six using FEM simulations. Subsequently, the data from the performed FEM simulations were submitted for the best obtained ANN model. Finally, a good agreement between FEsimulated and ANN-predicted results was obtained. The proposed ANN model is found to be useful in predicting the forming load of the different die set variations based on the reliable test data.

show abstract

“…In this study, temperature (T), strain (ε), and strain rate (ε) were trained in the BP-ANN model, and two stress-strain curves were reserved to evaluate the prediction accuracy, which increase the difficulties to construct the high accuracy model. O. Sabokpa et al 38 developed an ANN in predicting the hot compressive behavior of cast AZ81 magnesium alloy, and 49 established a BP-ANN with single hidden layer to investigate the flow behavior of ZK60 alloy during hot compression, and the R-value and AARE-value of the predicted data of the BP-ANN are 0.9819 and 3.91% respectively. A smaller AARE-value of 0.32% and a greater R-value of 0.9999 were achieved in this study.…”

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

View full text Add to dashboard Cite

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.

show abstract

Artificial Neural Network Modeling to Evaluate and Predict the Deformation Behavior of ZK60 Magnesium Alloy During Hot Compression

Cited by 45 publications

References 24 publications

A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy

A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy

Extrusion load prediction of gear-like profile for different die geometries using ANN and FEM with experimental verification

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

Contact Info

Product

Resources

About