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

Abstract

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.

Random Forest Approach in Modeling the Flow Stress of 304 Stainless Steel during Deformation at 700 °C–900 °C

“…Due to the drawbacks of the traditional approach, many researchers have begun to conduct research on predicting the flow stress using a neural network approach [ 11 , 12 , 13 , 14 , 15 , 16 ]. Yan et al [ 12 ] conducted hot compression tests for the Al−6.2Zn−0.70Mg−0.30Mn−0.17Zr alloy at temperatures ranging from 623K–773K and strain rates ranging from 0.01/s–20/s.…”

“…The research showed that the developed model predicted the flow stress of the alloy with good accuracy. Quan et al [ 15 ] also carried out hot compression tests for the AZ80 alloy at temperatures ranging from 523K–673K and strain rates ranging from 0.01/s–10/s. The flow stress was modeled using the Arrhenius-type equation and the BP-ANN (back propagation artificial neural network) model, and it was found that the BP-ANN predicted the flow stress with better accuracy than the Arrhenius-type equation.…”

Effect of Scroll Pin Profile and Tool Rotational Speed on Mechanical Properties of Submerged Friction Stir Processed AZ31B Magnesium Alloy

“…In some industries the use of magnesium alloy is very limited due to its poor workability and limited number of existing slip systems in the hexagonal close-packed structure [1][2][3] . Magnesium alloy has found application in structural components such as aerospace and automobile industries [4][5][6] For example, magnesium parts are used in various vehicles in automotive industries such as Benze, Chrysler Jeep, Renault 18 Turbo and Ford light truck vehicles.…”

Numerical Description of Hot Flow Behaviors at Ti-6Al-2Zr-1Mo-1V Alloy By GA-SVR and Relative Applications

“…Lately, the artificial neural network (ANN) of intelligence algorithm which imitates biological neural systems was applied in modelling the flow behaviors 26 ..Zhu et al and Peng et al respectively constructed ANN models for the flow behaviors of as-cast TC21 titanium alloy 27 and as-cast Ti60 titanium alloy 16 during hot deformation, and the correlation coefficients (R) in their work are about 0.992. The ANN can achieve a high-accuracy level, however, it needs to try a lot of network topologies and training parameters to obtain a higher accuracy, which will consume much time and energy.…”