An Investigation into the Dynamic Recrystallization (DRX) Behavior and Processing Map of 33Cr23Ni8Mn3N Based on an Artificial Neural Network (ANN)

Abstract

Based on an 33Cr23Ni8Mn3N thermal simulation experiment, the application of an artificial neural network (ANN) in thermomechanical processing was studied. Based on the experimental data, a microstructure evolution model and constitutive equation of 33Cr23Ni8Mn3N heat-resistant steel were established. Stress, dynamic recrystallization (DRX) fraction, and DRX grain size were predicted. These models were evaluated by a variety of statistical indicators to determine that these models would work well if applied in predicting microstructure evolution and that they have high precision. Then, based on the weight of the ANN model, the sensitivity of the input parameters was analyzed to achieve an optimized ANN model. Based on the most widely used sensitivity analysis (SA) method (the Garson method), the input parameters were analyzed. The results show that the most important factor for the microstructure of 33Cr23Ni8Mn3N is the strain rate ( ε ˙ ). For the control of the microstructure, the control of the ε ˙ is preferred. ANN was applied to the development of processing map. The feasibility of the ANN processing map on austenitic heat-resistant steel was verified by experiments. The results show that the ANN processing map is basically consistent with processing map based on experimental data. The trained ANN model was implanted into finite element simulation software and tested. The test results show that the ANN model can accurately expand the data volume to achieve high precision simulation results.

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

“…In subsequent iterations of the algorithm, a different set of independent variables is checked for which the neural network is trained. The genetic A common solution is to calculate the relative importance of the input variables [35,47,48]. The coefficient value for each independent variable can be calculated based on the weights of connections of the input and hidden layer neurons.…”

“…In their method, they used artificial neural networks and the ANSYS FLUENT software. Cai et al [47] developed a neural model for calculating the grain size, stress and volume fraction of the recrystallized phase in the heat-resistant alloy steel. The neural model was used to model the thermomechanical treatment process by the finite element method.…”

Microstructure Evolution and a Unified Constitutive Model of Ti-55511 Alloy Compressed at Stepped Strain Rates

“…As previously reported [1,6], DRX is the main deformation characteristic of the β phase in the hot compressed Ti-55511 alloy. Generally, the DRX mechanism can divided into CDRX and DDRX [42,43]. CDRX is related to the formation of subgrains while DDRX is related to the nucleation-growth of strain-free new grains [44,45].…”