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 (). For the control of the microstructure, the control of the 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.
Highlights
With the advent of the Internet age, computer technology has continued to evolve, and the artificial neural network (ANN) was born
ANN has been applied in research on the constitutive equation [4,5,6,7] and the prediction of microstructure evolution [8,9] in the thermal deformation of metallic materials
The ANN model is developed in finite element software, and experimental simulation is carried out to verify its feasibility in finite element simulation
Summary
With the advent of the Internet age, computer technology has continued to evolve, and the artificial neural network (ANN) was born. ANN has been applied in research on the constitutive equation [4,5,6,7] and the prediction of microstructure evolution [8,9] in the thermal deformation of metallic materials. Based on the Garson method, this paper will analyze the sensitivity of deformation conditions and determine the importance of different parameters to provide theoretical guidance for 33Cr23Ni8Mn3N heat-resistant alloy steel. A processing map is established based on the experimental data, and the optimal process parameter interval can be given for the specific material to ensure the product quality. Yu et al [29] established the ANN processing map of a Ti40 titanium alloy and verified the corresponding regional microstructure. Applied an ANN processing map to the 7075 aluminum alloy to verify the applicability of the method to aluminum alloys. The ANN model is developed in finite element software, and experimental simulation is carried out to verify its feasibility in finite element simulation
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