In order to predict hot deformation behavior of superalloy nimonic 80A, a back-propagational artificial neural network (BP-ANN) and strain-dependent Arrhenius-type model were established based on the experimental data from isothermal compression tests on a Gleeble-3500 thermo-mechanical simulator at temperatures ranging of 1050–1250 °C, strain rates ranging of 0.01–10.0 s⁻¹. A comparison on a BP-ANN model and modified Arrhenius-type constitutive equation has been implemented in terms of statistical parameters, involving mean value of relative (μ), standard deviation (w), correlation coefficient (R) and average absolute relative error (AARE). The μ -value and w -value of the improved Arrhenius-type model are 3.0012% and 2.0533%, respectively, while their values of the BP-ANN model are 0.0714% and 0.2564%, respectively. Meanwhile, the R-value and ARRE-value for the improved Arrhenius-type model are 0.9899 and 3.06%, while their values for the BP-ANN model are 0.9998 and 1.20%. The results indicate that the BP-ANN model can accurately track the experimental data and show a good generalization capability to predict complex flow behavior. Then, a 3D continuous interaction space for temperature, strain rate, strain and stress was constructed based on the expanded data predicted by a well-trained BP-ANN model. The developed 3D continuous space for hot working parameters articulates the intrinsic relationships of superalloy nimonic 80A.

Grain boundary engineering (GBE) is considered to be an attractive approach to microstructure control, which significantly enhances the grain-boundary-related properties of face-centered cubic (FCC) metals. During the twinning-related GBE, the microstructures are characterized as abundant special twin boundaries that sufficiently disrupt the connectivity of the random boundary network. However, controlling the grain boundary character distribution (GBCD) is an extremely difficult issue, as it strongly depends on diverse processing parameters. This article provides a comprehensive review of controlling GBCD during the twinning-related GBE of FCC materials. To commence, this review elaborates on the theory of twinning-related GBE, the microscopic mechanisms used in the optimization of GBCD, and the optimization objectives of GBCD. Aiming to achieve control over the GBCD, the influence of the initial microstructure, thermo-mechanical processing (TMP) routes, and thermal deformation parameters on the twinning-related microstructures and associated evolution mechanisms are discussed thoroughly. Especially, the development of twinning-related kinetics models for predicting the evolution of twin density is highlighted. Furthermore, this review addresses the applications of twinning-related GBE in enhancing the mechanical properties and corrosion resistance of FCC materials. Finally, future prospects in terms of controlling the GBCD during twinning-related GBE are proposed. This study will contribute to optimizing the GBCD and designing GBE routes for better grain-boundary-related properties in terms of FCC materials.

In order to investigate the hot deformation behaviors of as-extruded 7075 aluminum alloy, the isothermal compressive tests were conducted at the temperatures of 573, 623, 673 and 723 K and the strain rates of 0.01, 0.1, 1 and 10 s⁻¹ on a Gleeble 1500 thermo-mechanical simulator. The flow behaviors showing complex characteristics are sensitive to strain, strain rate and temperature. The effects of strain, temperature and strain rate on flow stress were analyzed and dynamic recrystallization (DRX)-type softening characteristics of the flow behaviors with single peak were identified. An artificial neural network (ANN) with back-propagation (BP) algorithm was developed to deal with the complex deformation behavior characteristics based on the experimental data. The performance of ANN model has been evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). A comparative study on Arrhenius-type constitutive equation and ANN model for as-extruded 7075 aluminum alloy was conducted. Finally, the ANN model was successfully applied to the development of processing map and implanted into finite element simulation. The results have sufficiently articulated that the well-trained ANN model with BP algorithm has excellent capability to deal with the complex flow behaviors of as-extruded 7075 aluminum alloy and has great application potentiality in hot deformation processes.

In order to study the dynamic recrystallization (DRX) behavior of as-extruded 42CrMo highstrength steel, a series of isothermal upsetting experiments were carried out in a temperature range of 1123~1348 K and a strain rate range of 0.01~10 s-1 on Gleeble 1500. It was found that DRX softening is more recognizable at higher temperatures and lower strain rates, and stress level increases with increasing strain rate and decreasing deformation temperature. The types of flow stress evolution were distinguished by the following three characteristics: WH followed by DRX, WH followed by DRV, and WH followed by no dynamic softening. At a fixed temperature, the average grain size refined by DRX linearly decreases with increasing strain rate in log scale. At a fixed strain rate, the average grain size remains almost constant below 1273 K, while it rapidly increases above 1273 K. At a larger strain rate, data set of grain sizes has a smaller standard deviation to the average size value, and data have tightly grouped. This indicates that as strain rate increases, the microstructure becomes more and more uniform. The relationships between the average grain size and Zener-Hollomon parameter were nonlinearly fitted by the equation D A = 319.81202-13.6114 lnZ + 0.15322 (lnZ) 2. The results from this equation show that the average grain size decreases with increasing Zener-Hollomon parameter. On the plot of D A versus lnZ, the regions corresponding to DRV (lnZ 37.8) and DRX (lnZ 37.8) were clarified clearly.