Numerical Simulation of Two Types of Electromagnetic Edge Dam Used in Twin Roll Strip Casting

Two types of electromagnetic edge dams were analyzed by using finite element method in present paper. The magnetic vector potential method and edge element method were used. The distributions of the magnetic field, the eddy current intensity and the magnetic force were obtained from the computing. The differences in these fields were explained according to the two types of electromagnetic dam, and characters of their application in twin roll casting were also discussed.

Effect of Heat Treatment on Microstructures and Mechanical Properties of Hot-Dip Galvanized DP Steels

In order to simulate the hot-dipped galvanizing of dual-phase （DP） steel （wt%） 0.15C-0.1Si-1.7Mn, the DP steels were obtained by different annealing schedules. The effects of soaking temperature, time, and cooling rate on ferrite grain, volume fraction of martensite, and the fine structure of martensite were studied. Results showed that the yield strength （YS） of DP steel is sensitive to annealing schedule, while total elongation has no noticeable dependence on annealing schedule. Increasing soaking temperature from 790 to 850 ℃, the YS is the lowest at soaking temperature of 850 ℃. Changing CR1 from 6 to 24 ℃/s, the YS is the highest when CR1 is 12 ℃/s. Increasing soaking time from 30 to 100 s, the YS is the lowest at soaking time of 100 s. Besides, it was found that sufficient movable dislocations within ferrite grains and high martensite volume fraction can eliminate yield point elongation, decrease the YS, and increase ultimate tensile strength. Through TEM observations, it was also found that increasing annealing temperature promotes austenite transformation into twin martensite, and increases volume fraction of martensite at sufficient cooling rate. With increasing the martensite volume fraction, the deformation substructure in the ferrite is well developed.

Microstructure and properties in dissimilar/similar weld joints between DP780 and DP980 steels processed by fiber laser welding

The microstructure and mechanical properties（strength, fatigue and formability） of dissimilar/similar weld joints between DP780 and DP980 steels were studied. The microstructure in fusion zone（FZ） was lath martensite（LM）, and alloying elements in the FZ were uniformly distributed. The hardness in the FZ of dissimilar weld joint was similar to the average value（375 HV） of the two similar weld joints. The microstructural evolution in heat affected zone（HAZ） of dissimilar/similar weld joints was as follows：LM（coarse-grained HAZ） →finer LM（fine-grained HAZ） →M-A constituent and ferrite（intercritically HAZ） →tempered martensite（TM） and ferrite（sub-critical HAZ）. Lower hardness in intercritically HAZ and sub-critical HAZ（softening zones） was observed compared to base metal（BM） in dissimilar/similar weld joints. The size of softening zone was 0.2-0.3 mm and reduction in hardness was ~7.6%-12.7% of BM in all the weld joints, which did not influence the tensile properties of weld joints such that fracture location was in BM. Formability of dissimilar weld joints was inferior compared to similar weld joints because of the softening zone, non-uniform microstructure and hardness on the two sides of FZ. The effect of microstructure on fatigue life was not influenced due to the presence of welding concavity.

Effect of Annealing Temperature on the Precipitation Behavior and Texture Evolution in a Warm-Rolled P-Containing Interstitial-Free High Strength Steel

By using transmission electron microscopy and electron back-scattered diffraction, the effect of annealing temperature on the precipitation behavior and texture evolution in a warm-rolled interstitial-free high strength steel was studied. The results indicated that fine FeTiP could precipitate at 650 ℃, and the number of those precipitates increased greatly with the increasing annealing temperature until 800 ℃. Furthermore, the nucleation of FeTiP was influenced by the precipitation of TiC and （Ti, Nb） C. The near absence of FeTiP and a large volume fraction of TiC and （Ti, Nb） C in matrix are envisaged to be primarily responsible for the sharp y-fiber texture. As the boundary pinning effect caused by FeTiP is weak and there are less interstitial C atoms in matrix. Thus, annealing at 800 ℃ leads to the highest intensity of y-fiber texture.

Orientation-Dependent Characteristics for Residual Grains during Hot Deformation of Nickel-Based Alloy 925

In the present work, the orientation characteristics of residual grains during hot deformation of an age-hardening Ni-Fe-Cr alloy(Alloy 925) at different conditions were systematically analyzed through high-resolution electron back-scatter diffraction. Based on the measurement of the kernel average misorientation, the density of geometrical necessary dislocations(GNDs) was further calculated. The orientation-dependent deformation mechanism of the residual grains was also discussed using Schmid factor difference ratio(SFDR) analysis. The results show that the deformed microstructure features typical "necklace" structures. Many distorted twin boundaries can be observed within the residual grains at 950 ℃. When the deformation temperature increases to 1150 ℃, the volume fraction of dynamic recrystallization(DRX) increases, leading to the extensive formation of primary Σ3 twin boundaries. Additionally, the GNDs are widely distributed in the residual grains, while they are rare for the recrystallized grains. The maximum GND density value can be obtained at the interface of "soft–hard" grains. The GND density also increases at higher strain rates, and the number of DRX grains significantly affects the distribution of the GND density. Moreover, based on the calculations and SFDR analysis, it can be summarized that the {100} and {111} grains are prone to deform in the uniserial slip mode and the multiple slip mode, respectively.

Employing deep learning in non-parametric inverse visualization of elastic-plastic mechanisms in dual-phase steels

Enhancing the interpretability of machine learning methods for predicting material properties is a key,yet complex topic in materials science.This study proposes an interpretable convolutional neural network(CNN)to establish the relationship between the microstructural evolution and mechanical properties of non-uniform and nonlinear multisystem dual-phase steel materials and achieve an inverse analysis of the elastic-plastic mechanism.This study demonstrates that the developed CNN model achieves an accuracy of 94%in predicting the stress-strain curves of dual-phase steel microstructures with different compositions and processes,with the mean absolute error not exceeding 50 MPa,representing merely 5.26%of the average tensile strength of dual-phase steels in the dataset.The reverse visualization results of the CNN model indicate that,during tensile deformation,the grain boundaries maintain deformation coordination within the grains by impeding dislocation slip.This results in a significant stress concentration at the grain boundaries,with stresses at the boundaries being higher than those borne by the martensitic phase and minimal stresses in the ferrite phase.Moreover,compared with traditional crystal plasticity models,the CNN model exhibits a substantial improvement in computational efficiency.This method provides a generic plan for improving the interpretability of machine learning methods for predicting material properties and can be easily applied to other alloy systems.