The mechanical behavior and microstructural evolution of a BCC‐phase NbTaTiV refractory multi‐principal element alloy (RMPEA) is studied over a wide range of strain rates (10 ⁻³ to 10 ³ s ⁻¹ ) and temperatures (room temperature to 850 °C). The mechanical property of present RMPEA shows less strain‐rate dependence and strong resistance to softening at high temperatures. Under high strain‐rate loading, the formation of thin type‐I twins is observed, which could lead to an increase in strain‐hardening rates. However, this hardening mechanism competes with adiabatic heating effects, resulting in the deterrence of strain‐hardening behaviors. In contrast, substantial strain‐hardening occurs at cryogenic temperatures due to the formation of twins, which act as stronger barriers to dislocation motion and interact with each other. To further understand the different strain‐hardening behaviors, density functional theory (DFT) calculations predict relatively low stacking fault energies and high twinning stress for the NbTaTiV RMPEA.

The mechanical behavior and microstructural evolution of a single body-centered-cubic (BCC) phase NbTaTiV refractory high-entropy alloy (RHEA) were studied over a wide range of strain-rates (from 1 × 10-4 to 2 × 103 s-1) and temperatures [from room temperature (RT) to 850 °C]. The evolution of yield strength as a function of strain rate and temperature showed that the present RHEA had less strain-rate dependence and a strong resistance to softening at high temperatures. The formation of thin type-I twins was observed during high-strain rate deformation. The present work also showed that the thin twins did not significantly affect the work-hardening/softening rate during high strain-rate testing at all temperatures. Contrary to the high strain-rate behavior, substantial strain-hardening was observed at quasi-static rates, when the materials were tested at cryogenic temperatures. This difference in behavior was attributed to the formation of “thick” twins at cryogenic temperatures, which not only act as stronger barriers to dislocation motion but also interact with each other leading to an increase in the work hardening rate. The temperature increase during high strain-rate testing was estimated by theoretical calculations. The relatively low stacking fault energies with high twinning stress for NbTaTiV were predicted by density functional theory calculations. Moreover, a modified Zerilli-Armstrong constitutive model was also fit to represent the strength of this material at varying strain rates and temperatures.

Extensive research has been conducted on Ti–Fe–Sn ultrafine eutectic composites due to their high yield strength, compared to conventional microcrystalline alloys. The unique microstructure of ultrafine eutectic composites, which consists of the ultrafine-grained lamella matrix with the formation of primary dendrites, leads to high strength and desirable plasticity. A lamellar structure is known for its high strength with limited plasticity, owing to its interface-strengthening effect. Thus, extensive efforts have been conducted to induce the lamellar structure and control the volume fraction of primary dendrites to enhance plasticity by tailoring the compositions. In this study, however, it was found that not only the volume fraction of primary dendrites but also the morphology of dendrites constitute key factors in inducing excellent ductility. We selected three compositions of Ti–Fe–Sn ultrafine eutectic composites, considering the distinct volume fractions and morphologies of $\beta$-Ti dendrites based on the Ti–Fe–Sn ternary phase diagram. As these compositions approach quasi-peritectic reaction points, the $\alpha$${}^{″}$-Ti martensitic phase forms within the primary $\beta$-Ti dendrites due to under-cooling effects. This pre-formation of the $\alpha$${}^{″}$-Ti martensitic phase effectively governs the growth direction of $\beta$-Ti dendrites, resulting in the development of round-shaped primary dendrites during the quenching process. These microstructural evolutions of $\beta$-Ti dendrites, in turn, lead to an improvement in ductility without a significant compromise in strength. Hence, we propose that fine-tuning the composition to control the primary dendrite morphology can be a highly effective alloy design strategy, enabling the attainment of greater macroscopic plasticity without the typical ductility and strength trade-off.

The dichromatic model has been popularly used for highlight removal. In this paper, we propose a deep learning network which estimates the dichromatic parameters to achieve the highlight removal. Unlike recent works, we utilize not only spatial but also temporal characteristic. The network structure consists of two subnetworks, and the diffuse dictionary and coefficient matrix are generated through each subnetwork. This model is jointly learned by setting 3 loss functions, which split the diffuse and specular components well. To evaluate the performance of highlight removal, we have the diffuse image that highlight is thoroughly removed by calculating the multiplication with the diffuse dictionary and the diffuse weight in a coefficient matrix. This result shows estimating the dichromatic parameters is well implemented in the proposed deep learning method.