In-situ observation of twinning and detwinning in AZ31 alloy

Twinning and detwinning behavior of a commercial AZ31 magnesium alloy during cyclic compression–tension deformation with a total strain amplitude of 4% (±2%) was evaluated using the complementary techniques of in-situ neutron diffraction, identical area electron backscatter diffraction, and transmission electron microscopy. In-situ neutron diffraction demonstrates that the compressive deformation was dominated by twin nucleation, twin growth, and basal slip, while detwinning dominated the unloading of compressive stresses and subsequent tension stage. With increasing number of cycles from one to eight: the volume fraction of twins at -2% strain gradually increased from 26.3% to 43.5%; the residual twins were present after 2% tension stage and their volume fraction increased from zero to 3.7% as well as a significant increase in their number; and the twinning spread from coarse grains to fine grains involving more grains for twinning. The increase in volume fraction and number of residual twins led to a transition from twin nucleation to twin growth, resulting in a decrease in yield strength of compression deformation with increasing cycles. A large number of -component dislocations observed in twins and the detwinned regions were attributed to the dislocation transmutation during the twinning and detwinning. The accumulation of barriers including twin boundaries and various types of dislocations enhanced the interactions of migrating twin boundary with these barriers during twinning and detwinning, which is considered to be the origin for increasing the work hardening rate in cyclic deformation of the AZ31 alloy

Neutron diffraction study of temperature-dependent elasticity of B19′ NiTi---Elinvar effect and elastic softening

The temperature-dependent elasticity of the B19′ NiTi is unknown today. To gain insights into the lattice-level temperature-dependent elasticity of the B19′ crystal, we present results of in-situ neutron diffraction experiments performed on polycrystalline martensitic specimens in the temperature range of 300 down to 50 K. The experimental results are compared with the density functional theory molecular dynamics (DFT-MD) and Quasi Harmonic Approximation (QHA) calculations. The results confirm that the temperature-dependent Young's modulus (TDYM) of the B19′ crystal is strongly anisotropic. For different crystallographic orientations, the change in Young's modulus over the temperature range of 300–50 K (), ranges from  = 2.8 ± 3.5 GPa (extremely weak dependence) to  = 59.6 ± 9.1 GPa (strong dependence). Moreover, it is found that the orientation-specific TDYM and thermal expansion (TE) of the B19′ crystal are correlated. The crystallographic orientations with weak and negative TE responses exhibit a weaker TDYM than the orientations with positive TE. The DFT-MD and QHA results capture qualitatively the above experimental observations and further show that there are orientations in a B19′ crystal exhibiting elastic softening (<0) and ideally no change in Young's modulus (= 0) with cooling. This is found to originate from the strong negative temperature dependence of c35 stiffness constant. The experimental results along with the first-principles calculations confirm that the Elinvar and Invar are two confluent properties in NiTi SMAs and can be tailored by texturing B19′ crystallographic orientations

A Method for Detecting Damage of Traffic Marks by Half Celestial Camera Attached to Cars

Roads are becoming deterioration in everywhere. In some places, traffic marks painted on roads are damaged thus needed to be updated. Municipalities must manage road condition and traffic marks (road painting). It is the municipalities task to manage those roads using, for example, special inspection cars and human eyes. However, the management cost is high if a city contains many roads. This paper proposes a mechanism that automates this management. Our idea is to leverage cameras attached to garbage trucks, which run through the entire city almost everyday. The mechanism collects road images and detects damaged traffic marks using an image recognition algorithm. This paper shows the algorithm and reports the benchmark results. The benchmark showed that the mechanism can detect the damaged traffic marks with 76.6% precision