Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data

The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld’s locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that they can be estimated from ultrasonic scattered wave data. However, conventional algorithms used for solving this inverse problem incur a significant computational cost. In this paper, we propose a framework which uses deep neural networks (DNNs) to reconstruct crystallographic orientations in a welded material from ultrasonic travel time data, in real-time. Acquiring the large amount of training data required for DNNs experimentally is practically infeasible for this problem, therefore a model based training approach is investigated instead, where a simple and efficient analytical method for modelling ultrasonic wave travel times through given weld geometries is implemented. The proposed method is validated by testing the trained networks on data arising from sophisticated finite element simulations of wave propagation through weld microstructures. The trained deep neural network predicts grain orientations to within 3° and in near real-time (0.04 s), presenting a significant step towards realising real-time, accurate characterisation of weld microstructures from ultrasonic non-destructive measurements. The subsequent improvement in defect imaging is then demonstrated via use of the DNN predicted crystallographic orientations to correct the delay laws on which the total focusing method imaging algorithm is based. An improvement of up to 5.3 dB in the signal-to-noise ratio is achieved.

Pyroelectric properties of 91.5Na0.5Bi0.5TiO3–8.5K0.5Bi0.5TiO3 lead-free single crystal

The dielectric and pyroelectric performances of 91.5Na[Formula: see text]Bi[Formula: see text]TiO3–8.5K[Formula: see text]Bi[Formula: see text]TiO3 lead-free single crystal were investigated. The depolarization temperature of the crystal is about 153[Formula: see text]C. Among the [Formula: see text]001[Formula: see text], [Formula: see text]110[Formula: see text], and [Formula: see text]111[Formula: see text] crystallographic orientations, the [Formula: see text]111[Formula: see text]-oriented crystal possesses the highest pyroelectric coefficient and the largest figures of merit, and the values of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]are 5.63× 10[Formula: see text] C/m2 ⋅ K, 0.06 m2/C, and 21.5 [Formula: see text]Pa[Formula: see text] for the [Formula: see text]111[Formula: see text]-oriented crystal at room temperature. The [Formula: see text]and [Formula: see text]exhibit weak frequency dependence in the range of 100–300 Hz. With the increase of the temperature, the value of [Formula: see text]increases, while the [Formula: see text] value of [Formula: see text] decreases from 18[Formula: see text]C to 103 [Formula: see text]C.

Effects of Geometric and Crystallographic Factors on the Reliability of Al/Si Vertically Cracked Nanofilm/Substrate Systems

In this study, tensile tests on aluminum/silicon vertically cracked nanofilm/substrate systems were performed using atomistic simulations. Various crystallographic orientations and thicknesses of the aluminum nanofilms were considered to analyze the effects of these factors on the reliability of the nanofilm/substrate systems. The results show that systems with some specific crystallographic orientations have lower reliability compared to the other orientations because of the penetration of the vertical crack into the silicon substrate. This penetration phenomenon occurring in a specific model is related to a high coincidence of atomic matching between the interfaces in the model. This high coincidence leads to a tendency of the interface to maintain a coherent form in which the outermost silicon atoms of the substrate that are bonded to the aluminum nanofilm tend to stick with the aluminum atoms under tensile loads. This phenomenon was verified by interface energy calculations in the simulation models.

Microstructures in a shear margin: Jarvis Glacier, Alaska

Microstructures, including crystallographic fabric, within the margin of streaming ice can exert strong control on flow dynamics. To characterize a natural setting, we retrieved three cores, two of which reached bed, from the flank of Jarvis Glacier, eastern Alaska Range, Alaska. The core sites lie ~1 km downstream of the source, with abundant water present in the extracted cores and at the base of the glacier. All cores exhibit dipping layers, a combination of debris bands and bubble-free domains. Grain sizes coarsen on average approaching the lateral margin. Crystallographic orientations are more clustered and with c-axes closer to horizontal nearer the lateral margin. The measured fabric is sufficiently weak to induce little mechanical anisotropy, but the data suggest that despite the challenging conditions of warm ice, abundant water and a short flow distance, many aspects of the microstructure, including measurable crystallographic fabric, evolved in systematic ways.