Forming Nanocrystalline Structures in Metal Particle Impact

Grain refinement by plastic deformation is becoming increasingly popular as a way of producing metals with improved properties, such as higher mechanical strength. Surface treatment techniques in which a metallic substrate is bombarded with metallic particles can generate nanocrystalline layers in the impact zone. Understanding the physical mechanisms underlying this grain refinement is crucial for achieving an improvement of existing experimental processes. In this article, we propose a numerical framework combining finite element (FE) simulations with a dislocation-based material model to predict the evolution of the microstructure under particle impact. A single particle normally impacting on a metallic substrate was simulated at different initial velocities. The simulations were compared with previously reported numerical and experimental data. The results indicate that our model accurately captures the grain refinement in the impact zone for a broad range of velocities. This approach provides valuable information on the formation of nanocrystalline layers in both the substrate and the impacting particle. Its potential applications include processes involving surface treatment by high velocity particles, such as shot peening, surface mechanical attrition treatment, kinetic metallization, cold spray, etc.     

Computational Analysis of Oxide Ion Conduction in Orthorhombic Perovskite Structured La0.9A0.1InO2.95 (A = Ca,Sr and Ba)

Oxide ion conduction in orthorhombic perovskite structured oxides, La_0.9A_0.1InO_2.95 (A = Ca, Sr and Ba) is analyzed using molecular dynamics simulation. Factors influencing oxide ion conductivity of the compositions considered are analyzed using radial distribution function, bond energies between dopant and oxide ions, and the diffusion path. It is known that perovskite oxides with smaller ion size mismatch between host and dopant ions have higher electrical conductivities. However, exceptions exist, such as a La_0.9A_0.1InO_2.95 (A = Ca, Sr and Ba) system, where high electrical conductivities occur with large ion size mismatches. Based on this study, a dopant with smaller ion than host ion results in the formation of strong ionic bonds with oxide ions, suggesting that the A‐site dopant should be larger than the host ion for forming weaker O–A bonds. Consequently, the trade‐off between ion size mismatch and O–A bond needs to be considered for enhancing oxide ion conductivity of perovskite oxides.     

Low‐Temperature Sintering of Bi0.5(Na,K)0.5TiO3 for Multilayer Ceramic Actuators

We investigated the influence of CuO amount (0.5–3.0 mol%), sintering temperature (900°C–1000°C), and sintering time (2–6 h) on the low‐temperature sintering behavior of CuO‐added Bi_0.5(Na_0.78K_0.22)_0.5TiO₃ (BNKT22) ceramics. Normalized strain (S_max/E_max), piezoelectric coefficient (d₃₃), and remanent polarization (P_r) of 1.0 mol% CuO‐added BNKT22 ceramics sintered at 950°C for 4 h was 280 pm/V, 180 pC/N, and 28 μC/cm², respectively. These values are similar to those of pure BNKT22 ceramics sintered at 1150°C. In addition, we investigated the performance of multilayer ceramic actuators made from CuO‐added BNKT22 in acoustic sound speaker devices. A prototype sound speaker device showed similar output sound pressure levels as a Pb(Zr,Ti)O₃‐based device in the frequency range 0.66–20 kHz. This result highlights the feasibility of using low‐cost multilayer ceramic devices made of lead‐free BNKT‐based piezoelectric materials in sound speaker devices.     

Materials and Wireless Microfluidic Systems for Electronics Capable of Chemical Dissolution on Demand

Electronics that are capable of destroying themselves, on demand and in a harmless way, might provide the ultimate form of data security. This paper presents materials and device architectures for triggered destruction of conventional microelectronic systems by means of microfluidic chemical etching of the constituent materials, including silicon, silicon dioxide, and metals (e.g., aluminum). Demonstrations in an array of home‐built metal‐oxide‐semiconductor field‐effect transistors that exploit ultrathin sheets of monocrystalline silicon and in radio‐frequency identification devices illustrate the utility of the approaches.     

High-temperature thermo-mechanical behavior of functionally graded materials produced by plasma sprayed coating: Experimental and modeling results

Thermal barrier coatings are widely used in aerospace industries to protect exterior surfaces from harsh environments. In this study, functionally graded materials (FGMs) were investigated with the aim to optimize their high temperature resistance and strength characteristics. NiCrAlY bond coats were deposited on Inconel-617 superalloy substrate specimens by the low vacuum plasma spraying technique. Functionally graded Ni-yttria-stabilized zirconia (YSZ) coatings with gradually varying amounts of YSZ (20%-100%) were fabricated from composite powders by vacuum plasma spraying. Heat shield performance tests were conducted using a high- temperature plasma torch. The temperature distributions were measured using thermocouples at the interfaces of the FGM layers during the tests. A model for predicting the temperature at the bond coating–substrate interface was established. The temperature distributions simulated using the finite element method agreed well with the experimental results.     

A study on magnetic abrasive deburring of dual micro pattern

Studies on dual micro pattern are not established because of difficulty of its fabrication and deburring technology. In this investigation, a dual micro pattern which consists of a net pattern and a lenticular pattern was fabricated on a cylindrical workpiece by turning process. Magnetic abrasive deburring (MAD) was proposed as a deburring process in this study. Burr height defined by difference of its height and theoretical pattern height was measured as about 1 μm. It is one of the non-traditional machining methods utilizing flexible tool which consists of ferrous particle and abrasive powder. Hence, the aim of this investigation is to remove generated burr on the dual micro pattern. Burr at the dual micro pattern was removed through MAD, and a prediction equation of machined pattern height was derived. A deburring condition was optimized and verified by experiments. As a result, it was confirmed that dual micro pattern which has high shape accuracy was fabricated using MAD.

     

Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures

Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection performance.