Influence of SiC dispersion and Ba(Sr) substitution on the thermoelectric properties of Ca3Co4O9+δ

Ca₃Co₄O_9+δ is a typical p-type thermoelectric oxide material with a low thermal conductivity. In this study, double-layered oxide samples Ca(Ba,Sr)₃Co₄O_9+δ dispersed with different SiC contents were obtained via the traditional solid phase reaction method. The effects of different elemental substitutions and SiC dispersion contents on the microstructure and thermoelectric properties of the samples were studied. The double optimisation of partial substitution of Ca-site atoms and SiC dispersion considerably improved the thermoelectric properties of Ca₃Co₄O_9+δ. Through the elemental substitution, the resistivity of the Ca₃Co₄O_9+δ material was reduced. Conversely, introducing an appropriate amount of SiC nanoparticles enhanced phonon scattering and was crucial in reducing its thermal conductivity. After double optimisations, the dimensionless thermoelectric figure of merit (ZT) values of both Ca_2.93Sr_0.07Co₄O_9+δ + 0.1 wt% SiC and Ca_2.9Ba_0.1Co₄O_9+δ + 0.1 wt% SiC achieved an optimum value of 0.25 at 923 K.     

Growth,mechanical properties,and tribological performance of TiAlN/NbN and NbN/TiAlN bilayer coatings

Three different coatings, namely TiAlN, TiAlN (external)/NbN (internal) and NbN (external)/TiAlN (internal), were deposited on cemented carbides by arc ion plating. The comparative investigation conducted in this study elucidates the effect of the NbN layer and coating systems on the growth, mechanical properties, and tribological performance of the coatings. The results showed that the surface of the TiAlN and TiAlN/NbN coatings was smoother when TiAlN served as the external layer. The NbN/TiAlN coating, wherein NbN formed the external layer, had a much rougher but more symmetrical surface. With the introduction of the NbN layer, the increased micro stress induced a lower adhesion strength in the TiAlN/NbN and NbN/TiAlN coatings. The TiAlN/NbN and NbN/TiAlN coatings exhibited higher hardness and hardness/effective elastic modulus (H/E*). During the friction test, when the temperature was elevated to 700 °C, the tribological performance of the monolayer TiAlN coating was the lowest because of the TiO₂-induced breakage of the dense tribo-oxide film. The NbN layer participated in the formation of a NbO_x film at elevated temperatures, which was responsible for the high tribological performance of the two bilayer coatings. When the NbN layer was on the outermost layer and in direct contact with the elevated temperature atmosphere, the NbN/TiAlN coating generated a tribo-oxide film with high integrity, and its coefficient of friction decreased by 27% of that at room temperature. Therefore, the NbN/TiAlN coating exhibited the highest wear resistance at 700 °C.     

Suppression of forced vibration due to chip segmentation in ultrasonic elliptical vibration cutting of titanium alloy Ti–6Al–4V

Ultrasonic elliptical vibration cutting of titanium alloy Ti–6Al–4V is investigated in this research. Because products made of Ti–6Al–4V alloy are usually designed for possessing low-rigidity structures or good-quality cut surfaces, machining requirements such as low cutting forces and slow rate of tool wear need to be fulfilled for realization of their precision machining. Therefore, the ultrasonic elliptical vibration cutting is applied as a novel machining method for those products. Machinability of Ti–6Al–4V alloy by the ultrasonic elliptical vibration cutting with cemented carbide tools is examined to figure out suitable cutting conditions for precision machining of Ti–6Al–4V alloy. As experimental results, generated chips, cutting forces, and profiles of cut surfaces are indicated. A forced vibration problem occurred due to the segmented chip formation, which is also well-known in the ordinary non-vibration cutting. Therefore, characteristics of the forced vibration due to the chip segmentation are investigated in this research. Through the experiments, it is found that the frequency and magnitude of the forced vibration have relation with the average uncut chip thickness and cutting width. Especially, it is found that the averaging effect can suppress the forced vibration, i.e. the chip segmentation tends to occur randomly over the large cutting width, and hence the force fluctuations with random phases tend to cancel each other as the cutting width increases relatively against the average uncut chip thickness. Based on the investigations, a new practical strategy to suppress the forced vibration due to chip segmentation is proposed and verified. Using the proposed method significantly decreased cutting forces and good quality of surfaces are obtained when the forced vibration is suppressed compared to the ordinary non-vibration cutting results. Therefore, the results suggest that the precision machining can be realized without sacrificing the machining efficiency by increasing the width of cut and decreasing the average uncut chip thickness.     

Comparison of point cloud data and 3D CAD data for on-site dimensional inspection of industrial plant piping systems

Inspection is vital in industrial plant construction and management. However, traditional inspection methods that rely on human involvement and paper documentation are becoming untenable as modern industrial plants are becoming larger and more complex than legacy facilities. Hence, an efficient and robust method is required to support the inspection of modern industrial plants. In this paper, an improved technique relying on terrestrial laser scanning (TLS) for data acquisition and normal-based region growing and efficient random sample consensus (RANSAC) for point cloud data processing is proposed for the on-site dimensional inspection of the piping systems of an industrial plant. Consequently, the as-built condition of the plant is assessed via a distance-based deviation analysis and a comparison of geometric parameters between the as-designed and as-built models. The method is validated using a dataset acquired from a compartment of a ship has verified the robustness and reliability of the proposed approach.     

VO2·0.2H2O nanocuboids anchored onto graphene sheets as the cathode material for ultrahigh capacity aqueous zinc ion batteries

Aqueous Zinc-ion batteries (ZIBs), using zinc negative electrode and aqueous electrolyte, have attracted great attention in energy storage field due to the reliable safety and low-cost. A composite material comprised of VO₂·0.2H₂O nanocuboids anchored on graphene sheets (VOG) is synthesized through a facile and efficient microwave-assisted solvothermal strategy and is used as aqueous ZIBs cathode material. Owing to the synergistic effects between the high conductivity of graphene sheets and the desirable structural features of VO₂·0.2H₂O nanocuboids, the VOG electrode has excellent electronic and ionic transport ability, resulting in superior Zn ions storage performance. The Zn/VOG system delivers ultrahigh specific capacity of 423 mAh·g⁻¹ at 0.25 A·g⁻¹ and exhibits good cycling stability of up to 1,000 cycles at 8 A·g⁻¹ with 87% capacity retention. Systematical structural and elemental characterizations confirm that the interlayer space of VO₂·0.2H₂O nanocuboids can adapt to the reversible Zn ions insertion/extraction. The as-prepared VOG composite is a promising cathode material with remarkable electrochemical performance for low-cost and safe aqueous rechargeable ZIBs.

     

Product appearance detection based on visual keywords matching

The product appearance detection device based on the USB (Universal Serial Bus) camera is studied. The USB camera is used as the front device of the image acquisition and the host computer is a PC for the detection system. Aiming at low resolution of USB camera, the super-resolution image reconstruction method based on sparse representation is used to overcome the low resolution for the low-cost imaging sensor and improve the image resolution. A method of product appearance detection based on visual keywords is proposed. Visual keywords are used to describe characteristics of defects. Visual keyword extraction is performed on sample images and test images, and visual keywords are used for matching. Identify defective products by setting different thresholds. Experiments show that the proposed system has better detection results and offers good robustness to the angle and illumination of the image from the product appearance. In this paper, the theory of visual keywords is applied to product appearance detection for the first time, which provides a reference for further research.     

A multi-material part design framework in additive manufacturing

Additive manufacturing (AM) technologies provide more freedom to functional part design in various industries. One of the unique capabilities of AM is that multi-material parts can be produced with material compositional and geometric complexity. Multi-material parts have the advantage of achieving multiple performance requirements. In the research, we propose a framework for designing multi-material parts using AM processes. The proposed framework is composed of four interacting modules, including design requirement identification, primary material selection, AM process selection, material composition, and part geometry determination. Rules and guidelines for AM are integrated into the proposed framework with AM processes’ capabilities and constraints compiled in databases. We also introduce databases to assist in decision-making and ensure manufacturability of the designed multi-material part in various product design phases. The proposed framework is applied to a case study involving a conceptual design of a multi-material battery pack cooling plate.     

Gas-assisted exfoliation of boron nitride nanosheets enhancing adsorption performance

A gas exfoliation strategy for controllable preparation of boron nitride (BN) nanosheets with few-layered structure were reported. The green exfoliation process provides the BN nanosheets remarkable increment of adsorption capacities to organic contaminants, which is ascribed to better exposure of active sites originating from the larger surface area and thinner layer. Moreover, the prepared BN also exhibits outstanding recyclability.     

Optimal 5G network slicing using machine learning and deep learning concepts

Network slicing is predetermined to hold up the diversity of emerging applications with enhanced performance and flexibility requirements in the way of splitting the physical network into numerous logical networks. Consequently, a tremendous data count has been generated with an enormous number of mobile phones due to these applications. This has made remarkable challenges and has a considerable influence on the network slicing performance. This work aims to design an efficient network slicing using a hybrid learning algorithm. Thus, we proposed a model, which involves three main phases: (a) Data collection, (b) Optimal weighted feature extraction (OWFE), and (c) Slicing classification. First, we collected the 5G network slicing dataset, which involves the attributes associated with various network devices like “user device type, duration, packet loss ratio, packet delay budget, bandwidth, delay rate, speed, jitter, and modulation type.” Next, we performed the OWFE, in which a weight function is multiplied with the attribute values to have high scale variation. We optimized this weight function by the hybridization of two meta-heuristic algorithms—glowworm swarm optimization and deer hunting optimization algorithm (DHOA)—and named the proposed model glowworm swarm-based DHOA (GS-DHOA). For the given attributes, we classified the exact network slices like “eMBB, mMTC, and URLLC” for each device by a hybrid classifier using deep belief and neural networks. The weight function of both networks is optimized by the GS-DHOA. The experiment results revealed that the proposed model could influence the provision of accurate 5G network slicing.