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Wireless sensor networks (WSN), as a solution for buried water pipe monitoring, face a new set of challenges compared to traditional application for above-ground infrastructure monitoring. One of the main challenges for underground WSN deployment is the limited range (less than 3 m) at which reliable wireless underground communication can be achieved using radio signal propagation through the soil. To overcome this challenge, the purpose of this paper is to investigate a new approach for wireless underground communication using acoustic signal propagation along a buried water pipe.

An acoustic communication system was developed based on the requirements of low cost (tens of pounds at most), low power supply capacity (in the order of 1 W-h) and miniature (centimetre scale) size for a wireless communication node. The developed system was further tested along a buried steel pipe in poorly graded SAND and a buried medium density polyethylene (MDPE) pipe in well graded SAND.

With predicted acoustic attenuation of 1.3 dB/m and 2.1 dB/m along the buried steel and MDPE pipes, respectively, reliable acoustic communication is possible up to 17 m for the buried steel pipe and 11 m for the buried MDPE pipe.

Although an important first step, more research is needed to validate the acoustic communication system along a wider water distribution pipe network.

This paper shows the possibility of achieving reliable wireless underground communication along a buried water pipe (especially non-metallic material ones) using low-frequency acoustic propagation along the pipe wall.

This paper aims to present two Anand’s model parameter sets for the multilayer silver–tin (AgSn) transient liquid phase (TLP) foils.

The AgSn TLP test samples are manufactured using pre-defined optimized TLP bonding process parameters. Consequently, tensile and creep tests are conducted at various loading temperatures to generate stress–strain and creep data to accurately determine the elastic properties and two sets of Anand model creep coefficients. The resultant tensile- and creep-based constitutive models are subsequently used in extensive finite element simulations to precisely survey the mechanical response of the AgSn TLP bonds in power electronics due to different thermal loads.

The response of both models is thoroughly addressed in terms of stress–strain relationships, inelastic strain energy densities and equivalent plastic strains. The simulation results revealed that the testing conditions and parameters can significantly influence the values of the fitted Anand coefficients and consequently affect the resultant FEA-computed mechanical response of the TLP bonds. Therefore, this paper suggests that extreme care has to be taken when planning experiments for the estimation of creep parameters of the AgSn TLP joints.

In literature, there is no constitutive modeling data on the AgSn TLP bonds.

The purpose is to explore the free vibration behaviour of elastic foundation-supported porous functionally graded nanoplates using the Rayleigh-Ritz approach. The goal of this study is to gain a better knowledge of the dynamic response of nanoscale structures made of functionally graded materials and porous features. The Rayleigh-Ritz approach is used in this study to generate realistic mathematical models that take elastic foundation support into account. This research can contribute to the design and optimization of advanced nanomaterials with potential applications in engineering and technology by providing insights into the influence of material composition, porosity and foundation support on the vibrational properties of nanoplates.

A systematic methodology is proposed to evaluate the free vibration characteristics of elastic foundation-supported porous functionally graded nanoplates using the Rayleigh-Ritz approach. The study began by developing the mathematical model, adding material properties and establishing governing equations using the Rayleigh-Ritz approach. Numerical approaches to solve the problem are used, using finite element methods. The results are compared to current solutions or experimental data to validate the process. The results are also analysed, keeping the influence of factors on vibration characteristics in mind. The findings are summarized and avenues for future research are suggested, ensuring a robust investigation within the constraints.

The Rayleigh-Ritz technique is used to investigate the free vibration properties of elastic foundation-supported porous functionally graded nanoplates. The findings show that differences in material composition, porosity and foundation support have a significant impact on the vibrational behaviour of nanoplates. The Rayleigh-Ritz approach is good at modelling and predicting these properties. Furthermore, the study emphasizes the possibility of customizing nanoplate qualities to optimize certain vibrational responses, providing useful insights for engineering applications. These findings expand understanding of dynamic behaviours in nanoscale structures, making it easier to build innovative materials with specific features for a wide range of industrial applications.

The novel aspect of this research is the incorporation of elastic foundation support, porous structures and functionally graded materials into the setting of nanoplate free vibrations, utilizing the Rayleigh-Ritz technique. Few research have looked into this complex combo. By tackling complicated interactions, the research pushes boundaries, providing a unique insight into the dynamic behaviour of nanoscale objects. This novel approach allows for a better understanding of the interconnected effects of material composition, porosity and foundation support on free vibrations, paving the way for the development of tailored nanomaterials with specific vibrational properties for advanced engineering and technology applications.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

Polyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process.

PETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles.

PLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one.

The combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding.

This paper aims to study the influence of microparticles on the surface cavitation behavior of 2Cr3WMoV steel; microparticle suspensions of different concentration, particle size, material and shape were prepared based on ultrasonic vibration cavitation experimental device.

2Cr3WMoV steel was taken as the research object for ultrasonic cavitation experiment. The morphology, quantity and distribution of cavitation pits were observed and analyzed by metallographic microscope and scanning electron microscope.

The study findings showed that the surface cavitation process produced pinhole cavitation pits on the surface of 2Cr3WMoV steel. High temperature in the process led to oxidation and carbon precipitation on the material surface, resulting in the “rainbow ring” cavitation morphology. Both the concentration and size of microparticles affected the number of pits on the material surface. When the concentration of microparticles was 1 g/L, the number of pits reached the maximum, and when the size of microparticles was 20 µm, the number of pits reached the minimum. The microparticles of Fe3O4, Al2O3, SiC and SiO2 all increased the number of pits on the surface of 2Cr3WMoV steel. In addition, the distribution of pits of spherical microparticles was more concentrated than that of irregularly shaped microparticles in turbidity.

Most of the current studies have not systematically focused on the effect of each factor of microparticles on the cavitation behavior when they act separately, and the results of the studies are more scattered and varied. At the same time, it has not been found to carry out the study of microparticle cavitation with 2Cr3WMoV steel as the research material, and there is a lack of relevant cavitation morphology and experimental data.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

Construction industry (CI) has great prominence for the world economy, and it is expected that, with the use of the innovative technologies and approaches of Industry 4.0 (I4.0), the new industrial paradigm, construction can reach higher levels of productivity. This study aims to develop a model (readiness model) to assess the level of use of I4.0 technologies by the construction sector in Brazil and its most relevant applications.

The methodology used was bibliographic research, design-science research and a survey to validate the model, carried out with 162 companies, considered among the main ones in the sector in Brazil. The literature review revealed 13 technologies of I4.0 applied to construction; hence, the views of industry experts were based on these technologies.

The Digital Advancement Within CoNstruction (DAWN) readiness model was proposed, showing that among the 13 evaluated technologies of I4.0 and their applications, the Brazilian construction companies had a low level of utilization; both high and middle-income companies presented this low level of use; some technologies with a greater number of scientific publications were less used in practice in the Brazilian construction.

The originality and theoretical contribution are to present a readiness model to assess the level of use of I4.0 technologies and their most relevant applications in the CI in countries with an economy similar to Brazil’s, making it possible to measure the level of adoption of these technologies.

Polyurethane concrete (PUC), as a new type of steel bridge deck paving material, the bond-slip pattern at the interface with the steel plate is not yet clear. In this study, the mechanical properties of the PUC and steel plate interface under the coupled action of temperature, normal force and tangential force were explored through shear tests and numerical simulations. An analytical model for bond-slip at the PUC/steel plate interface and a predictive model for the shear strength of the PUC/steel plate interface were developed.

The new shear test device designed in this paper overcomes the defect that the traditional oblique shear test cannot test the interface shear performance under the condition of fixed normal force. The universal testing machine (UTM) test machine was used to adjust the test temperature conditions. Combined with the results of the bond-slip test, the finite element simulation of the interface is completed by using the COHENSIVE unit to analyze the local stress distribution characteristics of the interface. The use of variance-based uncertainty analysis guaranteed the validity of the simulation.

The shear strength (τ_f) at the PUC-plate interface was negatively correlated with temperature while it was positively correlated with normal stress. The effect of temperature on the shear properties was more significant than that of normal stress. The slip corresponding to the maximum shear (D₁) positively correlates with both temperature and normal stress. The interfacial shear ductility improves with increasing temperature.

Based on the PUC bond-slip measured curves, the relationship between bond stress and slip at different stages was analyzed, and the bond-slip analytical model at different stages was established; the model was defined by key parameters such as elastic ultimate shear stress τ₀, peak stress τ_f and interface fracture energy G_f.

Composite materials have revolutionized the aerospace industry by offering superior structural qualities over traditional elements. This study aims to focus on the development and testing of bamboo natural fiber-based composites enhanced with SiO₂ nanoparticles.

The investigation involved fabricating specimens with varying nanoparticle compositions (0, 10 and 20%) and conducting tensile, flexural, impact and fracture toughness tests. Results indicated significant improvements in mechanical properties with the addition of nanoparticles, particularly at a 10% composition level.

This study underscores the potential of natural fiber composites, highlighting their environmental friendliness, cost-effectiveness and improved structural properties when reinforced with nanoparticles. The findings suggest an optimal ratio for nanoparticle integration, emphasizing the critical role of precise mixing proportions in achieving superior composite performance.

The tensile strength, flexural strength, impact resistance and fracture toughness exhibited notable enhancements compared with the 0 and 20% nanoparticle compositions. The 10% composition showed the most promising outcomes, showcasing increased strength across all parameters.