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Among the various applications involving the use of microwave energy, its growing utility within the mining industry is particularly noteworthy. Conventional grinding processes are often overburdened by energy inefficiencies that are directly related to machine wear, pollution and rising project costs. In this work, we numerically investigate the effects of microwave pretreatment through a series of compression tests as a means to help mitigate these energy inefficiencies.

We investigate the effects of microwave pretreatment on various rock samples, as quantified by uniaxial compression tests. In particular, we assign sample heterogeneity based on a Gaussian statistical distribution and invoke a damage model for elemental tensile and compressive stresses based on the maximum tensile stress and the Mohr–Coulomb theories, respectively. We further couple the electromagnetic, thermal and solid displacement relations using finite element modeling.

(1) Increased power intensity during microwave pretreatment results in decreased axial compressive stress. (2) Leveraging statistics to induce variable compressive and tensile strength can greatly facilitate sample heterogeneity and prove necessary for damage modeling. (3) There exists a nonlinear trend to the reduction in smax with increasing power levels, implying an optimum energy output efficiency to create the maximum degradation-power cost relationship.

Previous research in this area has been largely limited to two-dimensional thermo-electric models. The onset of high-performance computing has allowed for the development of high-fidelity, three-dimensional models with coupled equations for electromagnetics, heat transfer and solid mechanics.

This study investigates the impact of three parameters such as: number of LED chips, pitch and LED power on the junction temperature of LEDs using a best heat sink configuration selected according to a lower temperature. This study provides valuable insights into how to design LED arrays with lower junction temperatures.

To determine the best configuration of a heat sink, a numerical study was conducted in Comsol Multiphysics on 10 different configurations. The configuration with the lowest junction temperature was selected for further analysis. The number of LED chips, pitch and LED power were then varied to determine the optimal configuration for this heat sink. A general equation for the average LED temperature as a function of these three factors was derived using Minitab software.

Among 10 configurations of the rectangular heat sink, we deduce that the best configuration corresponds to the first design having 1 mm of width, 0.5 mm of height and 45 mm of length. The average temperature for this design is 50.5 C. For the power of LED equal to 50 W–200 W, the average temperature of this LED drops when the number of LED chips reduces and the pitch size decreases. Indeed, the best array-LED corresponds to 64 LED chips and a pitch size of 0.5 mm. In addition, a generalization equation for average temperature is determined as a function of the number of LED chips, pitch and power of LED which are key factors for reducing the Junction temperature.

The study is original in its focus on three factors that have not been studied together in previous research. A numerical simulation method is used to investigate the impact of the three factors, which is more accurate and reliable than experimental methods. The study considers a wide range of values for the three factors, which allows for a more comprehensive understanding of their impact. It derives a general equation for the average temperature of the LED, which can be used to design LED arrays with desired junction temperatures.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

This study aims to propose an aerodynamic force decomposition which, for the first time, allows for thrust/drag bookkeeping in two-dimensional viscous and unsteady flows. Lamb vector-based far-field methods are used at the scope, and the paper starts with extending recent steady compressible formulas to the unsteady regime.

Exact vortical force formulas are derived considering inertial or non-inertial frames, viscous or inviscid flows, fixed or moving bodies. Numerical applications to a NACA0012 airfoil oscillating in pure plunging motion are illustrated, considering subsonic and transonic flow regimes. The total force accuracy and sensitivity to the control volume size is first analysed, then the axial force is decomposed and results are compared to the inviscid force (thrust) and to the steady force (drag).

Two total axial force decompositions in thrust and drag contributions are proposed, providing satisfactory results. An additional force decomposition is also formulated, which is independent of the arbitrary pole appearing in vortical formulas. Numerical inaccuracies encountered in inertial reference frames are eliminated, and the extended formulation also allows obtaining an accurate force prediction in presence of shock waves.

No thrust/drag bookkeeping methodology was actually available for oscillating airfoils in viscous and compressible flows.

The modeling cost of the gradient-enhanced kriging (GEK) method is prohibitive for high-dimensional problems. This study aims to develop an efficient modeling strategy for the GEK method.

A two-step tuning strategy is proposed for the construction of the GEK model. First, an auxiliary kriging is built efficiently. Then, the hyperparameter of the kriging model is served as a good initial guess to that of the GEK model, and a local optimal search is further used to explore the search space of hyperparameter to guarantee the accuracy of the GEK model. In the construction of the auxiliary kriging, the maximal information coefficient is adopted to estimate the relative magnitude of the hyperparameter, which is used to transform the high-dimension maximum likelihood estimation problem into a one-dimensional optimization. The tuning problem of the auxiliary kriging becomes independent of the dimension. Therefore, the modeling efficiency can be improved significantly.

The performance of the proposed method is studied with analytic problems ranging from 10D to 50D and an 18D aerodynamic airfoil example. It is further compared with two efficient GEK modeling methods. The empirical experiments show that the proposed model can significantly improve the modeling efficiency without sacrificing accuracy compared with other efficient modeling methods.

This paper developed an efficient modeling strategy for GEK and demonstrated the effectiveness of the proposed method in modeling high-dimension problems.

Temperature is a critical factor in the fused filament fabrication (FFF) process, which affects the flow behavior and adhesion of the melted filament and the mechanical properties of the final object. Therefore, modeling and predicting temperature in FFF is crucial for achieving high-quality prints, repeatability, process control and failure prediction. This study aims to investigate the melt deposition and temperature profile in FFF both numerically and experimentally using different Acrylonitrile Butadiene Styrene single-strand specimens. The process parameters, including layer thickness, nozzle temperature and build platform temperature, were varied.

COMSOL Multiphysics software was used to perform numerical simulations of fluid flow and heat transfer for the printed strands. The polymer melt/air interface was tracked using the coupling of continuity equation, equation of motion and the level set equation, and the heat transfer equation was used to simulate the temperature distribution in the deposited strand.

The numerical results show that increasing the nozzle temperature or layer thickness leads to an increase in temperature at points close to the nozzle, but the bed temperature is the main determinant of the overall layer temperature in low-thickness strands. The experimental temperature profile of the deposited strand was measured using an infrared (IR) thermal imager to validate the numerical results. The comparison between simulation and observed temperature at different points showed that the numerical model accurately predicts heat transfer in the three-dimensional (3D) printing of a single-strand under different conditions. Finally, a parametric analysis was performed to investigate the effect of selected parameters on the thermal history of the printed strand.

The numerical results show that increasing the nozzle temperature or layer thickness leads to an increase in temperature at points close to the nozzle, but the bed temperature is the main determinant of the overall layer temperature in low-thickness strands. The experimental temperature profile of the deposited strand was measured using an IR thermal imager to validate the numerical results. The comparison between simulation and observed temperature at different points showed that the numerical model accurately predicts heat transfer in the 3D printing of a single-strand under different conditions. Finally, a parametric analysis was performed to investigate the effect of selected parameters on the thermal history of the printed strand.

This paper aims at building a discharge model for the power cable bellows based on plasma energy deposition and analyzing the discharge ablation problem.

Aiming at the multiphysical mechanism of the discharge ablation process, a multiphysical field model based on plasma energy deposition is established to analyze the discharge characteristics of the power cable bellows. The electrostatic field, plasma characteristics, energy deposition and temperature field are analyzed. The discharge experiment is also carried out for result validation.

The physical mechanism of the bellows ablative effect caused by partial discharge is studied. The results show that the electric field intensity between the aluminum sheath and the buffer layer easily exceeds the pressure resistance value of air breakdown. On the plasma surface of the buffer layer, the electron density is about 4 × 1,019/m³, and the average temperature of electrons is about 3.5 eV. The energy deposition analysis using the Monte Carlo method shows that the electron range in the plasma is very short. The release will complete within 10 nm, and it only takes 0.1 s to increase the maximum temperature of the buffer layer to more than 1,000 K, thus causing various thermal effects.

Its physical process involves the distortion of electric field, formation of plasma, energy deposition of electrons, and abrupt change of temperature field.

The purpose of this paper is to design and simulate a piezoelectric micropump using microelectromechanical systems technology for drug delivery applications.

Two piezoelectric actuators are used to actuate and bend the diaphragms in the proposed structure. In this micropump, the liquid flow is rectified by two silicon check valves.

The use of two piezoelectric transducer (PZT) actuators in the parallel mod not only reduces dead volume but also increases stroke volume as well. In addition to increasing the flow rate, this phenomenon enhances the operation of the micropump to have self-priming as smoothly as possible.

This actuating method results in a 22% increase in flow rate and compression ratio, as well as a 15% reduction in function voltage. The fluid-solid interaction is simulated using COMSOL Multiphysics 5.3a.