Advances in Engineering Research and Application

The trend of increasing data transmission traffic over meter radio channels requires the development of receiving paths that would be capable of operating effectively in a non-stationary electromagnetic environment with a lot of both in-band and out-of-band interferers. This paper discusses technical solution of a preselector designed to work as part of promising VHF transceiver modules. An effective design is proposed, discussed and installed in real devices with details. Experimental results are provided with different coefficients employed to improve the system. The results show promising points for VHF transceivers in various applications.

This article presents a study on the metallographic characteristics of Laser-TIG welded butt joints made from trimetal composite materials, specifically copper alloy - carbon steel - copper alloy, fabricated via powder metallurgy. The analysis encompassed macroscopic examination of the weld’s cross-section and microscopic evaluation of three key regions: base metal (BM), heat affected zone (HAZ), and weld metal (WM), using optical and scanning electron microscopy, along with EDX analysis. The results indicate that the macroscopic structure predominantly developed within the copper alloy layers and partially within the central steel layer when using a laser power range of 1800 ÷ 2000 W. The microscopic structure of the WM region differed notably from the HAZ and BM regions due to the use of alloy steel welding wire.

Currently, Vietnam has approximately 2 million blind people, which is not a small number. Therefore, developing intelligent embedded systems to implement solutions that assist the blind in their daily lives and help them better integrate into society is a topic of great interest to many researchers. One such problem is the recognition of Vietnamese currency using computer vision. Implementing this problem on mobile hardware platforms (with low configurations) requires a deep learning architecture that not only achieves high recognition accuracy but is also small in size. In this paper, we propose a solution to find a deep learning model with very high recognition accuracy (98%) on a dataset of over 9,683 images of Vietnamese currency. This model is very compact in size (512.8 KB). The results of Vietnamese currency recognition using a real camera mounted on a Raspberry Pi 4 also show an accuracy of up to 91.25%. This confirms that our solution can be fully applied to similar problems, such as recognizing objects encountered on the road, recognizing text on packaging, etc., making the system that supports the blind even more complete.

Gears are critical gearbox components known for their high manufacturing and maintenance costs and operation under harsh conditions. Due to the significant role of gearboxes in mechanical systems, early fault detection is crucial to prevent costly downtime and ensure operational efficiency. This research uses a Convolutional Neural Network (CNN) combined with an Autoencoder (AE) for automatic gear fault classification, aiming to develop an advanced diagnostic tool for identifying gear defects through vibration signal analysis. The data was sourced from publicly available datasets and processed using signal processing techniques in both the time and frequency domains. Key features were extracted using the Wavelet Packet Transform and the CNN-AE model training methods. The experimental results indicate that the proposed model accurately classifies gear faults, surpassing traditional diagnostic methods. This study demonstrates the potential of integrating advanced neural network architectures with signal processing techniques to improve the reliability and accuracy of machine condition monitoring systems.

To solve the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox with double gears in the second stage, this work applies the MAIRCA approach. The objective is to determine which critical design elements are most effective in maximizing gearbox efficiency while reducing the gearbox’s bottom area. Three important design factors were chosen for this purpose: the first stage’s gear ratio and the coefficients of wheel face width for the first and second stages. Additionally, the Multi-Criteria Decision Making (MCDM) problem was handled by the MAIRCA strategy, and the weight criterion for solving the MOOP was determined using the MEREC approach. The results of the research help in the building of a two-stage helical gearbox with double gears in the second stage by identifying the most beneficial values for three major design parameters.

This work presents a study on multi-objective optimization of the powder-mixed electrical discharge machining (PMEDM) method for cutting cylindrical parts made of 90CrSi tool steel. The study focused on three specific objectives: achieving the lowest possible surface roughness (SR), minimizing electrode wear rate (EWR), and maximizing material removal rate (MRR). Furthermore, a variety of input parameters have been chosen for the experiment, such as the powder concentration Cp, the pulse-on time Ton, the pulse-off time Toff, the pulse current IP, and the servo voltage SV. Furthermore, the experiment involved the utilization of graphite electrodes and the incorporation of SiC powder into the Diel MS 7000 dielectric solution. In addition, the Taguchi and grey relation analysis (GRA) methodologies were employed for experiment design and outcome analysis. An assessment was conducted to determine the impact of the input elements on the multi-criteria target. In addition, the necessary input parameters to achieve the aforesaid single objective functions simultaneously were provided.

This article presents an air mattress system designed to prevent pressure ulcers, which occur due to prolonged pressure on the skin. The primary objective of the project is to optimize the functionality of the air mattress block in conjunction with the existing pump block, enhancing its effectiveness in pressure ulcer prevention. The research emphasizes key risk factors, such as malnutrition and immobility, and underscores the importance of minimizing pressure on vulnerable areas through frequent repositioning or appropriate padding. By evaluating the outcomes of this innovative approach, we aim to develop an accessible method for preventing pressure ulcers and improving patient outcomes.

With the rapid increase in the application of parallel robots in recent years, delta-type parallel robots have become predominant compared to other parallel robots. Furthermore, due to the demand for expanding the technological scope, new designs have emerged, featuring robots similar to the delta type but with four legs instead of three. In the design process of these robots, multi-objective optimization problems are often solved, where evaluation criteria are based on the Jacobian matrix and pressure transmission angle. These characteristics are usually established through the analytical solution of the robot, as the delta robot series allows for an analytical solution to the inverse kinematics problem. However, in the stage of multi-objective optimal design, this approach leads to the handling of optimization problems with objective functions in functional form. To avoid this, the author proposes describing the aforementioned quality criteria through numerical solutions. Maintaining the mesh survey structure when calculating different criteria is also highly beneficial when employing the Atlas design method for delta robots.

With the advancement of technology, systems that traverse complex terrains without using wheels, known as terrain robots, have been researched and developed. Besides prototyping to evaluate the functionality of these systems, theoretical methods to analyze these mechanisms are also gaining attention. This research employs the closed-loop vector equation for calculations and proposes an analysis method for Klann linkages, a type of walking mechanism. Based on the analysis process, kinematic parameters such as tilting angle, average moving velocity, position, and trajectory of the robot’s legs are determined. The obtained results demonstrate the effectiveness of this method in analyzing other types of structures, reducing calculation time while ensuring accuracy in the calculations.

The technical parameters of the chassis, the resistance to moving, and the materials on a car are related to each other and are represented by the reference parameter which is the mass of the car. Vehicle mass is one of the most important factors affecting the vehicle's resistance to motion. The influence on the value of the mass is the material properties of the manufacturing. The traditional material for the basic body is steel, which is heavier than the light material, and the resistance to motion of the car will be different in these two cases.This paper analyzes relationship between mass and two material types on the body to resistance forces of the car moving by using simulation method. The study results can be applied in designing and manufacturing of the automobile components.

The seat frame is a component of the car seat, consisting of various beams, boxes, and plates made of structural steel. The seat frame transmits forces from the vehicle floor to the seat.Finite element analysis simulations in Ansys Workbench were used to assess the strength, deflection, and maximum stress characteristics of the car seat in two stages. In the first stage, simulations in Matlab Simulink were conducted to determine the forces acting on the seat as a harmonic function. In the second stage, finite element analysis was used to identify the maximum stress and deflection on the seat leg frame in both the initial case and the case equipped with an energy-absorbing cushion.The results of the simulation indicate that when the vehicle travels over bumpy roads with sinusoidal speed bumps, there are sudden increases in high-amplitude impact forces that could damage the seat. According to the simulation results regarding the deformation and stress conditions of the seat components, the location with the highest stress of 318 MPa was identified, exceeding the allowable stress of 270 MPa by 17.8%. After improving the seat frame structure by adding an energy-absorbing cushion at the assembly location with the vehicle floor, the maximum stress was reduced to 231 MPa, thereby ensuring compliance with safety standards.

Corrugated sheets made of metal have been widely utilized so far. Analysis for these structures is significant. In this study, the analysis of natural vibration of sinusoidal corrugated metal plats will be conducted. Instead of determining the natural vibration frequencies directly from the real sinusoidal corrugated plate, the results can be gotten from the equivalent orthogonal plates by analytical method. This is due to the fact that the origin is equivalently considered as a thin plate with uniform thickness and both membrane stiffness and flexural stiffness are transferred to those of equivalent plate. The analytical results obtained by the proposed models will be compared with those is directly received in the real sinusoidal corrugated plate by finite element method (ANSYS). Two boundary conditions considered are simple supported and clamped four edges. The results reveal that the relative errors of natural vibration frequencies between two methods are small. The maximum error percentage of 3.23% occurring at the seventh mode of the simply supported boundary condition. It can be concluded that the utilization of the equivalent orthogonal model of the real plate can reduce the computing time. Moreover, this proposed model can be widely the folded plate in form of trapezoidal shape made metal of composite materials, as well as the static, dynamic problems with various boundary conditions.

This paper introduces a program that utilizes OpenCV and MediaPipe to monitor and guide exercise posture, specifically for pull-ups, while counting repetitions through real-time image analysis. The program captures images via a webcam and identifies key landmarks on the user's body. By calculating body angles and relevant metrics, it assesses posture accuracy and provides corrective feedback when necessary. The system tracks the number of repetitions performed with correct posture and displays on-screen instructions and voice prompts to guide the user through the exercises. This program enables individuals to exercise at home, ensuring proper posture and technique without the need for a fitness trainer.

This study implements the Evaluation by an Area-based Method of Ranking (EAMR) approach for dealing with the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox with double gears in the second stage. The goal is to identify the key design features that are most efficient in optimizing gear-box performance while minimizing the gearbox’s footprint. Three crucial design elements were selected for this purpose: the gear ratio of the first stage and the coefficients of wheel face width for both the first and second stages. In addition, the EAMR strategy was employed to address the Multi-Criteria Decision Making (MCDM) problem, and the MEREC approach was used to identify the weight criterion for solving the MOOP. The research findings allow the building of a two-stage helical gearbox with dual gears in the second stage by determining the optimal values for three key design parameters.

This paper presents the resolution of the Multi-Objective Optimization Problem (MOOP) for a two-stage helical gearbox with double gears in the first stage using a Multi-Criteria Decision Making (MCDM) technique. The objective is to identify the most effective design components that will reduce the length of the gearbox and improve its efficiency. Furthermore, the analysis focused on three essential design parameters: the gear ratio of the initial stage, and the coefficients of wheel face width (CWFW) for the first and second stages. Furthermore, the MEREC technique was utilized to ascertain the weight criteria for resolving the MOOP, whereas the MAIRCA technique was chosen to tackle the MCDM problem. The study's findings are significant for identifying the optimal values for three important design parameters for constructing a two-stage helical gearbox with double gears in the first stage.

This paper presents the findings of a study that utilized Multi-Criteria Decision-Making (MCDM) to identify the most suitable input parameters for manufacturing cylindrical components using Powder-Mixed Electrical Discharge Machining (PMEDM) with SKD11 tool steel. This experiment utilized six input components: powder concentration Cp, powder size Sp, pulse on time Ton, pulse off time Toff, pulse current IP, and servo voltage SV. In addition, the Taguchi technique was adopted for the experimental design. The MAIRCA methodology was implemented to handle the MCDM problem, employing the Entropy method for estimating criterion weights. The objective was to achieve a high material removal speed (MRS) and a low electrode wear rate (EWR). The solution for the MCDM problem in PMEDM cylindrically shaped parts was found based on the findings.

This study demonstrates the use of the MARCOS methodology to solve the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox with double gears in the second stage. The purpose is to find the most effective critical design elements that can enhance gearbox efficiency while minimizing the bottom area of the gearbox. Three critical design parameters were chosen for this task: the gear ratio of the initial stage, and the coefficients of wheel face width (Xba) for both the first and second stages. In addition, the MARCOS approach was selected to address the Multi-Criteria Decision Making (MCDM) task, while the MEREC method was used to determine the weight criterion for solving the MOOP. The study’s findings aid in determining the optimal values for three major design parameters while developing a two-stage helical gearbox with double gears in the second stage.

This work presents a research investigation on multi-criteria decision-making (MCDM) in grinding SKD11 tool steel using a CBN wheel. The Simple Additive Weighting (SAW) technique has been used for addressing the Multiple Criterion Decision Making (MCDM) problem, while the Entropy method was implemented to calculate the weights of the criterion. The challenge was overcome by selecting two criteria: surface roughness (SR) and material removal speed (MRS). In addition, a Taguchi methodology was utilized to carry out an experiment using a design L18 (61 + 33). The analysis focused on four input factors: the dressing depth aed (mm), the wheel speed Rpm (rpm), the feed rate Fe (mm/min), and the wheel diameter dw (mm). The study’s findings were used to determine the most effective experimental setup.

This paper presents the findings of a study that employed Multi-Criteria Decision-Making (MCDM) to find the best input factors for Powder-Mixed Electrical Discharge Machining (PMEDM) cylindrical parts made of 90CrSi tool steel with the use of graphite electrodes. For this study, five input components including powder concentration Cp, pulse on time Ton, pulse off time Toff, pulse current IP, and servo voltage SV were selected. In addition, the Taguchi approach was utilized for the experimental design. In addition, the Simple Additive Weighting (SAW) approach was employed to address the MCDM problem, while the Entropy method was utilized to calculate the criteria weights, aiming to obtain a high material removal speed (MRS) and a low electrode wear rate (EWR). The proposed solution for the MCDM problem in PMEDM cylindrically shaped parts was determined based on the findings.

Artificial intelligence (AI) is becoming a prominent player in various scientific domains and industries. In particular, AI technologies are making inroads in tribology, where they can assist in navigating the intricacy of patterns and uncovering correlations among numerous interacting elements and processes. Extensive research publications encompass a broad spectrum of tribology subjects, such as composite materials, drive technologies, manufacturing, surface engineering, and lubricants. Consequently, the intended applications and computational methods encompass artificial neural networks, decision trees, random forest, and rule-based learners, as well as support vector machines. Therefore, the aim of this investigation is to introduce and assess the current trends and applications of AI in tribology, providing researchers and R&D engineers with support in identifying and choosing suitable and promising approaches and techniques to solve tribological problems.

The paper presents the solution and algorithm for balancing control for human transport robots when moving up and down stairs. The solution for balancing human transport robots through balancing control of the human transport mechanism is the robot’s seat. The balancing control process when the robot moves up and down stairs is performed through controlling the linear actuator combined with sensor signals that determine the robot’s tilt angle. The dynamic model of the robot balancing control system is built based on the solution for balancing with an electric cylinder. The sliding control algorithm with PID sliding surface is built to control the balancing mechanism for the robot. In order to increase the efficiency of the sliding controller, the Sugeno fuzzy controller is used to help calibrate the parameters for the controller. The control algorithm is compared with the basic SMC (Sliding Model Control) algorithm and the PID (Proportional Integral Derivative) controller. The simulation is performed on Matlab Simulink software. The experimental results show the effectiveness of the controller and the constructed solution, and that the FSMC-PID (Fuzzy Sliding Model Control-Proportional Integral Derivative) controller is better than the conventional SMC controller and the PID controller, and the chattering phenomenon in the sliding controller is overcome. The controller response still ensures to follow the reference value when the load and slope change, which shows the robustness of the controller for the robot balancing process.

The paper discusses a research study on Multi-Criteria Decision-Making (MCDM) applied to the internal grinding process of SKD11 tool steel. The objective is to find the most favorable input process parameters for the dressing process in order to minimize surface roughness (SR) and maximize wheel life (Tw). The MAIRCA approach was used to handle the MCDM task, while the Entropy method was used to establish the weights of the criterion. The experiment also involved the examination of six input process parameters: coarse dressing depth, coarse dressing passes, fine dressing depth, fine dressing passes, non-feeding dressing, and dressing feed rate. The experiment was conducted utilizing an L16 orthogonal array and the Taguchi method. The durability of the wheel and the roughness of the surface of the sample were measured and documented for investigation in the MCDM problem. The research findings have shown the most effective dressing options for internal cylindrical grinding.

The tests for pedestrian protection which is using in automotive accreditation are recommended by the European Enhanced Vehicle-Safety Committee (EEVC). According to this method, only three region are evaluated for injury level, including: head, femur and leg region. There are many other important region in pedetrian body such as neck, chest, etc. are not evaluated. A problem is whether it is possible that when testing ensures safety for the head, femur and leg regions, but the danger to pedestrians life comes from injuries to other regions. To solve this problem, this study will perform simulation of car impact to pedestrian full model to analyze and compare injury levels between different regions. Thereby, evaluating whether the testing method proposed by EEVC can evaluate the safety of the entire pedestrian body. The results of this research will contribute to develop the perfect methods of testing for pedestrian protection.

In this work, the conjugate heat transfer during the freezing process of salmon flesh was investigated by CFD simulation method. A mathematical model was developed to model the conductive heat transfer inside of a foodstuff during the freezing process. The latent heat of the liquid water solidification process is considered using the concept of effective heat capacity. The developed model was integrated into the CFD simulation model. The CFD model is validated by comparing numerical observation with the data measured from the freezing process of a single salmon fillet in a laboratory-scale freezing chamber. Afterward, the CFD simulation is performed to describe the freezing process in a large-scale freezer. The numerical results show a remarkable temperature maldistribution inside the chamber resulting in a non-uniform freezing time. It implies that the CFD simulation can be conducted to optimize the design of the large-scale freezer in the future.

Parallel finger grippers are one of the most versatile and common grippers used in the robotics industry. This type of grippers usually consists of two opposing fingers that are capable of moving parallel to each other. To be able to integrate on the robot, the size of the gripper must be compact and the gripping force must be enough to hold the workpiece. The small three finger parallel gripper is a type of clamping solution where the driving link is performed by a pneumatic cylinder.Currently, the three-finger parallel gripper uses a parallel opening and closing mechanism that guides the clamping jaws by a cam mechanism fixed to the piston head of the pneumatic cylinder. The newly designed three-finger parallel gripper is based on the working principle of three-finger parallel grippers with the goal of simple structure and compact size. This research has established kinematic schemes, analyzed the forces acting on links and joints, and built a 3D-CAD model to simulate the dynamics of the new parallel gripper with three fingers.

This paper presents a study on Multi-Criteria Decision-Making (MCDM) in cylindrical external grinding of SKD11 tool steel. The objective is to determine the optimal input process parameters for the dressing process, with the aim of minimizing surface roughness (SR) and maximizing wheel life (Tw). The MAIRCA methodology was utilized to address the MCDM challenge, while the Entropy method was employed to determine the criterion weights. The experiment also included an examination of six input process parameters: dressing feed rate, non-feeding dressing, coarse dressing depth, coarse dressing passes, and fine dressing depth. The experiment was designed using an L16 orthogonal array and the Taguchi method. The Tw and surface roughness of the specimens were quantified and submitted into the MCDM issue. The results of the research have proposed the optimal dressing option for external cylindrical grinding.

Importance of developing power density in high-voltage components at this point of automotive industry progress is at a high level. This tendency as well as tendency to combine multiple components into a common body are a key to achieving competitive advantages of electric vehicles. This scientific paper discovers the main approaches to evaluation of electric motor heat exchangers and provides a review of modern solutions in this field. Development of electric motor designs and components is in its active phase, as evidenced by many publications, patents and a variety of produced electric vehicles. Heat exchangers play an important role in securing reliability and durability of electric motors. Current tendencies in heat exchanger development are aimed at improving cooling efficiency, lowering energy consumption and reducing the device size.

The paper deals with the results of a study that employed Multi-Criteria Decision-Making (MCDM) to find the optimal input parameters to machine cylindrical parts from SKD11 tool steel using Powder-Mixed Electrical Discharge Machining (PMEDM). This experiment included six input components: powder concentration (Cp), powder size (Sp), pulse on time (Ton), pulse off time (Toff), pulse current (IP), and servo voltage (SV). Furthermore, the Taguchi approach was employed for the experimental design. The SAW methodology was employed to tackle the MCDM problem, utilizing the Entropy method for determining the weights of the criteria. The goal was to attain a high rate of material removal (MRS) while minimizing the rate at which the electrode wears rate (EWR). The solution for MCDM problem in PMEDM of cylindrically shaped components was determined using the discovered results.

Hydraulic systems are increasingly utilized in vehicles and industrial applications. The demand for designing and enhancing hydraulic components and systems, as well as for human resource training in this field, necessitates multi-purpose test benches capable of testing and evaluating hydraulic components (e.g., pumps, actuators, valves). This paper presents the design and fabrication of a hydraulic experiment bench with a power of 37 kW, intended for testing and training activities on hydraulic systems. The development of the test bench will be presented with integration of components and operation of the system to observe results (operating parameters of hydraulic flow and components such as: pressure, flow rate, driven power and force/torque). With the supports of the designed test bench, testing activities and research will assist designers in addressing challenges related to the operation and design of hydraulic systems.

The biggest challenge in developing Maximum Power Point Tracking (MPPT) algorithms for photovoltaic systems is accurately locating the global maximum power point (GMPP) under shading conditions. This study proposes an effective two-stage solution to track the GMPP. The first stage uses the photovoltaic module's current equation to identify the potential maximum power voltage (Vmp) region for the GMPP. In the second stage, the system is brought to the operating region containing the GMPP, and then the Perturb and Observe (P&O) algorithm is employed to precisely pinpoint the GMPP value. This method is not only simple and efficient but also highly accurate and achieves faster convergence compared to the adaptive Jaya (Ajaya) and Tent-FPA algorithms under similar testing conditions. The results of the study indicate that the proposed method not only improves GMPP tracking performance but also optimizes the operation of photovoltaic systems under shading conditions, contributing positively to enhancing energy efficiency and reducing operational costs.

We focus on determining the electromechanical properties of monolayer SiS2 material using Density Functional Theory (DFT). Our results reveal that the SiS2 structure exhibits high durability, withstanding mechanical deformation up to 26% along the x-direction and 14% along the y-direction while maintaining balance. Monolayer SiS2 behaves as an indirect semiconductor, possessing a band gap of 1.42 eV. Even under charge doping, it retains its indirect semiconductor properties, with a slight increase (approximately 5.45%) in critical stress. Additionally, we observe a relationship between charge doping and structural parameters: layer thickness correlates directly with charge doping, while lattice constant inversely relates. Notably, within the charge doping range from −0.02 to 0.04 e/atom, the Fermi energy level varies linearly with charge doping. These findings provide valuable insights into the electromechanical properties of the SiS2 monolayer and the impact of charge doping, opening avenues for electronic devices and sensors.

As renewable energy sources and electric car systems are rapidly integrated into the distribution network, it is crucial to guarantee the quality, efficiency, and dependability of the electric power distribution system today. The problem of reconfiguring the power grid with the objective of minimizing losses and optimizing the application of sustainable energy, as well as the charging/discharging processes of electric vehicles, is an essential issue. The Simulated Annealing (SA) and Genetic Algorithm (GA) are two algorithms with different strengths, and their combination can enhance the convergence speed and avoid local optima in optimization problems. Therefore, the research suggests employing a combined approach of the SA and GA algorithms, with parameter enhancements tailored for the reconfiguration of the distribution network, considering renewable energy sources and electric vehicles. The findings were applied and verified on the IEEE 33-bus test platform across different scenarios, affirming the precision and dependability of the method put forward.

The moment fluctuations, unbalanced radial force of electric motors, and road surface disturbances have introduced significant vibrations into electric vehicles. To address this issue, a two-degree-of-freedom dynamic model of distributed drive electric vehicles has been established. A Linear Quadratic Regulator (LQR) control strategy is applied to the active suspension system of the vehicle. Three performance indices, including the weighted root-mean-square (RMS) acceleration responses of body acceleration (BA), suspension working space (SWS), and dynamic tire load (TDL), are selected as the objective function to evaluate the performance of active suspension. Simulation results show that, the RMS values of body acceleration, suspension working space and dynamic tire load of the active suspension is reduced by 16.31%, 32.89%, 3.83% respectively by comparing to the passive suspension. These findings demonstrate the superior performance of the active suspension system compared to the passive suspension system.

Automated warehouses play a crucial role in developing modern logistics systems for moving goods. To increase productivity and effectiveness of warehouse use, stacker cranes need to have large dimensions, but this poses challenges in modelling and controlling. Previous studies often focused on approximating stacker cranes with a simple oscillatory motion, only describing the dynamics of the endpoint of the stacker cranes. This paper proposes the use of the Euler-Bernoulli beam model and applies Hamilton’s principle to construct a mathematical model with partial differential equations, boundary conditions, and constraints. The proposed model allows examining all points along the stacker cranes and is essential for developing control algorithms for stacker cranes. Simulations and comparisons to experimental results have been done to evaluate the accuracy and effectiveness of the proposed model.

In electro-discharge machining with mixed powder, the investigation of electrode wear rate (EWR) has interested many researchers. However, the exploration of EWR when processing different states of the same material is very limited. Furthermore, it is urgent to consider EWR when machining 9CrSi materials in heat-treated (HT) and non-heat-treated (NHT) states. Hence, in this study, the EDM process with tungsten alloy powder suspended in oil solvent was explored in EWR of copper material to process for 9CrSi workpieces in heat-treated and non-heat-treated states. The results indicate that the EWR obtained when machining 9CrSi material in these two states is different.

Polycaprolactone (PCL) is widely used in tissue engineering and regenerative medicine due to its biodegradability and biocompatibility. However, PCL has limitations in terms of mechanical properties and hydrophobicity, leading to challenges in bone tissue engineering applications and low cell adhesion. To enhance the properties of PCL, other materials such as ceramics and hydrogels are mixed with it. Materials in filament form are commonly available only for pure PCL, not for composite materials. To enable the use of PCL composites on conventional FDM 3D printers and expand the application range of these printers with biomaterials, this study focuses on the design and fabrication of a filament generator to directly produce PCL-based composite filament from a powder mixture. The effect of extrusion parameters on filament diameter and morphology was determined. Experiments evaluating the filament’s uniformity and printability were conducted, demonstrating the potential application of the filament generator and composite filament in tissue engineering.

This article presents research findings on the optimal process factors for Electrical Discharge Machining (EDM) of 90CrSi using graphite electrodes. This study employed a Multi-Criteria Decision Making (MCDM) methodology to manufacture cylindrical-shaped parts using the EDM process and different types of graphite as the electrode material. The MCDM problem had been solved utilizing the Simple Additive Weighting (SAW) technique, and the criteria weights were computed employing the Entropy method. Furthermore, the two criteria employed for inquiry were electrode wear rate (EWR) and material removal rate (MRR). Additionally, the study investigated five process factors: servo voltage (SV), servo current (IP), pulse on time (Ton), pulse off time (Toff), and graphite type (TOG). Furthermore, the experiment was formulated and the results were evaluated using the Taguchi method in the Minitab R19 program. In addition, the experiment utilized the L18 (61 + 34) design. The issue of Multiple Criteria Decision Making (MCDM) has been successfully handled, and the best possible process settings have been provided.

This study presents a novel configuration of an electrothermal micromotor optimized for high torque output. The enhanced device, with an overall diameter of 2400 µm, has been successfully designed and fabricated utilizing MEMS technology on a silicon-on-insulator (SOI) platform. A structure of improved driving mechanisms has been added to overcome a fabrication gap of MEMS-based micromotor, which occurs frequently after deep reactive ion etching (DRIE) process. Additionally, a particle swarm optimization (PSO) approach is employed to determine the optimal geometric parameters of the V-shaped beams, thereby maximizing the displacement efficiency of the electrothermal actuator. The micromotor's enhanced configuration is validated through simulation and experimental results, confirming its suitability for high-precision MEMS applications.

Continuous dynamic recrystallization induced by severe plastic deformation (SPD) is a special type of recrystallization that happens under large plastic deformation without the need to heat up the material. This type of recrystallization produces fine-grained microstructures that improve material strength. In this study, the improvement of the mechanical strength which is induced by the continuous dynamic recrystalization phenomenon is examined by using Plastic Flow Machining, a new SPD method, to process Al1050 samples. The results showed that after PFM processing, large plastic deformation was obtained across the produced sheet thickness. The deformation and microstructure showed the gradient characteristic which in favor for balancing strength and ductility. The mechanical property is greatly improved via the significant increase of the Vicker hardness values from 22 HV to 44 ÷ 54 after PFM thanks to considerable grain refinement induced by continuous dynamic recrystallization taking place during PFM processing.

Due to some problems in design or construction, some bridges may not be aerodynamically stable under the effects of wind. Usually, studies focus on installing damping devices in both passive and active control directions to improve the aerodynamic stability of bridge decks. In this paper, another solution is presented, which is to increase the torsional stiffness of the bridge decks. The complex eigenvalue method is used to calculate the critical flutter wind speed corresponding to the change of torsional stiffness. The calculation cases are applied to some famous bridges in the world with reliable data sets. Numerical simulation results show that this is a feasible method, bringing high efficiency to all calculated bridge cases without installing damping devices. In addition, the results also indicate that torsional stiffness plays an important role in the flutter stability. This is also a new research direction that can be carried out in subsequent studies.

Many technological systems, including mobile robots, are being controlled by intelligent and skilled systems that are being created. On the other hand, a quick low-level control method that is frequently employed in control engineering jobs is the PID (Proportional-Integral-Derivative) controller. To determine the gains of PID controllers under certain operating circumstances, many tuning techniques from classical control theory have been used. This paper firstly conducts a thorough survey of modern strategies for adjusting PID parameters using metaheristic algorithms to improve the performance of autonomous robots. The problems in different scenarios of changing the parameters are considered including in specific robot models. The methods are classified and evaluated in details. The most common algorithms are considered to be able to change the parameters GA, PSO, FLC, and others. The methods are analyzed, simulated and evaluated in specific cases. Simulation results on Matlab show that the metaheuristic algorithms help reduce errors and increase the stability of mobile robots compared to other methods. Finally, some proposals and future research directions for the parameter optimization problem of PID will be discussed.

In our paper, mechanical properties and modulation of electro-optical properties of the p-PdS2 monolayer are investigated using density functional theory. The lattice constant results indicated that the p-PdS2 monolayer is an anisotropic material. We also performed two tests to confirm the stability of this structure including phonon dispersions and static constants. Additionally, we found that the p-PdS2 monolayer is quite durable, with a maximum fracture strain of ε = 0.16. Under uniaxial strains along the x-axis and y-axis, we discovered the relationship between the calculated energy band structures. It is a linear function and decreases by approximately 68%. Furthermore, we successfully depicted the optical absorption spectra of the p-PdS2 monolayer. The absorption peak tends to move towards light regions with lower energy due to changes in electronic properties. These results demonstrate the potential applications of the p-PdS2 monolayer in electro-optical devices and highlight the positive impact of mechanical strain.

This study demonstrates the solution of the Multi-Objective Optimization Problem (MOOP) for a two-stage helical gearbox with double gears in the first stage by the application of a Multi-Criteria Decision Making (MCDM) technique. The aim is to determine the optimal design elements that will minimize gearbox length and enhance gearbox efficiency. In addition, the analysis concentrated on three crucial design parameters: the gear ratio of the initial stage, and the coefficients of wheel face width (CWFW) for the first and second stages. In addition, the Multi-Expert Ranking Evaluation with Compensation (MEREC) technique was employed to determine the weight criteria for addressing the Multi-Objective Optimization Problem (MOOP), while the Simple Additive Weighting (SAW) technique was selected to address the Multiple Criteria Decision Making (MCDM) problem. The study’s findings are valuable for determining the most effective values for three critical design parameters when constructing a two-stage helical gearbox with double gears in the first stage.

This paper shows how to solve the Multi-Objective Optimization Problem (MOOP) for a two-stage helical gearbox with double gears in the first stage by using a Multi-Criteria Decision Making (MCDM) technique. The objective is to identify the most beneficial key design components that will reduce gearbox dimensions and increase gearbox efficiency. Furthermore, the analysis focused on three important design parameters: the gear ratio of the first stage, and the coefficients of wheel face width (CWFW) for the first and second stages. Additionally, the Multi-Expert Ranking Evaluation with Compensation (MEREC) technique was used to calculate the weight criterion for solving the MOOP, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique was chosen to handle the MCDM problem. The results of the study are useful in determining the optimal values for three crucial design parameters for building a two-stage helical gearbox with double gears in the first stage.

This work shows the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology in addressing the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox with double gears in the second stage. The purpose is to identify the optimal key design variables that will enhance gearbox efficiency and lower gearbox bottom area. For this work, three important design parameters were selected: the gear ratio of the first stage, and the coefficients of wheel face width (Xba) for both the first and second stages. Furthermore, the TOPSIS technique was chosen to solve the Multi-Criteria Decision Making (MCDM) work and the MEREC method was selected to compute the weight criterion for solving the MOOP. The study’s findings facilitate the determination of the optimal values for three essential design parameters in the development of a two-stage helical gearbox with double gears in the second stage.

This work presents the application of a Multi-Criteria Decision Making (MCDM) technique for solving the Multi-Objective Optimization Problem (MOOP) associated with a two-stage helical gearbox with double gears in the second stage. The objective is to figure out the most useful key design components that will enhance gearbox efficiency and reduce gearbox length. Furthermore, the research particularly evaluated three important design parameters: the gear ratio of the initial stage, as well as the coefficients of wheel face width (CWFW) for both the first and second stages. In addition, the Evaluation by an Area-based Method of Ranking (EAMR) technique was chosen to address the Multi-Criteria Decision Making (MCDM) job, while the Multi-Expert Ranking Evaluation with Compensation (MEREC) technique was used to identify the weight criterion for addressing the Multi-Objective Optimization Problem (MOOP). The study's findings aid in establishing the optimal values for three crucial design parameters while designing a two-stage helical gearbox with double gears in the second stage.

This study presents a non-contact method for estimating human height from a distance of 2 to 3 m during horizontal body rotations, eliminating the reliance on manual or traditional contact-based techniques. The proposed system leverages computer vision, integrating OpenCV and Mediapipe libraries with the YOLOv8 model. Mediapipe is used to detect and annotate key body joints, while YOLOv8 identifies these annotated points. By analyzing the ratio of distances between specific joints and applying a linear regression model, the system accurately estimates a person's height. Experimental results show that the method achieves a low error rate of approximately 1.2%. Future research will aim to generalize this approach to various body positions—such as standing, lying, sitting cross-legged, and bending the legs—as well as accommodate different distances and camera angles, thereby improving its versatility and accuracy.

Recently, the optimal design problem of parallel robots with the improvement of the computing capacity of computer systems has many differences in solving methods. First, although the input data is larger in scale and the content of the multi-objective problem remains unchanged, the aims are to eliminate subjective factors related to the ability to automate design. Among the perspectives that have been applied to solve multi-objective optimal design problems, Multi Criteria Decision Making (MCDM) has not been applied to this field. Unlike the Atlas method, which only accepts the limited input materials with the range of criteria related to the Jacobian matrix, MCDM allows any input type for the optimization problem. This method allows the designers to ensure a second group of very important criteria for parallel robots, which is the transmission pressure angle. This article aims to implement an optimal MCDM design for the 3RRR robot with two groups of optimal criteria: transmission pressure angle and Jacobian matrix. The obtained results claim the potential application on all other parallel robots following the same design process with results that simultaneously meet up to 6 different optimization criteria or more.

One of the typical local structures of the freeform surfaces is the concave umbilical structure. This structure is often used in the design of flashlight reflectors, parabolic mirrors, lampshades, car and motorbike headlights, etc. This paper presents research on surface generation accuracy and machining time when 3-axis CNC milling of surfaces with concave umbilical structure. The experimental model has a concave umbilical structure surface designed based on the scanning of the inner surface of the flashlight reflector. The experiment was conducted according to an orthogonal matrix design Taguchi OA9(33). Analysis of variance (ANOVA) of the experimental results show that when finishing milling a surface with a concave umbilical structure, the type of tool path and feed per tooth have a great influence on the machined surface accuracy, while the influence of the cutting speed is quite small. Meanwhile, the effects of cutting speed, feed per tooth and toolpath type on machining time are quite similar. Gray relational analysis (GRA) combined with Taguchi technique is applied for multi-object optimization to achieve high accuracy of the machined surface and the short machining time at the same time.

As IoT expands, efficient data processing and low-latency service delivery are increasingly important. In fog computing, where resources are distributed at the network edge, optimal microservice placement is essential for resource utilization. Addressing challenges like limited computational power, energy constraints, and security, we introduce the Dynamic Fog Resource Allocation and Optimization (DFRAO) algorithm. DFRAO dynamically optimizes microservice placement by minimizing a multi-faceted cost function that accounts for latency, resource usage, energy consumption, and security. Continuously adapting to real-time conditions, DFRAO ensures QoS while maximizing efficiency. Evaluations against algorithms like GA, ACO, FFD, and PSO show DFRAO’s outstanding performance in reducing latency, optimizing resources, and enhancing energy efficiency. This paper presents DFRAO as a crucial tool for improving scalability, reliability, and overall performance in fog computing, essential for future IoT service delivery.

Over the years, interval-based fuzzy time series forecasting models have garnered significant interest from researchers across various fields. Unlike traditional time series models, the fuzzy time series forecasting models provide flexibility by not imposing stringent assumptions on data characteristics and can be applied to incomplete time series. However, the forecasting accuracy of these models heavily depends on effectively determining the lengths of intervals and output defuzzification rules. Motivated by this challenge, we propose a fuzzy time series forecasting model (FTSFM) based on the index of fuzzy sets, utilizing particle swarm Co-optimization to address these two critical factors. To address the first factor, particle swarm optimization (PSO) is employed to optimize the fuzziness parameters of hedge algebras (HA) and the lengths of intervals in the universe of discourse (UD) of time series data. Additionally, to enhance forecasting accuracy, a novel and more efficient formula for calculating crisp forecasting values based on the index of fuzzy sets is introduced. To evaluate the accuracy of the proposed model, three distinct time series datasets are considered and compared in performance with several recently developed models. The experimental results show that the proposed model achieves better forecasting performance than the comparative models and is more suitable for handling strongly varying data series. Introduce.

Reducing the dynamic responses of structures subjected to environmental loads is always a topic of interest in research and industry. In this study, an improved form of Tuned mass damper (TMD), called Tuned mass-bar damper (TMBD), is studied and applied in passive control of a structure subjected to earthquake loads. Unlike traditional TMD, TMBD consists of a light, non-deformable bar rotating around a point of the bar. The damper and spring elements are located at the two ends of the bar, and the mass of the TMBD is placed at the position of the spring. The parameters of TMBD are optimized based on data from the 1940 El Centro earthquake. Simulation results show that TMBD is effective in reducing the structure's dynamic responses to the El Centro earthquake and other testing earthquakes. Therefore, TMBD can be applied to reduce the vibration of structures subjected to different environmental loads, such as wave or wind loads.

Internal combustion engine (ICE) vibrations not only affect vehicle noise, but also affect vehicle ride comfort. In order to analyze the performance of two types of hydraulic engine mounting system (HEMs) on a passenger car, a full vehicle dynamic model with 11 degrees of freedom is established under the combination of two excitation sources such as the ICE and road surface excitations. The characteristics of HEMs with an inertia channel and a decoupler membrane (HEM 1) were investigated and analyzed for its ride performance compared to HEMs with multi-inertia channel and a decoupler membrane (HEM 2) according to the international standard ISO 2631–1. The achieved results have shown that the values of the objective functions with Type 2 decrease compared to Type 1 under different survey condition. In addition, the research results are the theoretical basis for controlling for multi-inertia channel MRF (magnetorheo-logical fluid) mount.

This article presents the results of research on shaft calibration and vibration during the rotary friction welding process on the VLUTE A01 welding device conducted by a research team at Vinh Long University of Technology Education. The shaft concentricity deviation of the VLUTE A01 friction welding machine is mainly in the vertical direction. The horizontal deviation is low, but the deviation range is quite large. The measured oscillations in different states are studied in the experiment, where the focus is on the origin of the oscillations formed during the welding process. The graph shows the level of vibration that occurs during the rotary friction welding process. When two welded parts begin to contact, the vibration increases and decreases suddenly. In the remaining stages of the welding process, the measured vibration values ​​are quite stable, within the allowable range.

As electric cars have become more prevalent in recent years, the demands of electric cars (EVs) are now essential to the worldwide movement to cut carbon emissions and rely less on fossil fuels. To increase EVs’ efficiency and range, it is crucial to comprehend and optimize their energy use. This research uses Matlab-Simulink software for modeling and simulation to thoroughly examine the factors affecting EV energy usage. Research is conducted to determine how several factors, including vehicle mass, speed, and road gradient, affect the total amount of energy used. The model incorporates elements like the electric motor, battery system, and regenerative braking to replicate actual driving circumstances. In order to assess their effects on energy efficiency, environmental factors such as road surface, vehicle mass, and driving behavior (based on driving cycle) are also considered. The simulation's findings show how various driving situations affect battery discharge rates and energy economy. Sensitivity analysis is used to identify essential elements that substantially impact EV performance. The results of this study are essential for designers and manufacturers that want to improve EV energy management techniques and vehicle performance in various operating environments.

This study investigates the influence of rear wing profile variations on the longitudinal dynamics of a high-performance vehicle. Prior research has established that, at high speeds, the rear wing generates significant downforce while also creating a large aerodynamic drag force. These aerodynamic forces directly impact the vehicle’s longitudinal dynamics, characterized by the maximum traction force that the car can generate. However, for each state regarding position, angle, length, etc., the aerodynamic influence of the rear wing on the vehicle’s dynamics also varies. The vehicle model used for the investigation has a rear wing that can change the angle of attack and wing profile. The results show that when the length of the rear wing changes significantly, either forward or backward, the impact on the vehicle’s dynamics also changes considerably for different angles of attack. The effectiveness of drag force and downforce on the rear axle increases significantly at large angles of attack when the rear wing is extended backward. The study results provide crucial information for controlling and developing an adaptive rear wing that corresponds to the motion states of the vehicle.

Semi-trailer convoys are large, with two-part structures and flexible joints, so the motion when steering is very complex. Lane changing is a common movement process in conditions of high vehicle density. The article establishes a dynamic model of a semi-trailer convoy using the multi-body method and the Newton-Euler equation system combined with the experimental procedure. In the road plane, each structural separation object is described with 3 degrees of freedom: longitudinal, transverse, and vehicle body rotation. The authors used the built-in dynamic model to study the effect of steering frequency on the motion stability of a semi-trailer convoy when changing lanes. The results of the study are used as a basis for optimizing the structure of a semi-trailer.

Waves and sea winds have a negative impact on the stability of the given cruising altitude Hct because they are external noise sources that affect the control channel and the stability of the flight altitude of the autonomous flying device at sea. Most of the time, the autonomous flying device at sea must fly at a small altitude limited from 10 m to 3 m. To eliminate the influence of external noise sources caused by waves and sea winds, minimize instantaneous altitude measurement errors, and stabilize the flight altitude to ensure safety for autonomous flying device at sea with waves and winds below level 7. The article proposes a modern control technique that is to synthesize a PID controller integrated with a radial neural network RBF (PID_RBF) for the control channel - stability of the altitude of the autonomous flying device at sea when flying low above the sea surface. Using the simulation method to evaluate the quality of the controller for the altitude stability channel under the impact of waves and sea winds at wave levels 4–6 and compare it with the reference adaptive controller according to the gradient, PID controller.

The cooling system is instrumental in ensuring the optimal operating temperature of the battery pack in electric vehicles (EVs). This study investigates two cooling system configurations (Model I and Model II), both employing liquid cooling channels to to control the temperature of an 84 cells battery module. The thermal effectiveness of the proposed cooling systems is evaluated based on three key parameters: the battery pack maximum temperature (Tmax), the cell-to-cell temperature difference (Tdiff), and the pressure drop through the cooling channels (Δp). The results indicate that the temperature distribution within the battery pack is highly sensitive to the coolant inlet velocity, the discharge rate of the cells, as well as the geometric configuration of cooling channels. Notably, a channel design with a higher-pressure drop may lead to increased pump power consumption, which can adversely impact the overall system efficiency and battery lifespan. A comparative analysis reveals that, at a constant inlet velocity of 0.1 m/s, Model I achieve reductions in Tmax of 0.9 ℃ and 1.92 ℃ at 3C/5C discharge rates, respectively, compared to Model II. However, these thermal improvements come with the drawback of a higher pressure drop. Overall, this study not only evaluates the impact of critical design and operating parameters on cooling performance but also provides strategies for improving thermal management in batteries to elevate efficiency and ensure dependable operation of EV battery packs.

The energy-saving efficiency and CO2 emission reduction when converting from the traditional buses using the internal combustion engine (ICE-bus) to electric buses (E-Bus) depend on the operating conditions in each area. This paper presents a method to calculate the energy consumption and CO2 emissions for E-Bus and ICE buses operating in Hanoi’s inner-city districts, based on the Parametric Analytical Model of Vehicle Energy Consumption (PAMVEC). Experimental data were collected from E03 bus routes representing the typical operating conditions of Hanoi’s inner-city traffic during peak and off-peak hours. Two calculation scenarios are provided: one using ICE-bus and the other using E-bus. The comparison along Hanoi’s E03 route reveals that the E-Bus transition annually saves 80% of energy consumption and reduces CO2 emissions by 32.4% from vehicle operations.

This study demonstrates the application of a Multi-Criteria Decision Making (MCDM) technique to solve the Multi-Objective Optimization Problem (MOOP) related to a two-stage helical gearbox with double gear sets in the second stage. The goal is to determine the most advantageous critical design elements that will improve gearbox efficiency and minimize gearbox dimensions. In addition, the analysis specifically examined three key design parameters: the gear ratio of the first stage, as well as the coefficients of wheel face width (CWFW) for both the first and second stages. Furthermore, the Multi-Attributive Ideal–Real Comparative Analysis (MAIRCA) technique was chosen to tackle the MCDM task, and the Multi-Expert Ranking Evaluation with Compensation (MEREC) technique was selected to calculate the weight criterion for solving the MOOP. The study’s findings help determine the best values for three important design parameters in creating a two-stage helical gearbox with second-stage double gear sets.