Machines, Volume 12, Issue 5 (May 2024) – 68 articles

Super duplex stainless steels (SDSSs) are widely utilized across industries owing to their remarkable mechanical properties and corrosion resistance. However, machining SDSS presents considerable challenges, particularly at high speeds. This study investigates the machinability of SDSS grade SAF 2507 (UNS S32750) under high-speed milling conditions using SiAlON insert tools. Comprehensive analysis of key machinability indicators, including chip compression ratio, chip analysis, shear angle, tool wear, and friction conditions, reveals that lower cutting speeds optimize machining performance, reducing cutting forces and improving chip formation. Finite element analysis (FEA) corroborates the efficacy of lower speeds and moderate feed rates. Furthermore, insights into friction dynamics at the tool–chip interface are offered, alongside strategies for enhancing SDSS machining. This study revealed the critical impact of cutting speed on cutting forces, with a significant reduction in forces at cutting speeds of 950 and 1350 m/min, but a substantial increase at 1750 m/min, particularly when tool wear is severe. Furthermore, the combination of 950 and 1350 m/min cutting speeds with a 0.2 mm/tooth feed rate led to smoother chip surfaces and decreased friction coefficients, thus enhancing machining efficiency. The presence of stick–slip phenomena at 1750 m/min indicated thermoplastic instability. Optimizing machining parameters for super duplex stainless steel necessitates balancing material removal rate and surface integrity, as the latter plays an important role in ensuring long-term performance and reliability in critical applications. Full article

Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training. Full article

Ejector-cleaning devices for abrasive jet machining have various practical applications. The working nozzle is one of the device’s key elements affecting the treated surface quality. There arises the necessity for new approaches to achieving an efficiency increase in abrasive jet equipment nozzles, namely their design improvement and further development of a new, relatively cheap but effective technology for their manufacturing and maintenance. This technology should allow for the high durability of nozzles without being essential for the hardness or wear resistance parameters of the material used for manufacturing. The nozzle should be designed as a long-length perforated insert to allow for radial airflow, forcing the abrasive material (river sand) from the inner walls of the nozzle’s working surface to reduce its friction with the abrasive material. This will result in new wear-out conditions, providing an essential decrease in the wear-out of a nozzle’s working surface. The article aims to develop a more effective design for the working nozzle based on the perforated insert application. The task was set to provide a more detailed experimental and theoretical study of the processes in perforated nozzles to improve their effectiveness. The research resulted in a new design for nozzles with higher efficiency. Full article

Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a good idea about the changes in the dynamic response available between different gear health states. Hence, a catalogue of the responses is currently available, which ought to aid predictions of the health of actual gears by their vibration patterns. However, these analytical models are limited in providing solutions to useful life prediction. This may be because a majority of these models used single fault conditions for modelling and are not valid to predict the remaining life of gears undergoing more than one fault condition. Existing reviews related to gear faults and dynamic modelling can provide an overview of fault modes, methods for modelling and health prediction. However, these reviews are unable to provide the critical similarities and differences in the single-fault dynamic models to ascertain the possibility of developing models under combined fault modes. In this paper, existing analytical models of spur gears are reviewed with their associated challenges to predict the gear health state. Recommendations for establishing more realistic models are made especially in the context of modelling combined faults and their possible impact on gear dynamic response and health prediction. Full article

Despite their complex design, cycloidal gearboxes are characterized by high efficiency. Nevertheless, due to friction, some power is lost during gearbox operation. Basically, these losses occur in two structural nodes: the cycloid gearing and the output mechanism. Since the first of these nodes has been well discussed in the literature, the output mechanism will be discussed in this article. The design of the output mechanism has a significant impact on mechanical power losses. There are several mechanism design solutions. One of them is a mechanism with serially arranged rollers. Three solutions that are different in design but work identically will be discussed. Due to this affinity, a single, common mathematical model will be used to determine the value of losses. As will be shown, the value of losses is directly affected by the backlash, number, and diameter of the rollers used in the output mechanism and indirectly by the ratio and eccentricity of the cycloidal gearbox. Sample calculations were carried out using the developed model of mechanical power losses in the output mechanism. This made it possible to analyze the distribution of backlash created by manufacturing tolerances. It was also shown that the backlash has a significant effect on the number of rollers involved in torque transmission, as well as on the distribution of loads, contact pressures, and mechanical power losses. Full article

Perception-based fidelity evaluation metrics are crucial in driving simulators, as they play a key role in the automatic tuning, assessment, and comparison of motion cueing algorithms. Nevertheless, there is presently no unified and effective evaluation framework for these algorithms. To tackle this challenge, our study initially establishes a model rooted in visual–vestibular interaction and head tilt angle perception systems. We then employ metrics like the Normalized Average Absolute Difference (NAAD), Normalized Pearson Correlation (NPC), and Estimated Delay (ED) to devise an evaluation index system. Furthermore, we use a combined approach incorporating CRITIC and gray relational analysis to ascertain the weights of these indicators. This allows us to consolidate them into a comprehensive evaluation metric that reflects the overall fidelity of motion cueing algorithms. Subjective evaluation experiments validate the reasonableness and efficacy of our proposed Perception Fidelity Evaluation (PFE) method. Full article

To address the problem of problematic spray design inside mining anchor-digging equipment, a switching seal using a permanent magnet eddy current drive is initially presented here. The layer model of the permanent magnet eddy current structure is established, the subdomain analysis model is introduced, the permanent magnet eddy current structure is divided into six regions along the axial direction, and the boundary equations are established at the interfaces of each region. The vector magnetic potential equations in each region are deduced, along with the electromagnetic torque and axial force equations. The computational results are compared and analyzed with the results of finite element simulation, verifying the accuracy of the theoretical model. The design of experiments is used to verify the feasibility of the switching seal using the permanent magnet eddy current structure. Full article

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction. Full article

In the manufacturing sector, tool wear substantially affects product quality and production efficiency. While traditional sequential deep learning models can handle time-series tasks, their neglect of complex temporal relationships in time-series data often leads to errors accumulating in continuous predictions, which reduces their forecasting accuracy for tool wear. For addressing these limitations, the parallel convolutional and recurrent neural networks with attention-modulated residual learning (ParaCRN-AMResNet) model is introduced. Compared with conventional deep learning models, ParaCRN-AMResNet markedly enhances the efficiency and precision of feature extraction from time-series data through its innovative parallel architecture. The model adeptly combines dilated convolution neural network and bidirectional gated recurrent units, effectively addressing distance dependencies and enriching the quantity and dimensions of extracted features. The strength of ParaCRN-AMResNet lies in its refined ability to capture the complex dynamics of time-series data, significantly boosting the model’s accuracy and generalization capability. The model’s efficacy was validated through comprehensive milling experiments and vibration signal analyses, showcasing ParaCRN-AMResNet’s superior performance. In evaluation metrics, the model achieved a MAE of 2.6015, MSE of 15.1921, R² of 0.9897, and MAPE of 2.7997%, conclusively proving its efficiency and accuracy in the precise prediction of tool wear. Full article

Based on a multi-source error model, this paper discusses the principle of error element identification and uses the mirror bias method to compensate the geometric errors of a process system. Firstly, a nine-line measurement method to determine the geometric error of the linear feed axes of machine tools is introduced, and the geometric error identification model based on the “nine-line method” is established. Then, using a ballbar mounted in the axial, tangential, and radial directions of the machine, the geometric error elements of the rotation axis are identified by three simple measurements in each direction. Subsequently, for the more common flat vise clamping workpiece in actual production, the workpiece position error is identified by using the traditional process of dimensional chain, and the workpiece attitude error is identified by fitting the angle between the positioning plane and the horizontal plane by the least squares method. Finally, based on the tool position points and tool axis vectors obtained from the multi-source error model, the error compensation value is solved using inverse machine tool kinematics to offset the machining error by mirroring the error value of the same size, and based on the “S-shaped specimen” to compensate the processing experiments, after compensation, the processing error is reduced by 30~45%, verifying the effectiveness of the compensation method. Full article

The demand for high-power density motors has been increasing due to their remarkable output capability and compact construction. To achieve a significant improvement in motor power density, lightweight design methods have been recognized as an effective enabler. Therefore, extensive investigations have been conducted to reduce motor mass and achieve lightweight configurations through the exploration of lightweight materials, structures and manufacturing techniques. This article provides a comprehensive review and summary of state-of-the-art lightweight implementation methods for electrical machines, including the utilization of lightweight materials, structural lightweight design, and incorporation of advanced manufacturing technologies, such as additive manufacturing techniques. The advantages and limitations of each approach are also discussed in this paper. Furthermore, some comments and forecasts on potential future methodologies for motor lightweighting are also provided. Full article

This study focuses on a marine two-stage tandem hybrid planetary system. Natural frequencies and vibration modes are determined using a translational–torsional coupled dynamic model. Based on the motion characteristics of the transmission system, free vibration is categorized into three typical modes. The parameter sensitivity of natural frequencies is computed, and the effects of structural parameters such as unequally spaced planet, mesh stiffness, planet mass and rotational inertia on the natural frequencies are analyzed. Utilizing the coupling factor, the mode transition criterion for the natural frequencies response to parameters is formulated. The results demonstrate that the vibration modes of the two-stage tandem hybrid planetary system can be classified as the fixed-axis train vibration mode, the differential train vibration mode, and the coupled vibration mode. Unequally spaced planet primarily disrupts vibration modes without significantly affecting natural frequencies. In contrast, the effects of mesh stiffness, planet mass and rotational inertia on the modes are opposite to those of unequally spaced planets. The validity of the parameter sensitivity and mode transition criterion is substantiated through illustrative examples. Full article

This article deals with the consensus tracking problem for multi-agent systems (MAS) under the influence of unknown time-varying delays. Each agent of the MAS is a quadrotor unmanned aerial vehicle (UAV) represented as a linear continuous-time system. The main objective of this paper is the stabilization of multi-agent systems where the control input is affected by unknown time-varying delays, which are assumed to be upper-bounded, and where these bounds are not required to be known. The proposed observer-based control scheme guarantees the consensus tracking of multi-UAV systems with the desired $H_{\infty }$ performance, which adds a further level of mitigation of unknown delays present in MAS systems by minimizing the $H_{\infty }$ norm, which measures the maximum gain from the disturbance to the controlled output of the system. For each UAV agent, an unknown input observer is employed to isolate the unknown time-varying delays in the state estimation process. With the use of an unknown input observer-based consensus tracking control, sufficient conditions are derived to ensure that all follower UAVs can reach a consensus with the leader, despite the presence of distinct unknown time-varying delays. The stability of the proposed scheme is proven using Lyapunov theory for the leader and follower agents. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method. Full article

Incorporating continuous carbon fibre-reinforced polymer (CCFRP) parts within additive manufacturing processes presents a significant advancement in the fabrication of robust lightweight parts, particularly relevant to aerospace, engineering, and various industrial sectors. Nonetheless, prevailing additive manufacturing methodologies for CCFRP parts exhibit notable limitations. Techniques reliant on resin and extrusion entail extensive and costly post-processing procedures to eliminate support structures, constraining design versatility and complicating small-scale production endeavours. In contrast, laser sintering (LS) emerges as a promising avenue for industrial application. It facilitates the efficient and cost-effective manufacturing of resilient parts without needing support structures. However, the current state of research and technological capabilities has yet to yield an LS machine that integrates the benefits of continuous fibre reinforcement with the inherent advantages of the LS process. This paper describes the systematic development process according to VDI 2221 of a new type of LS machine with automated continuous fibre integration while keeping the advantages of the LS process. The resulting physical prototype of the machine is also presented. Furthermore, this study presents an approach to integrate the cost and Product Carbon Footprint of the process in the product design. For this purpose, a machine state model was developed, and the costs and Product Carbon footprint of a part were analysed based on the model. The promising potential for future lightweight products is demonstrated through the production of CCFRP parts. Full article

Inconel 718’s exceptional strength and corrosion resistance make it a versatile superalloy widely adopted in diverse industries, attesting to its reliability. Electrochemical machining (ECM) further enhances its suitability for intricate part fabrication, ensuring complex shapes, dimensional accuracy, stress-free results, and minimal thermal damage. Thus, this research endeavors to conduct a novel investigation into the electrochemical machining (ECM) of the superalloy Inconel 718. The study focuses on unraveling the intricate influence of key input process parameters—namely, electrolytic concentration, tool feed rate, and voltage—on critical response variables such as surface roughness (SR), material removal rate (MRR), and radial overcut (RO) in the machining process. The powerful tool, response surface methodology (RSM), is used for understanding and optimizing complex systems by developing mathematical models that describe the relationships between input and response variables. Under a 95% confidence level, analysis of variance (ANOVA) suggests that electrolyte concentration, voltage, and tool feed rate are the most important factors influencing the response characteristics. Moreover, the incorporation of ANN modeling and the MOGA-ANN optimization algorithm introduces a novel and comprehensive approach to determining the optimal machining parameters. It considers multiple objectives simultaneously, considering the trade-offs between them, and provides a set of solutions that achieve the desired balance between MRR, SR, and RO. Confirmation experiments are carried out, and the absolute percentage errors between experimental and optimized values are assessed. The detailed surface topography and elemental mapping were performed using a scanning electron microscope (SEM). The nano/micro particles of Inconel 718 metal powder, obtained from ECM sludge/cakes, along with the released hydrogen byproducts, offer promising opportunities for recycling and various applications. These materials can be effectively utilized in powder metallurgy products, leading to enhanced cost efficiency. Full article

Railway signals’ fault text data contain a substantial amount of expert maintenance experience. Extracting valuable information from these fault text data can enhance the efficiency of fault diagnosis for signal equipment, thereby contributing to the advancement of intelligent railway operations and maintenance technology. Considering that the characteristics of different signal equipment in actual operation can easily lead to a lack of fault data, a fault diagnosis method for railway signal equipment based on data augmentation and an improved attention mechanism (DEIAM) is proposed in this paper. Firstly, the original fault dataset is preprocessed based on data augmentation technology and retained noun and verb operations. Then, the neural network is constructed by integrating a bidirectional long short-term memory (BiLSTM) model with an attention mechanism and a convolutional neural network (CNN) model enhanced with a channel attention mechanism. The DEIAM method can more effectively capture the important text features and sequence features in fault text data, thereby facilitating the diagnosis and classification of such data. Consequently, it enhances onsite fault maintenance experience by providing more precise insights. An empirical study was conducted on a 10-year fault dataset of signal equipment produced by a railway bureau. The experimental results demonstrate that in comparison with the benchmark model, the DEIAM model exhibits enhanced performance in terms of accuracy, precision, recall, and F1. Full article

Excessive pressure drop within the internal flow channel of the water hydraulic throttle valve will generate severe noise. In order to reduce the noise of the throttle valve, in this paper, the model of the throttle valve was established, and the flow characteristics and acoustic characteristics of the valve were simulated. The simulation results showed that the parameters of the throat structure, such as the half angle, throat inlet angle and throat length, have a significant effect on the noise of the valve. Then, the three main structural parameters were used as optimization variables, and radial basis function (RBF) neural networks and multi-island genetic algorithms (MIGA) were used to reduce the noise of the valve. The approximate model of the relationship between the structural parameters of the valve and noise was established by RBF neural networks, and MIGA was used to optimize the approximate model. Finally, the optimal valve model was established based on the obtained optimal parameters, and its noise was analyzed through simulation and experiment. The research results indicated that the optimization method, which combines RBF Neural Network and MIGA, can effectively reduce the noise of hydraulic throttle valves. Full article

Failure mode and effects analysis (FMEA) helps to identify the weak points in the processing, manufacturing, and assembly of products and plays an important role in improving product reliability. To address the shortcomings of the existing FMEA methods in terms of the uncertainty treatment of information and not considering the weights and correlations between risk factors, we propose a new FMEA method. In this paper, the Fermatean fuzzy Z-number (FFZN) is proposed by fusing the Fermatean fuzzy number and Z-number. Extending it to the Bonferroni mean (BM) operator, the Fermatean fuzzy Z-number-weighted Bonferroni mean (FFZWBM) operator is proposed. A new FMEA method is proposed based on this operator. In order to overcome the factors not considered in the FMEA method, two new risk factors are proposed and added. The ability of experts to express fuzzy information is enhanced by introducing the FFS. The weights and correlations between the influencing factors can be handled by aggregating the evaluation information using the FFZWBM operator. Finally, the proposed method is applied to an arithmetic example and the accuracy of the proposed method is proved by teaming it with other methods. Full article

Addressing the current issue of limited control methods for badminton serving devices, this paper proposes a vision-based multimodal control system and method for badminton serving. The system integrates computer vision recognition technology with traditional control methods for badminton serving devices. By installing vision capture devices on the serving device, the system identifies various human body postures. Based on the content of posture information, corresponding control signals are sent to adjust parameters such as launch angle and speed, enabling multiple modes of serving. Firstly, the hardware design for the badminton serving device is presented, including the design of the actuator module through 3D modeling. Simultaneously, an embedded development board circuit is designed to meet the requirements of multimodal control. Secondly, in the aspect of visual perception for human body recognition, an improved BlazePose candidate region posture recognition algorithm is proposed based on existing posture recognition algorithms. Furthermore, mappings between posture information and hand information are established to facilitate parameter conversion for the serving device under different postures. Finally, extensive experiments validate the feasibility and stability of the developed system and method. Full article

The reciprocating plate column is a kind of column with the plates driven by a geared motor, and it has advantages in regard to efficiency compared to traditional columns in the extraction process, however, it comes with an increase in energy consumption. A new type of reciprocating plate column driven by electro-permanent magnet technology (EPM) is proposed in this paper to obtain a better performance with lower energy consumption. The feasibility and performance of the proposed column is studied by numerical simulation and experiments with a kerosene–water system. The electro-permanent magnet chuck could provide a maximum amplitude of 12 mm in this study. Kerosene was used as the dispersed phase, and deionized water was used as the continuous phase, in a laboratory-scale 35 mm diameter reciprocating plate column driven by EPM. Hydrodynamic performance experiments were carried out with different flowrates of both phases and reciprocating frequencies. The experimental results show that the electro-permanent magnet chuck, which serves as the driving device of the reciprocating plate column, plays the role of adding energy and increasing the droplet breakage. In addition, the energy consumption of the reciprocating plate column with traditional geared motor and electro-permanent magnet chuck is calculated respectively. Compared with the traditional geared motor, the energy saving of the electro-permanent magnet chuck is as high as 98.55%. Full article

The electro-hydraulic servo system has advantages such as high pressure, large flow, and high power, etc., which can also realize stepless regulation, so it is widely used in many engineering machineries. A linear model is sometimes only a simple approximation of an idealized model, but in an actual system, there may be nonlinear and transient variation characteristics in the systems. Coupling is reflected in the fact that the components or functional structures implemented by each system used for the design of hydraulic systems are not completely or independently related to each other, but affect each other. The nonlinear clearance between the actuator and the load reduces the control accuracy of the system and increases the impact, thus losing stable working conditions. In the paper, based on the nonlinear clearance problem of the electro-hydraulic servo system, a mathematical transfer model with clearance is established, and on this basis, a clearance compensation method based on the Hopfield neural network is proposed. In this way, clearance compensation can be realized by adjusting the parameters of neural network nodes, through simple and convenient operation. Finally, by setting different clearance values, the results of the step response and sine response curve before and after clearance compensation of the hydraulic system are compared, and the effectiveness of Hopfield neural network compensation clearance control is verified based on the comparison simulation results. Full article

Worms can be produced using special machines or standard lathes equipped with a whirling thread-cutting device. A blank is placed on the mandrel and tightened using the three-jawed chuck of the standard lathe. If the workpiece diameter is excessively large, passage through the driven pulley is not possible, and the workpiece cannot be supported. Therefore, a new tool holder for whirling devices is needed. During the whirling process, vibrations in the form of machine velocity amplitudes were measured. After whirling was complete, roughness values were calculated. Using numerical procedures of Wolfram Mathematica 10, vibration peaks were extracted, from which frequencies and maximum amplitudes were determined. The data were then inputted into Design Expert, and the rotational speed and amount of separated material were optimized. The results of the study showed that the quality of the processed surface did not improve with processing in two passes of the tool. The measured vibration amplitudes on the lathe carrier and thread whirling attachment increased with cutting speed at the same cutting depth, whereas the quality of the machined surface was best at the smallest and largest cutting depths. Full article

In this article, rotor designs utilizing assisted-poles are investigated for a high-torque density spoke-type permanent magnet synchronous machine (PMSM) with fractional slot concentrated winding (FSCW) to explore the rich air-gap magnetic field harmonics and torque generation mechanism. Due to their higher average torque output, spoke-type PMSMs with FSCW are increasingly used in high-torque density applications. However, slot harmonics generate torque ripples that are difficult to eliminate in FSCW spoke-type PMSMs. Removing slot harmonics from the stator or winding results in a large drop in torque since their winding factors are identical to those of the main harmonic. Therefore, rotor designs having assisted-poles (symmetrical and asymmetrical) are investigated in this work to mitigate slot harmonics and minimize torque ripples. Firstly, the air-gap flux density is analyzed for the machines having assisted-poles, and a model of interaction between the stator and rotor-MMF harmonics is created and validated through Finite element analysis (FEA) to analyze the torque production mechanism. In addition, an analytical relationship between the assisted-poles’ dimensions and the generated torque harmonics is proposed. Furthermore, a generalized torque ripple reduction concept for the FSCW spoke-type PMSM having asymmetrically designed assisted-poles is presented. The proposed design and optimization method are validated through analytical calculations and FEA simulations, and a brief comparative analysis is presented for the analyzed machine prototypes. It has been established that the machine designed by applying the proposed asymmetrical assisted-poles can achieve a reduction in torque ripples while also significantly lowering cogging torque in comparison to the conventional spoke-type PMSMs and other spoke-type PMSMs with rotor having symmetrical assisted-poles. Full article

With the continuous growth of energy demand, the advantages of nuclear power, such as high energy density, low emissions, and cleanliness, are gradually highlighted. However, the increasing capacity of the turbine generator in nuclear power plants has led to greater losses and critical heating issues. Designing an effective cooling system plays an important role in improving the rotor’s heat dissipation ability, especially under the condition of limited rotor space. In this study, the cooling effects of the rotor using a radial straight-type cooling structure and a composited radial–axial–radial cooling structure are compared and analyzed for a 1555 MVA hydrogen-cooled nuclear turbine generator. Three-dimensional fluid thermal coupled models of the rotor with both cooling structures are established, and corresponding boundary conditions are provided. The models are solved using the finite volume method. The flow law of cooling hydrogen gas inside the rotor and the temperature distribution of various parts of the rotor are studied in detail. Compared with the radial straight-type cooling structure, adopting the composited radial–axial–radial cooling structure can reduce the average temperature of the rotor field windings by 4.5 °C. The research results provide a reference for the design and optimization of the rotor cooling system for large-capacity nuclear turbine generators. Full article

In this work, a modified numerical algorithm that couples the quasi-static theory with the mixed thermal elastohydrodynamic lubrication (mixed-TEHL) model is proposed to examine the mechanical properties and lubrication performance of the spindle bearing that is used in a high-speed machine tool with spinning. The non-Newtonian fluid characteristics of the lubricant and the non-Gaussian surface roughness are also considered. Moreover, the mechanical properties and lubrication state of the bearing are examined in various service environments. The results indicate that the temperature reduces the lubrication efficiency, which in turn exerts a significant impact on the mechanical properties. The lubrication that either behaves in the manner of Newtonian or non-Newtonian fluid has a relatively negligible influence on the bearing working state, while the non-Gaussian surface roughness significantly alters the oil film thickness and temperature. Calculations with different operating conditions demonstrate that the operating parameters (i.e., axial load, rotation speed) will directly affect the performance of the bearings via the changes in the oil film thickness and the temperature. Full article

The number of battery systems is also growing significantly along with the rise in electric and hybrid car sales. Different vehicles use different types and numbers of batteries. Furthermore, the layout and operation of the control and protection electronics units may also differ. The research aims to develop an approach that can autonomously detect and localize the weakest cells. The method was validated by testing the battery systems of three different VW e-Golf electric vehicles. A wide-range discharge test was performed to examine the condition assessment and select the appropriate state of charge (SoC) for all three vehicles. On the one hand, the analysis investigated the cell voltage deviations from the average; the tests cover deviations of 0 mV, 12 mV, 60 mV, 120 mV, and 240 mV. On the other hand, the mean value calculation was used to filter out possible erroneous values. Another important aspect was examining the relationship between the state of charges (SoC) and the deviations. Therefore, the 10% step changes were tested to see which SoC level exhibited more significant voltage deviations. Based on the results, it was observed that there are differences between the cases, and the critical range is not necessarily at the lowest SoC level. Furthermore, the load rate (current) and time of its occurrence play an important role in the search for a faulty cell. An additional advantage of this approach is that the process currently being tested on the VW e-Golf can be relatively simply transferred to other types of vehicles. It can also be a very useful addition for autonomous vehicles, as it can self-test the cells in the system at low power consumption. Full article

Unmanned ground vehicles (UGVs) have gained increased attention in different fields of application; therefore, their optimization requires special attention. Lowering the mass of a UGV is especially important to increase its autonomy, agility, and payload capacity and to reduce dynamic forces. This contribution deals with optimizing a UGV unit prototype that, when connected with similar units, forms a moving electric fence for animal grazing. Together, these units form a robotic system that is intended to solve the critical problem of lack of human capacity in herding and grazing. This approach employs topology optimization (TO) and finite element analysis (FEA) to lower the mass of a UGV unit and validate the design of its structural components. To our knowledge, no optimization of this type of UGV has been reported in the literature. Here, we present the results of a case study in which a set of four load cases served as a basis for the optimization of the UGV frame. Response surface analysis (RSA) was used to identify the worst load cases, while substructuring was used to allow for more detailed meshing of the frame portion that was subjected to TO. Thereby, we demonstrate that the prototype of the UGV unit can be built using standard parts and that TO and FEA can be efficiently used to optimize the load-carrying structure of such a specific vehicle. Full article

In the present work, based on an approximate modelling of a rover-type robot and a proportional control law, a simplified trajectory tracking strategy for a passive suspension rover-type mobile robot was developed. This strategy achieves trajectory tracking and the autonomous displacement of a rover, of which its configuration involves complex kinematics and dynamics. All these lineaments reduce the complexity of the analysis, the number of electronic components to implement, the computational requirements and the energy consumption. The robotic system used is based on the Shrimp rover, which is a robot with a passive suspension that is capable of carrying out displacements over rough terrain. The tests were performed using numerical simulations with different desired trajectories, and also using experimental tests using a passive suspension rover-type mobile robot. Full article

Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method. Full article

Industry 4.0 is an industrial paradigm that is causing changes in form and substance in factories, companies and businesses around the world and is impacting work and education in general. In fact, the disruptive technologies that frame the Fourth Industrial Revolution have the potential to improve and optimize manufacturing processes and the entire value chain, which could lead to an exponential evolution in the production and distribution of goods and services. All these changes imply that the fields of engineering knowledge must be oriented towards the concept of Industry 4.0, for example, Mechanical Engineering. The development of various physical assets that are used by cyber-physical systems and digital twins is based on mechanics. However, the specialized literature on Industry 4.0 says little about the importance of mechanics in the new industrial era, and more importance is placed on the evolution of Information and Communication Technologies and artificial intelligence. This article presents a frame of reference for the importance of Mechanical Engineering in Industry 4.0 and proposes an extension to the concept of Mechanics 4.0, recently defined as the relationship between mechanics and artificial intelligence. To analyze Mechanical Engineering in Industry 4.0, the criteria of the four driving forces that defined mechanics in the Third Industrial Revolution were used. An analysis of Mechanical Engineering Education in Industry 4.0 is presented, and the concept of Mechanical Engineering 4.0 Education is improved. Finally, the importance of making changes to the educational models of engineering education is described. Full article