Machines, Volume 11, Issue 12 (December 2023) – 55 articles

Transformerless inverters have an important role in the electrical energy market. The high-efficiency and reliable inverter concept is one of the most widely used inverters in single-phase photovoltaic systems because of its high efficiency, low cost, and reduced leakage ground current. However, the leakage ground current behavior depends on the power and weather conditions, which can increase the parasitic capacitance value, thus producing an increase in the leakage ground current magnitude. In this paper, it is proposed to add a passive inductive–capacitive output filter to the inverter structure in order to reduce the dependency of the leakage ground current on the system power and weather conditions. The inductive–capacitive output filter is designed in such a way that it can provide a low impedance path for the leakage ground current, different from the ground path. The proposed system was evaluated both through simulations and experimentally in a 1 kW laboratory prototype. Full article

The shaft eddy currents cause a significant saturation in two-pole induction machines (IMs) as they generate an opposing field and repulse the main flux, thus tightening the flux path. This results in inaccurate performance estimations with the magnetizing inductance measured in no-load conditions when the machine is loaded. This article presents a modified IM equivalent circuit considering the rotor back iron saturation effects caused by the solid shaft eddy currents using experimental measurements and recursive parameter estimation techniques. The classical equivalent circuit (CEC) parameters are determined with the standard test techniques followed by the parameter estimation of the newly introduced modified equivalent circuit (MEC) parameters. The proposed modified equivalent circuit is benchmarked with CEC and finite element analysis (FEA) simulations with and without considering eddy effects. The proposed MEC model and the FEA that consider eddy effects performed better than the other models and yielded a negligibly small error over a wide range of loading conditions. Compared to the FEA, the proposed MEC estimates the IM performance much faster, which makes it more appealing for IM performance estimations. Full article

The deep learning diagnosis of aircraft engine-bearing faults enables cost-effective predictive maintenance while playing an important role in increasing the safety, reliability, and efficiency of aircraft operations. Because of highly dynamic and harsh operating conditions of this system, such modeling is challenging due to data complexity and drift, making it difficult to reveal failure patterns. As a result, the objective of this study is dual. To begin, a highly structured data preprocessing strategy ranging from extraction, denoising, outlier removal, scaling, and balancing is provided to solve data complexity that resides specifically in outliers, noise, and data imbalance problems. Gap statistics under k-means clustering are used to evaluate preprocessing results, providing a quantitative estimate of the ideal number of clusters and thereby enhancing data representations. This is the first time, to the best of authors’ knowledge, that such a criterion has been employed for an important step in a preliminary ground truth validation in supervised learning. Furthermore, to tackle data drift issues, long-short term memory (LSTM) adaptive learning features are used and subjected to a learning parameter improvement method utilizing recursive weights initialization (RWI) across several rounds. The strength of such methodology can be seen by application to realistic, extremely new, complex, and dynamic data collected from a real test-bench. Cross validation of a single LSTM layer model with only 10 neurons shows its ability to enhance classification performance by 7.7508% over state-of-the-art results, obtaining a classification accuracy of 92.03 ± 0.0849%, which is an exceptional performance in such a benchmark. Full article

This research examines how fluid damping loss affects the operation of a two-pole, 5.5 HP (4 kW) induction machine (IM) within the context of different slot opening configurations developed for downhole water pump applications. Since these motors operate with their cavities filled with fluid, the variations in fluid viscosity and density, compared to air, result in the occurrence of damping losses. Furthermore, this loss can be attributed to the motor’s stator and rotor surface geometry, as the liquid within the motor cavity moves unrestrictedly within the motor housing. This study involves the examination of the damping loss in a 24-slot IM under different stator slot indentations. The investigation utilizes computational fluid dynamics (CFD) finite element analysis (FEA) and is subsequently validated through experiments. The aim of this work is to emphasize the significance of fluid damping loss in submerged machines. Results reveal that the damping loss exceeds 8% of the motor output power when the stator surface has indentations, and it diminishes to 3.2% of the output power when a custom wedge structure is employed to eliminate these surface indentations. Full article

The latest advances in car automation present new challenges in vehicle–driver interactions. Indeed, acceptance and adoption of high levels of automation (when full control of the driving task is given to the automated system) are conditioned by human factors such as user trust. In this work, we study the impact of anthropomorphic design on user trust in the context of a highly automated car. A virtual assistant was designed using two levels of anthropomorphic design: “voice-only” and “voice with visual appearance”. The visual appearance was a three-dimensional model, integrated as a hologram in the cockpit of a driving simulator. In a driving simulator study, we compared the three interfaces: two versions of the virtual assistant interface and the baseline interface with no anthropomorphic attributes. We measured trust versus perceived anthropomorphism. We also studied the evolution of trust throughout a range of driving scenarios. We finally analyzed participants’ reaction time to takeover request events. We found a significant correlation between perceived anthropomorphism and trust. However, the three interfaces tested did not significantly differentiate in terms of perceived anthropomorphism while trust converged over time across all our measurements. Finally, we found that the anthropomorphic assistant positively impacts reaction time for one takeover request scenario. We discuss methodological issues and implication for design and further research. Full article

In Additive Manufacturing, wherein the construction of parts directly from 3D models is facilitated, a meticulous focus on enhancing the mechanical characteristics of these components becomes imperative. This study delves into the nuanced impact of the orientation of deposited layers on the mechanical properties of 3D printed Polylactic Acid (PLA) parts. Experimental testing, coupled with predictive modeling using Tsai–Hill and Tsai–Wu criteria, forms the crux of our investigation. The predicted ultimate strength from both criteria exhibits commendable agreement with the 3D printed specimens across a spectrum of orientation angles. Concurrently, Finite Element Simulations are meticulously executed to forecast mechanical behavior, taking into account the observed elasticity and plasticity in various orientations. Our observations reveal a significant augmentation in Young’s modulus and ductility/elongation—40% and 70%, respectively—when transitioning from θ = 0° to θ = 90°. Furthermore, the ultimate strength experiences a notable increase, leading to varied failure modes contingent upon θ. These findings underscore the pivotal role played by the orientation of printed layers in shaping the anisotropic behavior of 3D printed PLA parts, thereby integrating key process variables for optimization objectives. This study contributes valuable insights for professionals in the engineering, design, and manufacturing domains who seek to harness the advantages of 3D printing technology while ensuring that the mechanical integrity of 3D printed parts aligns with their functional requisites. It emphasizes the critical consideration of orientation as a design parameter in the pursuit of optimization objectives. Full article

Supernumerary robotic limbs are mainly designed to augment the physical capabilities of able-bodied individuals, in a wide range of contexts from body support to surgery. When they are worn as wearable devices, they naturally provide inherent feedback due to the mechanical coupling with the human body. The user can, thus, perceive the interaction with the environment by relying on a combination of visual and inherent feedback. However, these can be inefficient in accomplishing complex tasks, particularly in the case of visual occlusion or variation in the environment stiffness. Here, we investigated whether, in a force-regulation task using a wearable supernumerary robotic arm (SRA), additional vibrotactile feedback can increase the control performance of participants compared to the inherent feedback. Additionally, to make the scenario more realistic, we introduced variations in the SRA’s kinematic posture and in the environment stiffness. Notably, our findings revealed a statistically significant improvement in user performance over all the evaluated metrics while receiving additional vibrotactile feedback. Compared to inherent feedback alone, the additional vibrotactile feedback allowed participants to exert the required force faster (p < 0.01), to maintain it for longer (p < 0.001), and with lower errors (p < 0.001). No discernible effects related to the SRA’s posture or environment stiffness were observed. These results proved the benefits of providing the user with additional vibrotactile feedback to convey the SRA’s force during interaction tasks. Full article

The dual drive sliding feed system can obtain a uniform and stable resolution at extremely low speeds and significantly reduce the system’s nonlinear friction. However, the numerous thermal sources within the system and the multipoint sliding contact during transmission result in a significant temperature rise, leading to considerable thermal deformation and errors. Moreover, the responsive mechanism of the thermal characteristics needs to be clarified. Therefore, firstly, a frictional torque model of the engagement of the screw and nut is established, and the heat generation, heat transfer, and thermal contact resistance (TCR) are solved. Then, based on the solution, a finite element thermal simulation model of the dual drive sliding feed system is established, and experiments are performed for validation. The results show that the error in temperature at the measuring point is less than 2.1 °C, and the axial thermal elongation of the screw is less than 6.2 µm. Finally, the thermal characteristics of the feeding system under various operating conditions are analyzed. The results show that the established thermal simulated model can effectively describe the dynamic thermal characteristics of the dual drive sliding feed system during operation. The effects of the rotational speed and ambient temperature on the dynamic thermal characteristics of the dual drive sliding feed system are investigated separately. The temperature increase in each part of the screw during the operation is characterized. Full article

This paper focuses on the development of an integrated system that can rapidly and accurately extract the geometrical dimensions of a physical object assisted by a robotic hand and generate a 3D model of an object in a popular commercial Computer-Aided Design (CAD) software using computer vision. Two sets of experiments were performed: one with a simple cubical object and the other with a more complex geometry that needed photogrammetry to redraw it in the CAD system. For the accurate positioning of the object, a robotic hand was used. An Internet of Things (IoT) based camera unit was used for capturing the image and wirelessly transmitting it over the network. Computer vision algorithms such as GrabCut, Canny edge detector, and morphological operations were used for extracting border points of the input. The coordinates of the vertices of the solids were then transferred to the Computer-Aided Design (CAD) software via a macro to clean and generate the border curve. Finally, a 3D solid model is generated by linear extrusion based on the curve generated in CATIA. The results showed excellent regeneration of an object. This research makes two significant contributions. Firstly, it introduces an integrated system designed to achieve precise dimension extraction from solid objects. Secondly, it presents a method for regenerating intricate 3D solids with consistent cross-sections. The proposed system holds promise for a wide range of applications, including automatic 3D object reconstruction and quality assurance of 3D-printed objects, addressing potential defects arising from factors such as shrinkage and calibration, all with minimal user intervention. Full article

Condition monitoring and fault management approaches can help with timely maintenance planning, assure industry-wide continuous production, and enhance both performance and safety in complex industrial operations. At the moment, data-driven approaches for condition monitoring and fault detection are the most attractive being conceived, developed, and applied with less of a need for sophisticated expertise and detailed knowledge of the addressed plant. Among them, Gaussian mixture model (GMM) methods can offer some advantages. However, conventional GMM solutions need the number of Gaussian components to be defined in advance and suffer from the inability to detect new types of faults and identify new operating modes. To address these issues, this paper presents a novel data-driven method, based on automated GMM (AutoGMM) and decision trees (DTree), for the online condition monitoring of electrical industrial loads. By leveraging the benefits of the AutoGMM and the DTree, after the training phase, the proposed approach allows the clustering and time allocation of nominal operating conditions, the identification of both already-classified and new anomalous conditions, and the acknowledgment of new operating modes of the monitored industrial asset. The proposed method, implemented on a commercial cloud-computing platform, is validated on a real industrial plant with electrical loads, characterized by a daily periodic working cycle, by using active power consumption data. Full article

This research explores the enhancement of mechanical properties in material architectures, such as strength-to-weight ratio and resilience, through the inspiration of natural systems. Historically, designs for additive manufacturing have relied on simple, repetitive structures like honeycombs, often leading to unnecessary material expenditure. This study aims to examine the compressive mechanical attributes of designs inspired by natural systems, including bird nests, cocoons, and the layered structure of skull bones. Through a comparative analysis, we assessed peak load capacity, strength-to-weight ratio, and resilience between these bioinspired architectures and a standard 3D infill pattern utilized in additive manufacturing. Findings indicate that structures inspired by sandwiched bone layers excel in resilience and peak load, whereas those based on bird nests are notably lighter and, in some cases, exhibit the highest strength-to-weight ratio. The insights provided here will help design engineers with empirically backed mechanical properties of bioinspired architectures, offering a novel methodology for the development of material systems influenced by biological paradigms. Full article

This paper proposes a new method for bearing fault diagnosis using wavelet packet transform (WPT)-based signal representation and informative factor linear discriminant analysis (IF-LDA). Time–frequency domain approaches for analyzing bearing vibration signals have gained wide acceptance due to their effectiveness in extracting information related to bearing health. WPT is a prominent method in this category, offering a balanced approach between short-time Fourier transform and empirical mode decomposition. However, the existing methods for bearing fault diagnosis often overlook the limitations of WPT regarding its dependence on the mother wavelet parameters for feature extraction. This work addresses this issue by introducing a novel signal representation method that employs WPT with a new rule for selecting the mother wavelet based on the power spectrum energy-to-entropy ratio of the reconstructed coefficients and a combination of the nodes from different WPT trees. Furthermore, an IF-LDA feature preprocessing technique is proposed, resulting in a highly sensitive set of features for bearing condition assessment. The k-nearest neighbors algorithm is employed as the classifier, and the proposed method is evaluated using datasets from Paderborn and Case Western Reserve universities. The performance of the proposed method demonstrates its effectiveness in bearing fault diagnosis, surpassing existing techniques in terms of fault identification and diagnosis performance. Full article

In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. Even a slight inaccuracy or minor error can render the localization system unreliable and unusable in a matter of seconds. Traditional algorithms, such as the extended Kalman filter (EKF), have been applied for a long time in non-linear systems. These systems have white noise in both the system and in the estimation model. These approaches require deep knowledge of the non-linear noise characteristics of the sensors. On the other hand, as a subset of artificial intelligence (AI), neural network-based (NN) algorithms do not necessarily have these strict requirements. The current paper proposes an AI-based long short-term memory (LSTM) localization approach and evaluates its performance against the ground truth. Full article

In order to break through the speed bottleneck, researchers envision using tubes to cover high-speed maglev trains and extract some of the air inside the tubes, creating a low-density environment on the ground, greatly reducing the aerodynamic drag of the trains, and in a relatively economical and feasible way, making high subsonic (600 km/h and above) and even supersonic ground transportation possible. The faster the running speed of high-speed trains, the greater the impact of aerodynamic drag on their energy consumption. Studying the aerodynamic characteristics of trains with a speed of 600 km/h can help optimize the aerodynamic shape of the train, reduce aerodynamic drag, and reduce energy consumption. This has positive implications for improving train energy efficiency, reducing energy consumption, and environmental impact. This paper adopts the numerical simulation method to study the drag reduction effect of the plasma arrangement and different excitation speeds on the train set in four positions when the incoming wind speed is 600 km/h, to analyze the mechanism of drag reduction, and then to analyze the combination of working conditions in order to investigate the drag reduction effect of plasma on the vacuum tube train set with an ambient pressure of 10,000 Pa. The findings demonstrate that the plasma induces the directional flow of the gas close to the wall to move the flow separation point backward and delay the separation of the flow, thereby reducing the front and rear differential pressure drag of the train set and lowering the aerodynamic drag coefficient of the entire train. The plasma arrangement is located at the rear of the flow separation point and in close proximity to the flow separation point. The pneumatic drag reduction effect peaks when the excitation speed reaches 0.2 times the train speed and the pneumatic drag reduction ratio is around 0.88%; the pneumatic drag reduction ratio of the rear car peaks when the excitation speed reaches 0.25 times the train speed and the pneumatic drag reduction ratio is 1.62%. The SDBD (Surface Dielectric Barrier Discharge) device is installed at the flow separation point around the nose tip of the rear car. Full article

This study proposes a double-crown tooth surface modification technology that improves the load-carrying capacity of non-orthogonal helical tooth surface gears. The tooth modification is determined by a modified rack-cutter, and its feed motion is related to an intentionally designed transmission error. The novelty of the tooth modification design is that the transmission error can be pre-designed. First, changing the tooth profile of the tool enables preliminary modification along the tooth profile direction; second, by modifying the interaction between the tool and the machined gear, subsequent fine adjustments are made to the contact path. This two-stage tooth modification strategy not only retains the advantages of the traditional method but also significantly improves the balance of the load distribution on the tooth surface through an original contact path modification strategy. Through systematic tooth contact analysis (TCA) and loaded tooth contact analysis (LTCA), it was verified that the new method reduces contact stress and tooth root bending stress and improves the gear’s resistance to misalignment errors. This research provides the basis and motivation for further exploring and improving this tooth profile modification technology to solve the challenges faced by more complex gear systems. Full article

In self-driving vehicles, passengers can set their seats in an unconventional seating position, such as rear-facing. Sitting in such an orientation can increase the risk of whiplash in the head-and-neck system in a frontal impact, as frontal crashes usually have higher severities compared with rear-end crashes. This paper shows that a forward-facing front seat optimised for rear-impact protection needs to be redesigned to be used as a rear-facing seat. In the second and main part of this paper, a restraint system for rear-facing car seats is developed, and frontal impact simulations with 64 km/h of delta-V are used to evaluate its performance. The designed seating system comprises two rigid torso plates, a fixed recliner and an energy absorber under the seat pan. Without using the developed restraint system, the 50th percentile male human model is exposed to neck shear forces exceeding 600 N. With the developed restraint system, neck shear forces are less than 350 N in frontal impacts with 64 km/h of delta-V. Apart from whiplash, the risk of head, chest, lower extremity and lower back injuries are also evaluated. The results confirm that the developed restraint system successfully protects the occupant since all assessment criteria values are lower than the injury assessment reference values. Full article

In the grinding process of complex-shaped cast workpieces, discrepancies between the workpiece’s contours and their corresponding three-dimensional models frequently lead to deviations in the machining trajectory, resulting in instances of under-grinding or over-grinding. Addressing this challenge, this study introduces an advanced robotic grinding force automatic tracking technique, leveraging a combination of deep neural networks and genetic algorithms. Harnessing the capability of force sensing, our method dynamically recalibrates the grinding path, epitomizing truly flexible grinding. Initially, in line with the prerequisites for force and pose tracking, an impedance control strategy was developed, integrating pose deviations with force dynamics. Subsequently, to enhance steady-state force tracking, we employed a genetic algorithm to compensate for force discrepancies caused by positional errors. This was built upon the foundational concepts of the three-dimensional model, impedance control, and environmental parameter estimation, leading to an optimized grinding trajectory. Following tracking tests, it was observed that the grinding’s normal force was consistently controlled within the bracket of 20 ± 2.5 N. To further substantiate our methodology, a specialized experimental platform was established for grinding complex-shaped castings. Optimized strategies were employed under anticipated forces of 5 N, 10 N, and 15 N for the grinding tests. The results indicated that the contact forces during the grinding process remained stable at 5 ± 1 N, 10 ± 1.5 N, and 15 ± 2 N. When juxtaposed with conventional teaching grinding methods, our approach manifested a reduction in grinding forces by 71.4%, 70%, and 75%, respectively. Post-grinding, the workpieces presented a pronounced enhancement in surface texture, exhibiting a marked increase in surface uniformity. Surface roughness metrics, originally recorded at 17.5 μm, 17.1 μm, and 18.7 μm, saw significant reductions to 1.5 μm, 1.6 μm, and 1.4 μm, respectively, indicating reductions by 76%, 73%, and 78%. Such outcomes not only meet the surface finishing standards for complex-shaped castings but also offer an efficacious strategy for robot-assisted flexible grinding. Full article

The mechanism parameters of the manipulator not only have a great influence on the size of the working space but also affect flexible performance distribution. Aimed at obtaining a 6 DOF modular manipulator, mechanism parameters were optimized in order to explore the effect of upper arm and forearm dimensions on the end dexterity of the manipulator. First, forward kinematic equations were derived using the DH method, and the Jacobian matrix of the manipulator was solved. Second, three indicators, including the condition number index, structural length index, and global conditioning index, were employed as optimization indicators for the mechanism parameters of the manipulator, and an orthogonal experiment was designed based on the Grey–Taguchi method and robot toolbox. Third, the grey relational analysis method was used to process the experimental results, and the grey relational grade for each group was solved. Last, the variation curve between the grey relational grade and the parameter level of each mechanism was drawn, and optimized mechanical arm mechanism parameters were derived. It was found that although the overall dimension of the manipulator was slightly decreased, as determined via comparing the original and optimized manipulator length, the performance indexes were improved. The results not only verified the correctness of the proposed optimization method but also laid a foundation for subsequent research on the dynamic performance of modular robot systems. Full article

The increasing use of high-speed machinery leads to a growing demand for efficient balancing methods for flexible rotors. Conventional balancing methods are costly and time-consuming since they require multiple trial runs. For this reason, recent research focuses on model-based balancing methods, which substitute measurements with simulations. This work presents and examines a model-based modal balancing method, which utilizes the Numerical Assembly Technique (NAT) for the in situ balancing of warped rotors with flexible behaviour. NAT is a successive modification of discrete–continuous modelling that leads to analytical harmonic solutions and is very computationally efficient. In this version of NAT, internal damping is also included with a viscoelastic material model using fractional time derivatives. The modal balancing procedure is adapted to handle measurements outside of the critical speeds and the effect of the pre-bend on the rotor. The accuracy of the simulations is shown by comparing measured mode shapes and eigenvalues with values calculated with NAT. Furthermore, the first two modes of a rotor test bed are successfully balanced without trial runs. Full article

In this paper, the design and experimental evaluation of a parallel robotic system based on a linear delta geometry is presented. The design considers the requirements for high-precision applications including workspace, motion resolution, and payload. The entire design process includes robot kinematics, control, and optimization, resulting in the demonstration of a working device. The robot structure offers a versatile and simplified design when compared with state-of-the-art devices being able to be adapted to perform different tasks while keeping the advantages of high precision with reduced complexity. The presented robot prototype was constructed and evaluated experimentally through three proof-of-concept experiments mimicking tasks requiring high motion precision such as microsurgery, semiconductor testing, and optical device alignment. The obtained results in the three experimental scenarios validate that the here-proposed design can achieve an average motion precision of ~3.3 ± 0.3 μm with varying load conditions, thus confirming its potential to be used for high-precision tasks in industrial and medical settings. Full article

Humans and animals navigate complex and variable terrain in day-to-day life. Wearable assistive exoskeletons interact with biological tissues to augment movement. Yet, our understanding of how these devices impact the biomechanics of movement beyond steady-state environments remains limited. We investigated how passive ankle exoskeletons influence mechanical energetics and neuromuscular control of the lower-limb during level, incline, and decline walking. We collected kinematic and kinetic measures to determine ankle, knee, and hip mechanics and surface electromyography to characterize muscle activation of lower-limb muscles while participants walked on level, incline, and decline surfaces (0°, +5°, and −5°) with exoskeletons of varying stiffnesses (0–280 Nm rad⁻¹). Our results demonstrate that walking on incline surfaces with ankle exoskeletons was associated with increased negative work and power at the knee and increased positive work and power at the hip. These alterations in joint energetics may be linked to an additional requirement to load the springy exoskeleton in incline conditions. Decline walking with ankle exoskeletons had no influence on knee or hip energetics, likely owing to disrupted exoskeleton clutch actuation. To effectively offload the musculoskeletal system during walking on sloped surfaces, alterations to passive ankle exoskeleton clutch design are necessary. Full article

This paper presents the basic structural parts, a 3D model, and the overall design of a laboratory machine, which was created to detect vibrations generated by the casing of a conveyor roller rotating at different speeds. The intention of the authors was to verify whether plastic brackets inserted into the structurally modified trestles of a fixed conveyor idler can reduce the vibration values transmitted from the rotating conveyor roller to the trestle of a fixed idler. Experimental vibration measurements taken on the non-rotating parts of conveyor rollers, performed on a laboratory machine according to ISO 10816, are suitable for characterizing their operating conditions with regard to trouble-free operation. The aim of this paper is to detect the vibrations of a rotating conveyor roller on a laboratory machine in the defined places of a fixed conveyor idler and also on the steel frame of a laboratory machine that represents the supporting track of a belt conveyor. Vibrations detected by piezoelectric acceleration sensors were recorded by a measuring apparatus and displayed in the environment of Dewesoft X software (version 10). The measurements show that the vibration values grow with the increasing speed of the conveyor roller rotation. Experimental measurements have proven the correctness of the assumption that the vibrations transmitted to the trestle of a fixed conveyor idler are lower by up to 40% when using plastic brackets into which the axle of the conveyor roller is attached, compared to the solution where the axle of the conveyor roller is inserted into the notches of a steel trestle. Full article

This article focuses on the development and assessment of a PID-based computationally cost-efficient longitudinal control algorithm for platooning trucks. The study employs a linear controller with a nested architecture, wherein the inner loop regulates relative velocities while the outer loop governs inter-vehicle distances within platoon vehicles. The design of the proposed PID controller entails a comprehensive focus on system identification, particularly emphasizing actuation dynamics. The simulation framework used in this study has been established through the integration of TruckSim^® and Simulink^®, resulting in a co-simulation environment. Simulink^® serves as the platform for control action implementation, while TruckSim^® simulates the vehicle’s dynamic behavior, thereby closely replicating real world conditions. The significant effort in fine-tuning the PID controller is described in detail, including the system identification of the linearized longitudinal dynamic model of the truck. The implementation is followed by an extensive series of simulation tests, systematically evaluating the controller’s performance, stability, and robustness. The results verify the effectiveness of the proposed controller in various leading truck operational scenarios. Furthermore, the controller’s robustness to large fluctuations in road grade and payload weight, which is commonly experienced in commercial vehicles, is evaluated. The simulation results indicate the controller’s ability to compensate for changes in both road grade and payload. Additionally, an initial assessment of the controller’s efficiency is conducted by comparing the commanded control efforts (total torque on wheels) along with the total fuel consumed. This initial analysis suggests that the controller exhibits minimal aggressive tendencies. Full article

Newly introduced vehicles come with various added functions, each time utilizing data from different sensors. One prominent related function is autonomous driving, which is performed in cooperation with multiple sensors. These sensors mainly include image sensors, depth sensors, and infrared detection technology for nighttime use, and they mostly generate data based on image processing methods. In this paper, we propose a model that utilizes a parallel transformer design to gradually reduce the size of input data in a manner similar to a stairway, allowing for the effective use of such data and efficient learning. In contrast to the conventional DETR, this model demonstrates its capability to be trained effectively with smaller datasets and achieves rapid convergence. When it comes to classification, it notably diminishes computational demands, scaling down by approximately 6.75 times in comparison to ViT-Base, all the while maintaining an accuracy margin of within ±3%. Additionally, even in cases where sensor positions may exhibit slight misalignment due to variations in data input for object detection, it manages to yield consistent results, unfazed by the differences in the field of view taken into consideration. The proposed model is named Stairwave and is characterized by a parallel structure that retains a staircase-like form. Full article

A comparative study is presented, focusing on three different bioinspired design methodologies applied to a large-scale aeronautical tooling use case. The study aims to optimize the structure in terms of the first vibration mode, minimizing mass, and supporting operational loads. The development of lightweight metallic components is of great importance for industries such as aerospace, automotive, and energy harvesting, where weight reduction can lead to significant improvements in performance, efficiency, and sustainability. Bioinspired design offers a promising approach to achieving these goals. The study begins with an introduction to natural selection and various bioinspired concepts. It proceeds with a thorough review of the selected bioinspired design methodologies and tools, which are then applied to the chosen use case. The outcomes for each methodology were explored with respect to the design requirements. Subsequently, the most suitable design was selected according to the success criteria defined and its validation is explained. The manufacturing of this design was carried out using an advanced and novel approach specifically tailored to accommodate the large dimensions and complexity of the structure. Finally, modal testing was performed to validate the entire process, and the results obtained demonstrate the potential effectiveness of bioinspired design methodologies in achieving lightweighting and optimizing vibration modes for large-scale aeronautical tooling. Full article

Local Fisher discriminant analysis (LFDA) has been widely applied to dimensionality reduction and fault classification fields. However, it often suffers from small sample size (SSS) problem and incorporates all process variables without emphasizing the key faulty ones, thus leading to degraded fault diagnosis performance and poor model interpretability. To this end, this paper develops the sparse variables selection based exponential local Fisher discriminant analysis (SELFDA) model, which can overcome the two limitations of basic LFDA concurrently. First, the responsible faulty variables are identified automatically through the least absolute shrinkage and selection operator, and the current optimization problem are subsequently recast as an iterative convex optimization problem and solved by the minimization-maximization method. After that, the matrix exponential strategy is implemented on LFDA, it can essentially overcome the SSS problem by ensuring that the within-class scatter matrix is always full-rank, thus more practical in real industrial practices, and the margin between different categories is enlarged due to the distance diffusion mapping, which is benefit for the enhancement of classification accuracy. Finally, the Tennessee Eastman process and a real-world diesel working process are employed to validate the proposed SELFDA method, experimental results prove that the SELFDA framework is more excellent than the other approaches. Full article

An observer is proposed for a trapezoidal brushless DC motor composed of a cascade connection of a reduced-order Luenberger observer and a high-order sliding mode (HOSM) differentiator. This configuration can estimate the angular velocity and reconstruct the load torque, key elements for the control of this type of motor, under the mild assumption that the variable load torque and its k-th time derivatives are bounded. The proposed observer was tested on an experimental test bench based on Texas Instruments (TI) High Voltage Digital Motor Control (HVMTR Kit) using a Delfino F28379D micro controller. The results show that the velocity and load torque can be properly estimated, despite the presence of noise in the current measurements. Full article

Global food security is a critical issue today, strained by a wide range of factors including global warming, carbon emissions, sociopolitical and economic challenges, traditional workforce decline and population growth. Technical innovations that address food security, like agricultural robotics, are gaining traction in industry settings, moving from controlled labs and experimental test facilities to real-world environments. Such technologies require sufficient network infrastructure to support in-field operations; thus, there is increased urgency to establish reliable, high-speed wireless communication networking solutions that enable deployment of autonomous agri-robots. The work presented here includes two contributions at the intersection of network infrastructure and in-field agricultural robotics. First, the physical performance of a private 5G-SA system in an agri-robotics application is evaluated and in-field experimental results are presented. These results are compared (using the same experimental setup) against public 4G and private WiFi6 (a newly emerging wireless communication standard). Second, a simulated experiment was performed to assess the “real-time” operational delay in critical tasks that may require quick turnaround between in-field robot and off-board processing. The results demonstrate that public 4G cannot be used in the agricultural domain for applications that require high throughput and reliable communication; that private 5G-SA greatly outperforms public 4G in all performance metrics (as expected); and that private WiFi6, though limited in range, is a fast and very reliable alternative in specific settings. While a single wireless solution does not currently exist for the agricultural domain, multiple technologies can be combined in a hybrid solution that meets the communications requirements. Full article

This article investigates the comparison between two configurations of 20 MW offshore synchronous wind generators using ferrite and rare-earth permanent magnets. The optimization-based comparison concerns the torque ripple and active mass, which are two crucial criteria for offshore wind generators. Both generators adopt surface-mounted permanent magnet type with direct-drive technology to avoid problems associated with the gearboxes. The result shows that at the full-load condition, the ferrite permanent magnet generator can reduce the torque ripple to as much as 0.12%, while the rare-earth counterpart can be about 2.5 times lighter than the former one. Full article

Propanol isomers, which are oxygen-rich fuels, possess superior octane ratings and energy density in comparison to methanol and ethanol. Recently, due to advancements in fermentation techniques, these propanol isomers have garnered increased interest as additives for engines. They are being explored to decrease emissions and reduce the usage of conventional fossil fuels. This study delves into this emerging field. One of the alternatives is the use of alcohol fuels in their pure state or as additives to traditional fuels. Alcohols, due to their higher volumetric energy density, are better fuels for spark ignition engines than hydrogen and biogas. Alcohol-blended fuels or alcohol fuels in their pure state may be used in gasoline engines to reduce exhaust emissions. The current research emphasizes the effect of isopropanol gasoline blends on the performance and emissions characteristics of a gasoline direct injection (GDI) engine. This investigation was conducted with different blends of isopropanol and gasoline (by volume: 10% isopropanol [IP10], 20% isopropanol [IP10], 30% isopropanol [IP30], 40% isopropanol [IP40], and 50% isopropanol [IP50]). The reviewed results showed that with increasing isopropanol in the fuel blends, engine brake power increased while BSFC decreased. In terms of emissions, with the increase in isopropanol in the fuel blends, CO and HC emissions decreased while CO₂ and NOx emissions increased. Full article