Machines

Probabilistic random vibration can speed up wear and tear on several components of the tracked vehicle, including the track system, drivetrain, and suspension. Extended exposure to high levels of vibration can cause structural damage to the vehicle frame and other critical components. Assessing random vibration in track vehicles requires a comprehensive approach that considers both the root causes and potential consequences of the vibrations. This random vibration significantly influences the structural performance of suspension arm which is key component of tracked vehicle. Damage due to fatigue is conventionally computed using time domain loaded signals with stress or strain data. This approach generally holds good when loading is periodic in nature but not be a good choice when dynamic resonance is in process. In this case an alternative frequency domain fatigue life analysis is used where the random loads and responses are characterized using a concept called Power spectral density (PSD). The current research article investigates the fatigue damage characteristics of a tracked vehicle suspension arm considering the dynamic loads induced by traversing on smooth and rough terrain. The analysis focusses on assessing the damage and stress response Power spectral density (PSD) ground-based excitation which is termed PSD-G acceleration. Quasi Static Finite Element Method based approach is used to simulate the operational conditions experienced by the suspension arm. Through comprehensive numerical simulations, the fatigue damage accumulation patterns are examined, providing insights into the structure integrity and performance durability of the suspension arm under varying operational scenarios. The obtained stress response PSD data and fatigue damage showed that the rough terrain response exhibits higher stresses in suspension arm. The accumulated stresses in case of rough terrain may prompt to brittle failure at specific critical locations. This research contributes to the advancement to the design and optimization strategies for tracked vehicle components enhancing their reliability and longevity in demanding operational environments.

In modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-precision, high-efficiency machining. To address this problem, this paper proposes a novel approach to identify the tool-wear state using information fusion technology and the sparrow search algorithm (SSA)–backpropagation (BP) neural network framework. This method uses a principal component analysis (PCA) to fuse multi-domain features extracted from three-way vibration signals, power signals, and temperature signals. Subsequently, the optimal initial threshold and weight of the BP neural network are optimized using the SSA to prevent the network from falling into the local optimum and accelerate the convergence of the algorithm. Lastly, a tool-wear-state identification model based on the SSA–BP neural network is constructed. Experimental results show that the proposed method has an identification accuracy of 98.33%, precision rate of 98.81%, recall rate of 97.96%, and F1 score of 98.36%.

Unmanned Aerial Vehicles (UAVs), as widely used tools, can achieve better efficiency when integrated into a multi-UAV system than individual, dispersed units. Obstacle avoidance and formation control are fundamental requirements for such systems. The Boids algorithm, a biomimetic model suitable for swarming, serves as the foundation for this study. This paper proposes a novel integrated algorithm based on Boids that can be applied to multi-UAV systems for obstacle avoidance and formation control. The algorithm enables the multi-UAV system to automatically form formations, autonomously avoid obstacles, and recover formations rapidly. In this algorithm, each UAV functions as an agent within the system that is capable of independently collecting and sharing information. Each agent can make independent decisions to enter either the formation mode or the obstacle avoidance mode based on external environmental factors. The formation mode utilizes the virtual structure method to guide UAVs to their virtual formation positions. In the obstacle avoidance mode, the artificial potential field method is employed to ensure that each UAV maintains a safe distance from other UAVs that pose collision risks and various complex obstacles, regardless of their number. Simulation experiments were conducted on the Unity platform, varying the number of UAVs and the formation shapes. The results verified that the algorithm operates correctly, stably, and in a timely manner, demonstrating good performance.

Surface classification is critical for ground robots operating in diverse environments, as it improves mobility, stability, and adaptability. This study introduces IMU-based deep learning models for surface classification as a low-cost alternative to computer vision systems. Two feature fusion models were introduced to classify the surface type using time-series data from an IMU sensor mounted on a ground robot. The first model, a cascaded fusion model, employs a 1-D Convolutional Neural Network (CNN) followed by a Long Short-Term Memory (LSTM) network and then a multi-head attention mechanism. The second model is a parallel fusion model, which processes sensor data through both a CNN and an LSTM simultaneously before concatenating the resulting feature vectors and then passing them to a multi-head attention mechanism. Both models utilize a multi-head attention mechanism to enhance focus on relevant segments of the time-sequence data. The models were trained on a normalized Internal Measurement Unit (IMU) dataset, with hyperparameter tuning achieved via grid search for optimal performance. Results showed that the cascaded model achieved higher accuracy metrics, including a mean Average Precision (mAP) of 0.721 compared to 0.693 for the parallel model. However, the cascaded model incurred a 44.37% increase in processing time, which makes the parallel fusion model more suitable for real-time applications. The multi-head attention mechanism contributed significantly to accuracy improvements, particularly in the cascaded model.

Wood-based panels (WBPs) have versatile structural applications and are a suitable alternative to plastic panels and metallic materials. They have appropriate strength parameters that provide the required stiffness and strength for furniture products and construction applications. WBPs are usually processed by cutting, milling and drilling. Especially in the furniture industry, the accuracy of processing is crucial for aesthetic reasons. Ensuring the WBP surface's high quality in the production cycle is associated with the appropriate selection of processing parameters and tools adapted to the specificity of the processed material (properties of wood, glue, type of resin and possible contamination). Therefore, expert assessment of the durability of WBPs is difficult. The interest in the automatic monitoring of cutting tools in sustainable production, according to the concept of Industry 4.0, is constantly growing. The use of flexible automation in the machining of WBPs is related to the provision of tools monitoring the state of tool wear and surface quality. Drilling is the most common machining process that prepares panels for assembly operations and directly affects the surface quality of holes and the aesthetic appearance of products. This paper aimed to synthesize research findings across Medium-Density Fiberboards (MDFs), particleboards and oriented strand boards (OSBs), highlighting the impact of processing parameters and identifying areas for future investigation. This article presents the research trend in the adoption of the new general methodological assumptions that allow one to define both the drill condition and delamination monitoring in the drilling of the most commonly used wood-based boards, i.e., particleboards, MDFs and OSBs.

To investigate the size effect on the energy characteristics of axial flow pumps, this study scaled the original model size based on the head similarity principle, resulting in four size schemes (Schemes 2–4 correspond to 3, 5, and 10 times the size of Scheme 1, respectively). By solving the unsteady Reynolds-averaged Navier–Stokes (URANS) equations with the Shear Stress Transport (SST) k-omega turbulence model, the external characteristic parameters and internal flow field structures were predicted. Additionally, the spatial distribution of internal hydraulic losses was analyzed using entropy generation theory. The results revealed three key findings: (1) the efficiency of axial flow pumps significantly improves with increasing size ratio, with Scheme 4 exhibiting a 6.1% efficiency increase compared to Scheme 1; (2) as the size ratio increases, the entropy production coefficients of all hydraulic components decrease, with the impeller and guide vanes in Scheme 4 showing reductions of 55.1% and 56.5%, respectively, compared to Scheme 1; (3) the high entropy generation coefficient regions in the impeller and guide vanes are primarily concentrated near the rim, with their area decreasing as the size ratio increases. Specifically, the entropy production coefficients at the rim of impeller and guide vanes in Scheme 4 decreased by 84.85% and 58.2%, respectively, compared to Scheme 1. These findings provide valuable insights for the selection and optimization of axial flow pumps in applications such as cross-regional water transfer, agricultural irrigation, and urban drainage systems.

Through hydraulic control principles, numerical simulation and indoor testing, the opening principle of a toe-end sliding sleeve with a time delay mechanism is explained. Conventional toe-end sliding sleeve in shale oil wells have problems with premature opening and a failure to open, which means they cannot ensure the whole-well pressure test process and can cause serious economic losses to the oil and gas industry. In order to solve the above problems, a new type of optimal design for toe-end sliding sleeve with a 30 min delayed opening is proposed. In this paper, based on the principle of hydraulic flow, ABAQUS 2022 numerical simulation software was used to study the influence of different states and the same hydraulic pressure on its internal stress–strain value. A qualitative study of the delayed-opening function was carried out using a pressurized pump unit. In addition, principle tests under different operating parameters were designed to quantitatively analyze the pin shear situation and the delayed opening time of the toe-end sliding sleeve when the tool was fitted with different numbers of pins and when the delay valve was fitted. In addition, the simulation results of the hydraulic fluid’s flow inside the time delay mechanism with different nozzle diameters were compared with the theoretical values, which showed that the hydraulic fluid’s flow rate inside the mechanism increased with the enlargement of the nozzle diameter, and the optimal nozzle diameter was 0.56 mm. The indoor test showed that when the tool was retrofitted with a time delay mechanism, installing six pins was the optimal combination. The field application of the slip-on was able to satisfy an opening time delay of 28.3 with a relative error of only 5.67%. These results complement the research on toe-end sliding sleeve and provide ideas for the optimization of toe-end slipcovers incorporating a time delay mechanism.

In this study, a novel fully compliant four-bar-based universal joint is introduced. The difference between the angular positions of the input and output shafts is obtained by two equivalent fully compliant four-bar mechanisms that operate simultaneously by sharing the same input link. During the design phase of the mechanism an iterative method for determining the optimum angular position of the links is proposed and applied. The proposed design is a single-piece mechanism that is produced from polypropylene and compatible with both additive manufacturing and injection molding techniques. The scalability of compliant mechanisms allows for a wide range of size options during the design process. An extensive survey of the current literature reveals that the design proposed is without precedent, marking it as both novel and inventive. In this study, the design procedure of the proposed universal joint, stress analysis of the links, the torque capacity of the joint, and an experimental setup are presented. The produced prototype demonstrates the functionality of the proposed design. In addition, it should be noted that the prototype production of the proposed design was conducted using the additive manufacturing method. This production technique is a significant motivation behind the design of the mechanism as a single piece. Additionally, the proposed mechanism in its current form is also suitable for production using the injection molding method which is widely used in the industry.

Air compressors play a crucial role in industrial production, and accurately assessing their health status is vital for ensuring stable operation. The field of health status assessment has made significant progress; however, challenges such as dataset class imbalance, feature selection, and accuracy improvement remain and require further refinement. To address these issues, this paper proposes a novel algorithm based on multi-strategy optimization, using air compressors as the research subject. During data preprocessing, the Synthetic Minority Over-sampling Technique (SMOTE) is introduced to effectively balance class distribution. By integrating the Squeeze-and-Excitation (SE) mechanism with Convolutional Neural Networks (CNNs), key features within the dataset are extracted and emphasized, reducing the impact of irrelevant features on model efficiency. Finally, Bidirectional Long Short-Term Memory (BiLSTM) networks are employed for health status assessment and classification of the air compressor. The Ivy algorithm (IVYA) is introduced to optimize the BiLSTM’s hyperparameters to improve classification accuracy and avoid local optima. Through comparative and ablation experiments, the effectiveness of the proposed SMOTE-IVY-SE-CNN-BiLSTM model is validated, demonstrating its ability to significantly enhance the accuracy of air compressor health status assessment.

The integration of vision–language models (VLMs) with robotic systems represents a transformative advancement in autonomous task planning and execution. However, traditional robotic arms relying on pre-programmed instructions exhibit limited adaptability in dynamic environments and face semantic gaps between perception and execution, hindering their ability to handle complex task demands. This paper introduces GPTArm, an environment-aware robotic arm system driven by GPT-4V, designed to overcome these challenges through hierarchical task decomposition, closed-loop error recovery, and multimodal interaction. The proposed robotic task processing framework (RTPF) integrates real-time visual perception, contextual reasoning, and autonomous strategy planning, enabling robotic arms to interpret natural language commands, decompose user-defined tasks into executable subtasks, and dynamically recover from errors. Experimental evaluations across ten manipulation tasks demonstrate GPTArm’s superior performance, achieving a success rate of up to 91.4% in standardized benchmarks and robust generalization to unseen objects. Leveraging GPT-4V’s reasoning and YOLOv10’s precise small-object localization, the system surpasses existing methods in accuracy and adaptability. Furthermore, GPTArm supports flexible natural language interaction via voice and text, significantly enhancing user experience in human–robot collaboration.

The dynamic characteristics of high-precision planetary reducers in terms of vibration response and dynamic transmission error have a significant impact on positioning accuracy and service life. However, the dynamics of high-precision two-stage helical planetary reducers have not been studied extensively enough and must be studied in depth. In this paper, the dynamic characteristics of the high-precision two-stage helical planetary reducer are investigated in combination with simulation tests, and the microscopic modification of the gears is optimized by the helix modification with drums, with the objective of reducing the vibration response and dynamic transmission error. Considering the time-varying meshing stiffness of gears and transmission errors, a translation–torsion coupled dynamics model of a two-stage helical planetary gear drive is established based on the Lagrange equations by using the centralized parameter method for analyzing the dynamic characteristics of the reducer. The differential equations of the system were derived by analyzing the relative displacement relationship between the components. On this basis, a finite element model of a certain type of high-precision reducer was established, and factors such as rotate speed and load were investigated through simulation and experimental comparison to quantify or characterize their effects on the dynamic behavior and transmission accuracy. Based on the combined modification method of helix modification with drum shape, the optimized design of this type of reducer is carried out, and the dynamic characteristics of the reducer before and after modification are compared and analyzed. The results show that the adopted modification optimization method is effective in reducing the vibration amplitude and transmission error amplitude of the reducer. The peak-to-peak value of transmission error of the reducer is reduced by 19.87%; the peak value of vibration acceleration is reduced by 14.29%; and the RMS value is reduced by 21.05% under the input speed of 500 r/min and the load of 50 N·m. The research results can provide a theoretical basis for the study of dynamic characteristics, fault diagnosis, optimization of meshing parameters, and structural optimization of planetary reducers.

This paper systematically reviews research progress in semi-active suspension systems for agricultural machinery seats, focusing on key technologies and methods to enhance ride comfort. First, through an analysis of the comfort evaluation indicators and constraints of seat suspension systems, the current applications of variable stiffness and damping components, as well as semi-active control technologies, are outlined. Second, a comparative analysis of single control methods (such as PID control, fuzzy control, and sliding mode control) and composite control methods (such as fuzzy PID control, intelligent algorithm-based integrated control, and fuzzy sliding mode control) is conducted, with control mechanisms explained using principle block diagrams. Furthermore, key technical challenges in current research are summarized, including dynamic characteristic optimization design, adaptability to complex operating environments, and the robustness of control algorithms. Further research could explore the refinement of composite control strategies, the integrated application of intelligent materials, and the development of intelligent vibration damping technologies. This paper provides theoretical references for the optimization design and engineering practice of agricultural machinery suspension systems.

The work presented in this paper is to be read within the context of a connected autonomous vehicle (CAV). This context makes it possible to consider dividing the overall operational domain (operational design domain: ODD) of the vehicle into three sub-domains, relating to the areas of comfort (ODD1), road-holding (ODD2), and emergency situations (ODD3). Thus, based on information from the CAV’s proprioceptive and exteroceptive sensors, in addition to information from the infrastructure and other vehicles, supervision makes it possible, at any time, to identify the ODD in which the vehicle is located and to propose the most appropriate strategy, particularly for suspension control. Work already carried out by the authors made it possible to determine a crone sky hook (CSH) strategy for suspension control, 100% comfort-oriented for ODD1, a mixed crone sky hook—crone ground hook (CSH-CGH) strategy, oriented towards road-holding for ODD2, and a CGH strategy oriented towards safety for ODD3. In this paper, a comparative study focusing on security (ODD3) is presented. It concerns two versions of the CGH strategy (nominal CGHN and generalized CGHG). More precisely, for the comparative study to be meaningful, the control loops of the two versions have the same speed (iso-speed constraint), and the performance indices are normalized with respect to the values obtained in fault mode when the actuator is faulty. Notably, the CGHG version is part of the dynamics of fractional systems.

Space exploration and satellite communication demand lightweight, large-scale, and highly deployable structures. Inspired by the folding mechanism of frilled lizards and origami mechanisms, this study explores a deployable structure based on the single-vertex multi-crease origami (SVMCO) concept. The design focuses on crease distribution optimization to enhance deployment efficiency. A mathematical model analyzes the relationship between sector angles of three types of facets and structural performances, providing guidelines for achieving optimal deployment. Drawing from the rib patterns of frilled lizards, a rib support system for thick-panel mechanisms was designed and verified through a physical prototype. The structure achieves smooth-surface deployment with fewer supports, offering a lightweight and efficient solution for deployable systems.

This study proposes a Bayesian failure mode and effects analysis (FMEA)-based method for identifying critical faults and guiding maintenance decisions in stacker-automated stereoscopic warehouses, addressing the limited research on whole-machine systems and the interactions among fault modes. First, the hesitant fuzzy evaluation method was utilized to assess the influences of risk factors and fault modes in a stacker-automated stereoscopic warehouse. A hesitant fuzzy design structure matrix (DSM) was then constructed to quantify their interaction strengths. Second, leveraging the interaction strengths and causal relationships between severity, detection, risk factors, and fault modes, a Bayesian network model was developed to compute the probabilities of fault modes under varying severity and detection levels. FMEA was subsequently applied to evaluate fault risks based on severity and detection scores. Following this, fault risk ranking was conducted to identify critical fault modes and formulate targeted maintenance strategies. The proposed method was validated through a case study of Company A’s stacker-automated stereoscopic warehouse. The results demonstrate that the proposed approach can more objectively identify critical fault modes and develop more precise maintenance strategies. Furthermore, the Bayesian FMEA method provides a more objective and accurate reflection of fault risk rankings.

This research introduces a virtual reality (VR) training system for improving human–robot collaboration (HRC) in industrial disassembly tasks, particularly for e-waste recycling. Conventional training approaches frequently fail to provide sufficient adaptability, immediate feedback, or scalable solutions for complex industrial workflows. The implementation leverages Quest Pro’s body-tracking capabilities to enable ergonomic, immersive interactions with planned eye-tracking integration for improved interactivity and accuracy. The Niryo One robot aids users in hands-on disassembly while generating synthetic data to refine robot motion planning models. A Robot Operating System (ROS) bridge enables the seamless simulation and control of various robotic platforms using Unified Robotics Description Format (URDF) files, bridging virtual and physical training environments. A Long Short-Term Memory (LSTM) model predicts user interactions and robotic motions, optimizing trajectory planning and minimizing errors. Monte Carlo dropout-based uncertainty estimation enhances prediction reliability, ensuring adaptability to dynamic user behavior. Initial technical validation demonstrates the platform’s potential, with preliminary testing showing promising results in task execution efficiency and human–robot motion alignment, though comprehensive user studies remain for future work. Limitations include the lack of multi-user scenarios, potential tracking inaccuracies, and the need for further real-world validation. This system establishes a sandbox training framework for HRC in disassembly, leveraging VR and AI-driven feedback to improve skill acquisition, task efficiency, and training scalability across industrial applications.

The cage strength is a critical factor that constrains performance of high-speed deep groove ball bearing (DGBB) used in the drive motor of new energy vehicles. This paper presents a rigid-flexible coupling dynamic model for high-speed DGBBs, based on interactions dynamic of the flexible crown cage, balls, and rings. This study systematically analyzed the cage weaknesses in strength, and explored how factors such as the pocket clearance, claw length, modification radius and bottom thickness influence cage strength. In addition, an improved design aimed at enhancing cage strength was proposed. The results indicate that the cage strength is more sensitive to the inner-ring speed. Particularly, both the maximum stress and deformation in the radial direction increase sharply when the speed exceeds a threshold of 18,000 r/min. Additionally, an increase in the bearing rotational acceleration leads to a 45.7% rise in the cage stress. Furthermore, the sensitivity of the cage strength to temperature also escalates with bearing speed; the maximum stress and deformation increase by 5% to 16% at 80 °C compared to that obtained at 25 °C. Based on the structural influence on the cage strength, a structural improvement is proposed. With a pocket clearance of 0.23 mm, a claw length of 2.3 mm, a bottom thickness of 2.4 mm, and a shaping radius of 7.0 mm, the strength of the cage was evaluated both before and after the improvements. The results indicated that the enhanced cage exhibited superior strength.

Accurately modeling the flow curve over a large strain range is crucial for predicting the flow stress behavior of high silicon steel undergoing strain hardening in the continuous cold rolling process. This study proposes a large strain flow curve model for high-silicon steel, a material commonly used in the cores of electromagnetic devices such as electric motors, generators, and transformers. This model was developed through a series of tensile tests on homogenously pre-strained specimens. Pilot cold rolling was performed at various thickness reduction ratios to impart different magnitudes of pre-strain to sheet-type tensile specimens. The proposed flow curve model was implemented in a VUHARD user-defined subroutine within Abaqus/Explicit, and the predicted roll separating forces were compared with those measured from the pilot cold rolling tests. The comparison demonstrated that the proposed flow curve model accurately captures the flow stress behavior of high-silicon steel at different strain rates over a large strain range, with an R-squared value of 0.9932. The predicted roll separating forces closely matched the measurements from the pilot cold rolling tests, with an average difference of 5.1%.

The looper-tension control is a crucial aspect of a hot strip finishing mill. It involves a highly nonlinear system with strong states coupling and uncertainty, and the performance directly impacts the thickness deviation, which is the most critical product index. From the system dynamics, it is known that tension is highly sensitive to the strip velocity variation, which is typically unmeasurable. Instead, it needs to be calculated through work roll speed and strip slip which contains uncertainties, negatively affecting tension control performance. First, a feedback linearization-based proportional–integral (PI) controller design approach is proposed for the hot rolling looper-tension system. Second, to reduce the impact of speed uncertainties and enhance thickness response, an adaptive PI controller is introduced. Validation was conducted by numerical simulations; the result indicates that an adaptive PI controller reduces the magnitude of thickness variation and shortens the duration of its impact, verifying the consistency between theoretical derivation. The proposed control method effectively addresses the impact of uncertainties encountered in real-world applications. Additionally, it simplifies control parameter adjustment in practical use, reduces testing time, and improves product quality.

Citation: Balasuadhakar, A.; Kumaran, S.T.; Ali, S. Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model. Machines 2025, 13, 237. https:// Abstract: In hard milling, there has been a significant surge in demand for sustainable machining techniques. Research indicates that the Minimum Quantity Lubrication (MQL) method is a promising approach to achieving sustainability in milling processes due to its eco-friendly characteristics, as well as its cost-effectiveness and improved cooling efficiency compared to conventional flood cooling. This study investigates the end milling of AISI H11 die steel, utilizing a cooling system that involves a mixture of graphene nanoparticles (Gnps) and sesame oil for MQL. The experimental framework is based on a Taguchi L36 orthogonal array, with key parameters including feed rate, cutting speed, cooling condition , and air pressure. The resulting outcomes for cutting zone temperature and surface roughness were analyzed using the Taguchi Signal-to-Noise ratio and Analysis of Variance (ANOVA). Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model was developed to assess the impact of process parameters on cutting temperature and surface quality. The optimal cutting parameters were found to be a cutting speed of 40 m/min, a feed rate of 0.01 mm/rev, a jet pressure of 4 bar, and a nano-based MQL cooling environment. The adoption of these optimal parameters resulted in a substantial 62.5% reduction in cutting temperature and a 68.6% decrease in surface roughness. Furthermore , the ANFIS models demonstrated high accuracy, with 97.4% accuracy in predicting cutting temperature and 92.6% accuracy in predicting surface roughness, highlighting their effectiveness in providing precise forecasts for the machining process.

This study addressed the challenges of excessive eddy current losses and elevated thermal risks to permanent magnets in titanium alloy rotor sleeves for high-speed permanent magnet synchronous motors (HSPMSMs). Focusing on a 10 kW, 30,000 rpm high-speed motor, we innovatively propose incorporating insulating layers between axially laminated sleeve structures. Current research primarily mitigates eddy currents through the limited axial segmentation of sleeves/permanent magnets or radial shielding layers, while the technical approach of applying insulating coatings between laminated sleeves remains unexplored. This investigation demonstrated that compared with conventional solid sleeves, segmented sleeves, and carbon fibre sleeves, the laminated structure with a coordinated design of aluminium oxide and epoxy resin insulating layers effectively blocked the eddy current paths to achieve a substantial reduction in the sleeve eddy current density. This research concurrently highlights that the dynamic stress response and long-term operational reliability require further experimental validation. Subsequent investigations could explore optimised lamination patterns, parameter matching of insulating layers, and integration with emerging cooling technologies, thereby advancing synergistic breakthroughs in lightweight design and thermal management for high-speed motor rotors.

There has been a growth of collaborative robots in Industry 5.0 due to the research in automation involving human-centric workplace design. It has had a substantial impact on industrial processes; however, physical exertion in human workers is still an issue, requiring solutions that combine technological innovation with human-centric development. By analysing real-world data, machine learning (ML) models can detect physical fatigue. However, sensor-based data collection is frequently used, which is often expensive and constrained. To overcome this gap, synthetic data generation (SDG) uses methods such as tabular generative adversarial networks (GANs) to produce statistically realistic datasets that improve machine learning model training while providing scalability and cost-effectiveness. This study presents an innovative approach utilising conditional GAN with auxiliary conditioning to generate synthetic datasets with essential features for detecting human physical fatigue in industrial scenarios. This approach allows us to enhance the SDG process by effectively handling the heterogeneous and imbalanced nature of human fatigue data, which includes tabular, categorical, and time-series data points. These generated datasets will be used to train specialised ML models, such as ensemble models, to learn from the original dataset from the extracted feature and then identify signs of physical fatigue. The trained ML model will undergo rigorous testing using authentic, real-world data to evaluate its sensitivity and specificity in recognising how closely generated data match with actual human physical fatigue within industrial settings. This research aims to provide researchers with an innovative method to tackle data-driven ML challenges of data scarcity and further enhance ML technology’s efficiency through training on SD. This study not only provides an approach to create complex realistic datasets but also helps in bridging the gap of Industry 5.0 data challenges for the purpose of innovations and worker well-being by improving detection capabilities.

This work uses recurrence plots (RPs) to identify nonlinearities and non-stationary conditions in wind turbines. Traditionally, recurrence plots have been applied to vibration or acoustic data; this paper applies them to magnetometer and accelerometer data to compare the sensitivity. The recurrence plots are generated by plotting points in the phase space and identifying those points where the dynamic system returns to a similar configuration, meaning that the state variables are similar to previous conditions. The state variables for the acceleration data are the position and velocity, whereas, for the magnetometer data, they are the magnitude of the magnetic field and its integral. The time series are integrated by combining the shifting principle of harmonic functions and the empirical mode decomposition. The EMD method separates the original signal into several modes, shifts them, and combines them back. The time series were obtained from an accelerometer and a magnetometer mounted in a wind turbine. The results showed that the RP presents different patterns depending on the signal; magnetometer signals identify low-frequency components, such as magnetic field anomalies, and accelerometer signals identify high-frequency components, such as bearings and gears.

PolyJet™ 3D printing is an additive manufacturing (AM) technology from StratasysTM. It has been used for applications such as tissue mimicking, printing anatomical models, and surgical planning. The materials available from StratasysTM have the inherent capabilities of producing a number of PolyJet™ materials with a range of physical properties that can be utilized for representing realistic tissue behavior mechanically. The preset materials available in the PolyJet™ printing software version 1.92.17.44384 GrabCADTM Print allow the user to manufacture materials similar to biological tissue, but the combinations of possibilities are limited and might not represent the broad spectrum of all tissue types. The purpose of this study was to determine the combination of PolyJet™ materials that most accurately mimicked a particular biological tissue mechanically. A detailed Design of Experiment (DOE) methodology was used to determine the combination of material mixtures and printing parameters and to analyze their mechanical properties that best matched the biological tissue properties available in the literature of approximately 50 different tissue types. Uniaxial tensile testing was performed according to the ASTM standard D638-14 of samples printed from Stratasys J850 digital anatomy printer to their determined stress–strain properties. The obtained values were subsequently validated by comparing them with the corresponding mechanical properties of biological tissues available in the literature. The resulting model, developed using the DOE approach, successfully produced artificial tissue analogs that span a wide range of mechanical characteristics, from tough, load-bearing tissues to soft, compliant tissues. The validation confirmed the effectiveness of the model in replicating the diverse mechanical behavior of various human tissues. Overall, this paper provides a detailed methodology of how materials and settings were chosen in GrabCADTM Print software and Digital Anatomy CreatorTM (DAC) to achieve an accurate artificial tissue material.

To address the limitations of large silage machines in hilly and small-scale farming regions and the inefficiencies of existing small-scale crushing and baling machines, in this study, we developed an integrated silage crushing, shredding, and baling machine. Using discrete element software (EDEM 2022.3), the baling process of shredded straw was simulated, achieving a baled grass density of 140.067 kg/m3, meeting practical requirements. A three-factor, three-level experiment was conducted to evaluate the effects of the hammer blade quantity, blade length, and hammer angle on machine productivity and straw shredding rate. Performance data were analyzed using Design-Expert 10.0.7 software to develop regression models and assess the significance of each factor. The results indicated that productivity was most influenced by hammer blade quantity, followed by blade length and hammer angle, while the shredding rate was primarily affected by blade length, then hammer blade quantity, and hammer angle. The optimal configuration was identified as 32 hammer blades, a blade length of 99 mm, and a hammer angle of 14°. Validation experiments demonstrated a productivity of 2815.29 kg/h, a straw shredding rate of 94.28%, and a baled grass density of 124.52 kg/m3, closely aligning with the predicted values and confirming the reliability of the optimization.