Search Results (197)

Liquid crystal elastomers (LCEs) have shown great potential in the field of soft robotics due to their unique actuation capabilities. Despite the growing number of experimental studies in the soft robotics field, theoretical research remains limited. In this paper, a dynamic model of a bionic arm using an LCE fiber as artificial muscle is established, which exhibits periodic oscillation controlled by periodic illumination. Based on the assumption of linear damping and angular momentum theorem, the dynamics equation of the model oscillation is derived. Then, based on the assumption of linear elasticity model, the periodic spring force of the fiber is given. Subsequently, the evolution equations for the cis number fraction within the fiber are developed, and consequently, the analytical solution for the light-excited strain is derived. Following that, the dynamics equation is numerically solved, and the mechanism of the controllable oscillation is elucidated. Numerical calculations show that the stable oscillation period of the bionic arm depends on the illumination period. When the illumination period aligns with the natural period of the bionic arm, the resonance is formed and the amplitude is the largest. Additionally, the effects of various parameters on forced oscillation are analyzed. The results of numerical studies on the bionic arm can provide theoretical support for the design of micro-machines, bionic devices, soft robots, biomedical devices, and energy harvesters. Full article

This paper investigates the dependencies between the design parameters of the armature (stator) winding of a high-speed PMSM machine and the electrical losses in its windings resulting from eddy currents. In addition, the factors accounting for the occurrence of parasitic circulating currents, whose presence in the phase windings is associated with the design specificity, are analyzed. Quantitative analysis is carried out by the application of a newly developed mathematical model for the calculation of fundamental and additional losses in a multi-turn coil enclosed in the slots of a ferromagnetic core. The analysis takes into account the actual design of the slot and the conductor, the variable arrangement of individual conductors in the slot, the core saturation and the presence of the excitation field—to represent the main factors that affect the process of additional losses in the slot of the electric machine. The verification of the mathematical model developed in this study was carried out by comparing the distribution of power losses in the slot section of the coil, consisting of several elementary conductors connected in parallel and located in a rectangular open slot, with an identical distribution derived on the basis of an analytical method from the classical circuit theory. For the purpose of confirming the results and conclusions derived from simulation studies, a number of physical experiments were carried out, consisting in determining the power losses in multi-turn coils of different designs. Recommendations have been developed to minimize additional losses by optimizing the arrangement of conductors within the slot, selecting the appropriate cross-sectional size of a single conductor and the saturation level of the tooth zone. Full article

With the digital transformation of power systems, higher demands are being placed on smart grids for the timely and precise acquisition of the status of transmission and transformation equipment during operational and maintenance processes. When a transformer is energized under no-load conditions, an excitation inrush phenomenon occurs in the windings, posing a hazard to the stable operation of the power system. A machine learning approach is proposed in this paper for predicting the internal magnetic field of transformers under excitation inrush condition, enabling the monitoring of transformer operation status. Experimental results indicate that the mean absolute percentage error (MAPE) for predicting the transformer’s magnetic field using the deep neural network (DNN) model is 4.02%. The average time to obtain a single magnetic field data prediction is 0.41 s, which is 46.68 times faster than traditional finite element analysis (FEA) method, validating the effectiveness of machine learning for magnetic field prediction. Full article

Fatigue failure, generated by local multi-axial random state stress, frequently occurs in many engineering fields. Therefore, it is customary to perform experimental vibration tests for a structural durability assessment. Over the years, a number of testing methodologies, which differ in terms of the testing machines, specimen geometry, and type of excitation, have been proposed. The aim of this paper is to describe a new testing procedure for random multi-axial fatigue testing. In particular, the paper presents the experimental set-up, the testing procedure, and the data analysis procedure to obtain the multi-axial random fatigue life estimation. The originality of the proposed methodology consists in the experimental set-up, which allows performing multi-axial fatigue tests with different normal-to-shear stress ratios, by choosing the proper frequency range, using a single-axis exciter. The system is composed of a special designed specimen, clamped on a uni-axial shaker. On the specimen tip, a T-shaped mass is placed, which generates a tunable multi-axial stress state. Furthermore, by means of a finite element model, the system dynamic response and the stress on the notched specimen section are estimated. The model is validated through a harmonic acceleration base test. The experimental tests validate the numerical simulations and confirm the presence of bending–torsion coupled loading. Full article

Eddy current testing (ECT) has become a widely adopted technique for non-destructive testing (NDT) due to its effectiveness in detecting surface and near-surface defects in conductive materials. However, traditional methods mainly focus on defect detection and face significant challenges in extracting geometric information such as defect size and shape, which is crucial for structural health monitoring (SHM) and remaining useful life (RUL) assessment. To address these challenges, this study proposes a defect reconstruction approach based on a complex-valued convolutional neural network (CV-CNN), which directly leverages both amplitude and phase information inherent in complex-valued impedance signals. The proposed framework employs convolution, pooling, and activation operations specifically designed within the complex-valued domain to facilitate the high-fidelity reconstruction of defect morphology as well as precise multi-class defect classification. Notably, this approach processes the complete complex-valued signal without relying on prior structural parameters or baseline data, thereby achieving substantial improvements in both defect visualization and classification performance. Moreover, when compared to a complex-valued fully convolutional neural network (CV-FCNN), CV-CNN demonstrates a superior average classification accuracy of 85%, significantly outperforming the CV-FCNN model. Experimental results on carbon steel specimens with standard electrical discharge machining (EDM) notches under multi-frequency excitation confirm these advantages. This contribution provides a promising solution in the field of NDT for intelligent and precise defect detection. Full article

Hybrid excited machines are strong competitors for application in hybrid/full electric vehicles due to their high torque density and strong air gap field-regulating capability. Similar to armature back-EMF, back-EMF also exists in the field windings of hybrid excited machines. However, the existence of field back-EMF is harmful to the safe and stable operation of machine systems, e.g., lower efficiency, higher torque ripple, reduced control performance, etc. In this paper, the influence of the hybridization ratio k, i.e., the ratio of the field winding slot area to the total field slot area, on the field back-EMF in hybrid excited machines with a switched flux stator is comprehensively investigated. In addition, a comparative study of the field back-EMF ripple in hybrid excited machines and wound field synchronous machines is conducted. It shows that the field back-EMF in flux-enhancing, zero field current, and flux-weakening modes is significantly affected by the hybridization ratio under different conditions. Moreover, the on-load field back-EMF in wound field machines is considerably higher than that in hybrid excited machines due to the mitigated magnetic saturation level in the field winding’s magnetic flux path. Finally, to validate the results predicted using the finite element method, a prototype hybrid excited machine is built and tested. Full article

During field operations, the potato harvester is subjected to multiple sources of excitation, including internal vibratory mechanisms and field surface excitation, resulting in significant vibrations in the frame. Based on the physical parameters of the harvester’s internal structure and the connection parameters between components, a 12-degree-of-freedom (12-DOF) dynamic model of the entire machine was constructed. The corresponding simulation model was created in a MATLAB/Simulink environment to analyze the vibration characteristics of each component during harvesting operations. The comparison between actual and simulated signals shows that the RMS error of acceleration is only 2.42%, indicating that introducing two degrees of freedom in pitch and roll directions to the potato harvester can accurately describe its vibration characteristics. On this basis, the Bayesian optimization algorithm was used to obtain optimal connection parameters. The optimization results demonstrate a 0.85 m/s² and 0.45 m/s² increase in RMS values for the soil-cutting disc and lifting chain, respectively, effectively enhancing the harvester’s work efficiency, while the frame exhibited a 0.31 m/s² reduction in RMS value, significantly improving structural stability. This study provides a theoretical foundation for the parameter optimization of large-scale agricultural machinery. Full article

Currently, electric machines predominantly rely on costly rare-earth NdFeB magnets, which pose both economic and environmental challenges due to rising demand. This research explores recent advancements in machine topologies and magnetic materials to identify and assess promising solutions to this issue. The study investigates two alternative machine topologies to the conventional permanent magnet synchronous machine (PMSM): the permanent magnet-assisted synchronous reluctance machine (PMaSynRM), which reduces magnet usage, and the wound-field synchronous machine (WFSM), which eliminates magnets entirely. Additionally, the potential of ferrite and recycled NdFeB magnets as substitutes for primary NdFeB magnets is evaluated. Through detailed simulations, the study compares the performance and cost-effectiveness of these solutions against a reference permanent magnet synchronous machine (PMSM). Given their promising performance characteristics and potential to reduce or eliminate the use of rare-earth materials in next-generation electric machines, it is recommended that future research should focus on novel topologies like hybrid-excitation, axial-flux, and switched reluctance machines with an emphasis on manufacturability and also novel magnetic materials such as FeN and MnBi that are currently seeing synthesis challenges. Full article

Rice combine harvester conveyor troughs and their bolted connections are susceptible to vibration-induced failure due to operational and environmental excitations. Addressing the challenge of predicting the state of the combine harvester’s conveyor trough bolted structure prior to vibration-induced failure, this study addresses this by investigating signal analysis, system design, and condition identification for these critical components. Firstly, multi-point vibration signals from the conveyor trough were acquired and analyzed in the time-frequency domain. The analysis pinpointed the X-direction at the trough-frame connection (Point 5) as the most responsive location, with RMS peaking at 6.650 during header start-up (vs. 0.849 idle). Significant responses were also noted at Point 3 (Y-dir, 4.628) and Point 6 (X-dir, 3.896) under certain conditions (where Z-direction responses were minimal), identifying critical points that form the basis for condition assessment. Secondly, a vibration acquisition system was developed using a high-performance AD7606 ADC and A39C wireless technology. It features 16-bit resolution (0.00076 mm/s theoretical sensitivity), 8-channel synchronous sampling up to 200 kSPS, and rapid (0.8 s) wireless data transmission. This system meets the demands for high-frequency, high-precision monitoring of the bolted structure. Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. Using a one-vs.-one strategy and data from critical points, an operational condition identification model was developed. Validation with field data confirmed high accuracy (96.9–99.7%) for principal states and low misclassification rates (<5%). This allows for precise identification of the bolted structure’s working status. The research presented in this study offers effective methodologies and technical underpinning for the condition monitoring of critical structural components in rice combine harvesters. Full article

The PCHE, as an efficient heat exchanger, plays a crucial role in the storage and regasification of LNG. However, among the existing studies, those that integrate this field with deep learning are scarce. Moreover, research on explainability remains insufficient. To address these gaps, this study first constructs a dataset of heat transfer coefficients (h) through numerical simulations. Pearson correlation analysis is employed to screen out the most influential features. In terms of predictive modeling, the study compares five traditional machine learning models alongside deep learning models such as long short-term memory neural networks (LSTMs), gated recurrent units (GRUs), and Transformer. To further enhance prediction accuracy, three attention mechanisms—self-attention mechanism (SA), squeeze-and-excitation mechanism (SE), and local attention mechanism (LA)—are incorporated into the deep learning models. The experimental results demonstrate that the artificial neural network achieves the best performance among the traditional models, with a prediction accuracy for straight-path h reaching 0.891799 (R²). When comparing deep learning models augmented with attention mechanisms against the baseline models, both LSTM–SE in the linear flow channel and Transformer–LA in the hexagonal flow channel exhibit improved prediction accuracy. Notably, in predicting the heat transfer coefficient of the hexagonal channel, the determination coefficient (R²) of the Transformer–LA model reaches 0.9993, indicating excellent prediction performance. Additionally, this study introduces the SHAP interpretable analysis method to elucidate model predictions, revealing the contributions of different features to model outputs. For instance, in a straight flow channel, the hydraulic diameter (Dh) contributes most significantly to the model output, whereas in a hexagonal flow channel, wall temperature (Tinw) and heat flux (Qw) play more prominent roles. In conclusion, this study offers novel insights and methodologies for PCHE performance prediction by leveraging various machine learning and deep learning models enhanced with attention mechanisms and incorporating explainable analysis methods. These findings not only validate the efficacy of machine learning and deep learning in complex heat exchanger modeling but also provide critical theoretical support for engineering optimization. Full article

Wheat is one of the most essential food crops globally, but diseases significantly threaten its yield and quality, resulting in considerable economic losses. The identification of wheat diseases faces challenges, such as interference from complex environments in the field, the inefficiency of traditional machine learning methods, and difficulty in deploying the existing deep learning models. To address these challenges, this study proposes a multi-scale feature fusion shuffle network model (MFFSNet) for wheat disease identification from complex environments in the field. MFFSNet incorporates a multi-scale feature extraction and fusion module (MFEF), utilizing inflated convolution to efficiently capture diverse features, and its main constituent units are improved by ShuffleNetV2 units. A dual-branch shuffle attention mechanism (DSA) is also integrated to enhance the model’s focus on critical features, reducing interference from complex backgrounds. The model is characterized by its smaller size and fast operation speed. The experimental results demonstrate that the proposed DSA attention mechanism outperforms the best-performing Squeeze-and-Excitation (SE) block by approximately 1% in accuracy, with the final model achieving 97.38% accuracy and 97.96% recall on the test set, which are higher than classical models such as GoogleNet, MobileNetV3, and Swin Transformer. In addition, the number of parameters of this model is only 0.45 M, one-third that of MobileNetV3 Small, which is very suitable for deploying on devices with limited memory resources, demonstrating great potential for practical applications in agricultural production. Full article

Modeling many-body quantum systems is widely regarded as one of the most promising applications for near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials science or strongly correlated systems. A promising avenue of research is to combine many-body physics with machine learning for the classification of distinct phases. We present a workflow that synergizes quantum computing, many-body theory, and quantum machine learning (QML) for studying strongly correlated systems. In particular, it can capture a putative quantum phase transition of the stereotypical strongly correlated system, the Hubbard model. Following the recent proposal of the hybrid quantum-classical algorithm for the two-site dynamical mean-field theory (DMFT), we present a modification that allows the self-consistent solution of the single bath site DMFT. The modified algorithm can be generalized for multiple bath sites. This approach is used to generate a database of zero-temperature wavefunctions of the Hubbard model within the DMFT approximation. We then use a QML algorithm to distinguish between the metallic phase and the Mott insulator phase to capture the metal-to-Mott insulator phase transition. We train a recently proposed quantum convolutional neural network (QCNN) and then utilize the QCNN as a quantum classifier to capture the phase transition region. This work provides a recipe for application to other phase transitions in strongly correlated systems and represents an exciting application of small-scale quantum devices realizable with near-term technology. Full article

The combine harvester, as a multi-component machine comprising a cutting table, a conveyor, a threshing cylinder, and other components, experiences significant stress and bolt failures in cutting table-conveyor structures due to inherent excitation and the cutting table’s cantilevered design. To address bolt loosening monitoring in the critical joint, this paper designed a Wheatstone bridge circuit-based wireless monitoring system and a multi-channel Wheatstone bridge sensor, enabling multi-bolt monitoring on combine harvesters. Utilizing LoRa wireless communication, the system effectively overcomes the wiring complexity and deployment difficulties of traditional agricultural machinery bolt monitoring systems. The Wheatstone bridge sensor can precisely monitor pre-tightening forces up to 150 kN for M12–M24 bolts. A calibration test based on dynamic time warping (DTW) accurately fitted the sensor’s response to pressure and displacement with determination coefficients of 0.9780 and 0.9753. Then, a validation test focusing on connection bolts revealed a 95.12% overlap between the simulated measurement range and the calibration range under pre-tightening conditions. Furthermore, fitting curves for simulated measurements against tightening torque and angle yielded coefficients of determination of 0.9945 and 0.9939, which demonstrated accurate fitting of pre-tightening conditions and defined the monitoring range of 3.02 × 10¹² to 3.49 × 10¹². Finally, combined with simulation results, a field performance test confirmed the sensor’s ability to detect minute 5% pre-load reductions, achieve 200 ms data transmission to a host computer, and maintain lossless data transmission over 1.2 km. This sensor and system design provided a valuable reference for bolt loosening monitoring in combine harvesters and other agricultural machinery. Full article

Quantum Machine Learning (QML) opens up exciting possibilities for tackling problems that are incredibly complex and consume a lot of time. The drive to make QML a reality has sparked significant progress in material science, inspiring a growing number of research publications in the field. In this study, we extracted articles from the Scopus database to understand the contribution of material science in the advancement of QML. This scientometric analysis accumulated 1926 extracted publications published over 11 years spanning from 2014 to 2024. A total of 55 countries contributed to this domain of QML, among which the top 10 countries contributed 65.7% out of the total number of publications; the USA is on top, with 19.47% of the publications globally. A total of 57 authors contributed to this research area from 55 different countries. From 2014 to 2024, publications had an average citation impact of 32.12 citations per paper; the year 2015 received 16.7% of the total citations, which is the highest in the 11 years, and the year 2014 had the highest number of citations per paper, which is 61.4% of the total. The study also identifies the most significant document in the year 2017, with the source title Journal of Physics Condensed Matter, having a citation count of 2649 and a normalized citation impact index (NCII) of 91.34. Full article

This paper proposes an E-core outer-rotor hybrid-excitation flux switching (OR-HEFS) machine for in-wheel direct driving application. According to the general air gap field modulation theory, the magneto-motive force (MMF) permeance model was established to investigate the air gap flux density, and then the torque generation, the flux regulation principle, and the excitation-winding-induced voltage of the E-core OR-HEFS machine were analyzed. To characterize the output performances, the influence of the design parameters was investigated for the E-core OR-HEFS machine, including the split ratio, stator tooth arc, PM arc, fault-tolerant tooth arc, rotor tooth arc, stator yoke width and rotor yoke width. The performances contained the output torque, torque ripple, flux regulation ratio, and the excitation-winding-induced voltage. On this basis, the aforementioned four performances were optimized by means of the non-dominated sorting genetic algorithm II (NSGA-II). Based on the optimization result, a prototype was manufactured and tested to verify the whole investigation of this paper. Full article