Reluctance actuators (RAs) excel in ultraprecision motion stages because of their high force density. However, high-fidelity force tracking control is significantly hindered by complex coupled nonlinearities, including variable-gap flux linkage, hysteresis, eddy currents, and flux leakage. Accurate inverse modeling is indispensable for counteracting these effects to enable precise control. Existing identification methods typically rely on additional sensing equipment and off-machine fixtures. Moreover, the sensitivity of model parameters to mover–stator position renders off-machine calibration vulnerable to reassembly errors. This paper proposes a comprehensive hybrid-driven inverse modeling and sensorless on-machine identification framework. First, an integrated analytical structure unifying multisource nonlinearities into a hybrid-driven model that ensures physical interpretability and high accuracy is presented. Second, a sensorless in situ identification strategy calibrates parameters without auxiliary metrology, hardware disassembly, or manual adjustments. By designing specific control laws and trajectories, the method utilizes only inherent displacement feedback to decouple and identify complex nonlinearities in situ. Consequently, the inverse model evolves from a coarse nominal classical lumped-parameter representation into a high-precision hybrid-driven model. Multiple force and motion control experiments are designed to demonstrate the proposed method’s repeatability and advantages over existing methods, verifying its effectiveness in enhancing high-precision feedforward controller performance and further approaching the accuracy limit, thereby aiding RA control research.

A high thrust-to-weight ratio imposes stricter requirements on thermal barrier coating for gas turbine blades. High-entropy pyrochlore oxides are particularly attractive due to their excellent performance. In this paper, a series of high-entropy oxides with the general formula La-2(Yb0.25(1-x)Y0.25(1-x)ZrxNb0.25(1-x)Ta0.25(1-x))(2)O-7 (x = 0 similar to 0.3) were designed and fabricated, and their phase composition, microstructure, and key properties were investigated. With increasing Zr4+ content, the phase composition transitions from a mixture of pyrochlore and secondary phase to a dual-phase pyrochlore, and finally to single-phase high-entropy pyrochlore oxides. Single-phase HEPOs La-2(Yb0.25(1-x)Y0.25(1-x)ZrxNb0.25(1-x)Ta0.25(1-x))(2)O-7 (x = 0.2 similar to 0.3) possess low thermal conductivity, a high coefficient of thermal expansion, and excellent high-temperature phase stability. Specifically, La-2(Yb0.1875Y0.1875Zr0.25Nb0.1875Ta0.1875)(2)O-7 exhibits a thermal conductivity as low as 0.96 W/(m & centerdot;K), and a coefficient of thermal expansion of 9.8 & times; 10(-6) K-1. After 120 h of heat treatment at 1450 degrees C, its grain growth rate is only 11.69%. These properties indicate that La-2(Yb0.1875Y0.1875Zr0.25Nb0.1875Ta0.1875)(2)O-7 has great potential for applications in the field of thermal barrier coating.

The pursuit of high lithography throughput necessitates dual-stage reticle systems that can achieve high scanning acceleration and long strokes, while maintaining nanometric positioning accuracy. Reluctance actuators (RAs) are considered ideal candidates for next-generation motion systems due to their high-force densities. However, conventional dual-stage control strategies predominantly employ a primary-secondary following mode, which strictly limits the relative displacement between the coarse and fine stages. This prevents the system from exploiting the nonlinear force surge capability of RAs with small air gaps, bottlenecking the overall system acceleration at the saturation limit of the coarse stage. To overcome this, a synergistic framework integrating nonlinear trajectory planning with a decoupled control architecture is proposed. This approach surpasses physical acceleration limits by optimizing the relative displacement to exploit the variable-gap force gain of the RA and breaking traditional kinematic constraints via decoupled control. Furthermore, a supporting in situ parameter identification method for RAs in a closed-loop dual-stage environment is developed to meet the stringent modeling accuracy requirements. Experimental validation demonstrates an acceleration improvement of up to 36.2% in the scanning acceleration. This is achieved without additional hardware costs, and while maintaining tracking errors below 10 nm during scanning. Overall, a novel approach is offered for high-performance lithography stage design.

Reluctance actuators (RAs) have been widely adopted in high-acceleration precision electromechanical systems such as lithography reticle stages due to their high thrust density and compact configuration. For system-level RA design, a multi-physics methodology balancing computational flexibility and predictive accuracy is essential to address bidirectional actuator-structure interactions. Current RA design methodologies predominantly focus only on electromagnetic performance optimization while neglecting thermal constraints in precision systems, particularly the thermal impact on external structures during continuous operation. To address this challenge, this paper proposes a bidirectional co-optimization framework integrating data-driven and physics-based approaches. Firstly, a high-fidelity structure-thermal surrogate model, neural network-LPTN hybrid network (NNLN), that synergizes data-driven and physics-based modeling approaches is proposed. This hybrid architecture achieves high-precision scalable thermal modeling while preserving physical interpretability, enabling dynamic coupling with external thermal models and effectively resolving the inherent trade-off between accuracy and scalability in conventional approaches, demonstrating over 50

Metadevices have emerged as a new element or system in recent years, from optics to mechanical science, showing superior performance and powerful application potential. In this study, a mechanical metadevice that capable of low-frequency vibration isolation, which is called metamaterial springs or metasprings, is proposed. Meanwhile, a modular design method is reported to obtain the customizable quasi-zero stiffness characteristic of the designed metaspring. As proof-of-concept, we demonstrate, both in simulations and experiments, the quasi-zero stiffness characteristics of the proposed metasprings using 3D-printed experimental specimens. Moreover, the low-frequency vibration isolation properties of the proposed metasprings is demonstrated both in vibration tests and automotive vibration tests. This work provides a new mechanical metadevice, that is, metasprings for low-frequency vibration isolation, as well as a modular design method for designing metasprings, which may revolutionize vibration isolation devices in the field of low-frequency vibration isolation.

Reaching mode, which guides system states toward the sliding mode surface, is a crucial aspect of sliding mode control. This paper proposes an optimal reaching filter designed to achieve an optimal reaching mode while accounting for control input constraints. The optimal reaching law is formulated as an optimization problem to ensure efficient and constrained control execution. Using a double integrator system with a linear sliding mode surface as a representative example, the effectiveness and superiority of the proposed approach are validated through rigorous theoretical analysis and simulation. Additionally, an experimental setup is developed for further verification, with comparative results demonstrating that the proposed method significantly enhances the speed at which system states converge to the sliding mode surface.

There are lots of actual factors that cause the thrust nonlinearity (or thrust fluctuation) of permanent magnet synchronous linear motor (PMSLM), which increases the difficulty of controller design for precision motion. The existing methods to reduce thrust nonlinearity include structure optimization, mathematical model improvement, and the use of intelligent controllers, all of which have limitations. This article proposes a novel drive method to calculate and assign the coil currents which can reach higher thrust linearity so that provides the high-quality controlled plant. It makes PMSLM output accurate thrust by modeling and identifying the thrust coefficient of each phase coil separately, and calculating the corrected coil currents in real-time. This method does not require changes to the existing motor structure, does not rely on the nominal motor model, and its correction algorithm runs in the drive instead of the controller, hence it has the advantages of low cost, high precision, and fast dynamic response. The experiment results show its ability to identify the high-order harmonics and the nonperiodic component of the thrust coefficient and the detent force, significantly reducing the thrust nonlinearity and the controlling error of precision trajectory tracking.

Active damping is important for using input filters to suppress resonance in permanent magnet synchronous motors (PMSM), but additional sensors are often required. A new multivariate reconstruction (MVR) method is proposed, which is based on the current of inverter and can estimate the current and voltage of the motor with high accuracy and strong robustness, thereby achieving active damping without additional current or voltage sensors. The accuracy and robustness of the proposed MVR in damping systems were tested. Experiment was carried out to test the effectiveness of MVR.

This paper presents a framework utilising digital twins for predictive maintenance planning of fuel cells in electric vehicles, focusing on real-time condition monitoring and Remaining Useful Lifetime (RUL) prediction. By integrating advanced algorithms, it optimises maintenance schedules to reduce downtime and extend fuel cell lifetime. Despite relying on simulated data, the findings highlight the potential of digital twins to improve fuel cell reliability, and sustainability, illustrating their transformative impact on smart urban transportation systems.

Magnetic levitation (maglev) planar motor has broad application prospect because of its excellent performance in many aspects. In this article, a 6-DOF extended unified wrench model (6-DOF EUWM) is proposed to describe the wrench of maglev planar motor more accurately with low computational consumption for real-time calculations. The proposed 6-DOF EUWM can express the wrench on the coil in arbitrary coil–magnet configuration analytically as a function of 6-DOF displacement, and does not require simplified coil modeling. Specifically, the rotational magnetic flux is linearly expressed by Taylor series, and the wrench is then calculated based on Lorentz's law. After that, the 6-DOF displacement parameters are decoupled from the coil-shaped integral parameters by triangular equation transformation to realize the 6-DOF analytical expression. Numerical validation experiments show that the proposed 6-DOF EUWM is accurate, can describe the end effect well, and has low computational consumption. Motion control experiments on maglev planar motor show that the proposed 6-DOF EUWM can improve the motion performance of each axis of the planar motor due to accurate modeling. The proposed 6-DOF EUWM is suitable for planar motor design as well as real-time motion control, and will facilitate the maglev planar motor for industrial applications.

Laser far-field focus measurement is an important method for measuring laser beam power. The measurement of optical power is a key problem in many fields, such as laser technology, metrology, etc. In multi-step phase recovery on for high-precision measurement of the position and size of the far-field focus. The method can be divided into two steps: firstly, the spatial intensity distribution of laser beam is calculated by using the fast Fourier transform (FFT) algorithm; Secondly, it uses laser far-field focus measurement to measure the size of laser beam spot on an object. This method is based on phase recovery technology, which uses two or more measurements to determine the size of the object. The first measurement is carried out at a certain distance from the object, and then another measurement is carried out at a relatively close distance from the same object. If the size between these two measurements does not change, it can be assumed that the size of the focus itself does not change.

Lithium-ion batteries (LIBs) are widely used in many fields, such as electric vehicles and energy storage, and directly impact the device performance and safety. Therefore, the state of health (SOH) assessment is critical for LIB usage. However, most of the existing data-driven SOH modeling methods overlook the inherent uncertainty in battery health prediction, which decreases the reliability of the model. To address this issue, this paper proposes a novel SOH assessment model based on the deep learning framework. The SOH results are derived from the quantile distribution of deep features, giving the SOH values with associated confidence intervals. This enhances the reliability and generalization of SOH assessment results. Additionally, to complete the optimization of the deep model, a Wasserstein distance-based quantile Huber (QH) loss function is developed. This function integrates Huber loss and quantile regression loss, enabling the model to be optimized based on a distribution output. The proposed method is validated using the NASA dataset, and the results confirm that the proposed method can effectively estimate the SOH of LIB while accounting for uncertainty. The incorporation of SOH distribution enhances the reliability and generalization ability of the SOH assessment model.

In recent years, various biodiesels have been developed to decrease pollutant emissions from compression ignition engine. However, the current research focuses on reducing the pollutant components without considering the mechanical vibration that occurred due to the changes in fuel properties such as viscosity, calorific values, density, and bulk modulus. It is important to explore the relationships between fuel properties and engine vibration. Mechanical vibration could cause power loss and affect the lifetime of the engine. In this investigation, a lister-pitter 3-cylinder diesel engine was used to analyse the mechanical vibration of three different fuels including diesel, waste cooking oil biodiesel (WCOB), and lamb fat biodiesel (LFB). The high-frequency vibration sensors were mounted on the cylinder head to monitor and assess the vibration performance. The vibration data were collected under various operating conditions including varying engine speed from 1500 to 2000 rpm and varying engine loads ranging from 20% to 100%. Three practical assessment features of vibration signals were investigated to evaluate the vibration characteristics. The experimental results clearly demonstrate the relative relations between vibration, and fuel properties of the tested fuels, used in the diesel engine. Compared with fossil diesel fuel, the total vibration level decreased by 17% and 23% for WCOB and LFB fuels, respectively. The engine performance powered with LFB and WCOB are better than diesel’s effect on both vibration and friction power (FP) perspective. Superior lubricity and viscosity of WCOB and LFB is the main reason causing good vibration performance.

Abstract. Laser interferometers and grating interferometers based on optical interferometry are widely used in displacement measurement of precision machining and testing equipment, such as the measurement system of integrated circuit equipment, due to their high precision, noncontact, and large dynamic measurement range. The ghost reflection in optical elements may lead to the periodic nonlinear error of the interferometer and also reduce alternating current/direct current. We propose a general method for automatic ghost reflection interface identification. It can analyze the influence weight of ghost reflection for each interface of any interferometer. In addition, the manufacturing cost of the interferometer is effectively reduced by optimization algorithms that enable ghost reflection avoidance in the interferometer design. Experimental results prove the influence weight of ghost reflection at different positions in the interferometer and provide the parameter selection of the most suitable interface reflection of the interferometer.

The vehicle landing process is simulated by dropping the air-dropped vehicle, establishing the vehicle finite element model based on the drop test and verifying the accuracy of the model, and deriving the weak components in the vehicle drop process by transient response analysis of LS-DYNA. Based on the complete restart technology, we propose the simulation method of cumulative damage of vehicle fall and realize the simulation analysis of multiple vehicle falls and the cumulative analysis of vehicle stress state and plastic deformation. Finally, the Johnson–Cook failure damage model was combined to calculate the cumulative damage caused by the fall impact; the variation law of the damage was derived, and the structural damage was evaluated according to the failure model, which has certain guiding significance for the study of the cumulative damage of vehicle airdrop.

To improve the maneuvering stability of uncrewed armored vehicles during emergency steering and obstacle avoidance under high-speed driving conditions in off-road undulating terrain, in this paper, we establish a 7DOF dynamics model based on the time-varying trajectory curvature for the lateral and vertical motion of the spring-loaded mass and the vertical jump of the unsprung mass. Also, we consider two slip motion states with tires in the linear zone and tire slip nonlinear zone based on the Dugoff model and use trip and non-trip rollover under random road input as rollover warning constraint, and conduct flat road DLC test, 20° side slope test, JLTV twisted road respectively by Carsim-Simulink jointly according to NATO double shift line test standard. The improved LTV-MPC algorithm solves the optimal tracking control sequences of 2DOF and 7DOF kinetic models, and the trajectory tracking is completed, compared, and analyzed.

With the increasing demand of mechanical stiffness performance and electromagnetic performance of maglev planar motor, especially the moving-magnet motor with Halbach array, an integrated optimization with 3D structural optimization and magnet parameter optimization is proposed and executed in this paper. The macroscopic electromagnetic performances consisting of thrust-mass ratio and power dissipation are taken as the optimization objective, which improves the performance more directly and comprehensively than traditional optimization methods. A streamlined dual-loop optimization framework is constructed and proved to be able to reduce the computational consumption by tens of times compared to the initial conventional framework, authentically guaranteeing a smooth integrated optimization. At the outer loop of the optimization framework, this paper proposes an oriented strategy for the individuals of Genetic Algorithm (GA) to quickly meet the strict constraints. The integrated optimization method solves the parameter coupling problem between electromagnet and stiffness. Compared to existing optimized structure, the resulting structure improves the thrust-mass ratio by 7.7%, power dissipation by 12.8% and natural frequency by 16.6%, respectively. Mechatronic experimental results in maglev planar motor systems show that the proposed integrated optimization can significantly improve the control bandwidth and reduce the current consumption, laying the foundation for better performance in practical applications.& COPY; 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Sustainable manufacturing is of great importance in today’s world. In manufacturing, keep industrial equipment well-functioning is important because failure of equipment leads to significant financial and production losses. In addition, disposal of such failed equipment is both costly and environmentally unfriendly and does not recover any residual value. This raises the need to adopt methods and means that help extending the life of equipment and reduce waste of material. This paper presents a digital toolkit of cost model to estimate and understand the costs to be incurred when applying life extension strategy for industrial equipment. It is meant to be integrated with other tools and methodologies to enable end-users to perform optimal decision-making regarding which life extension strategy (e.g., remanufacturing, refurbishment, repair) to implement for large industrial equipment that is towards its end-of-life or needs maintenance, taking into account criteria such as cost, machine performance, and energy consumption. The cost model developed integrates a combination of parametric costing and activity-based costing methods to per form cost estimation. It has been implemented in an Excel-based Macro platform. A case study with application scenarios has been conducted to demonstrate the application of the cost model to optimize life extension strategies for industrial equipment. Finally, conclusions on the developed cost model have been reported.