Search Results (371)

Frequency-modulated continuous-wave (FMCW) LiDAR systems are extensively utilized in industrial metrology, autonomous navigation, and geospatial sensing due to their high precision and resilience to interference. However, the intrinsic nonlinear dynamics of laser systems introduce significant distortion, adversely affecting measurement accuracy. Although conventional iterative pre-distortion correction methods can effectively mitigate nonlinearities, their long-term reliability is compromised by factors such as temperature-induced drift and component aging, necessitating periodic recalibration. In light of recent advances in artificial intelligence, deep reinforcement learning (DRL) has emerged as a promising approach to adaptive nonlinear compensation. By continuously interacting with the environment, DRL agents can dynamically modify correction strategies to accommodate evolving system behaviors. Nonetheless, existing DRL-based methods often exhibit limited adaptability in rapidly changing nonlinear contexts and are constrained by inefficient uniform experience replay mechanisms that fail to emphasize critical learning samples. To address these limitations, this study proposes an enhanced Soft Actor-Critic (SAC) algorithm incorporating a hybrid prioritized experience replay framework. The prioritization mechanism integrates modulation frequency (MF) error and temporal difference (TD) error, enabling the algorithm to dynamically reconcile short-term nonlinear perturbations with long-term optimization goals. Furthermore, a time-varying delayed experience (TDE) injection strategy is introduced, which adaptively modulates data storage intervals based on the rate of change in modulation frequency error, thereby improving data relevance, enhancing sample diversity, and increasing training efficiency. Experimental validation demonstrates that the proposed method achieves superior convergence speed and stability in nonlinear correction tasks for FMCW LiDAR systems. The residual nonlinearity of the upward and downward frequency sweeps was reduced to $1.869\times {10}^{-5}$ and $1.9411\times {10}^{-5}$, respectively, with a spatial resolution of 0.0203m. These results underscore the effectiveness of the proposed approach in advancing intelligent calibration methodologies for LiDAR systems and highlight its potential for broad application in high-precision measurement domains. Full article

During the operation of steel cable-driven radiation-resistant robots in nuclear industrial environments, the tensile force of a steel cable is influenced by temperature variations, which can cause significant detection errors. To address this problem, this study proposes a temperature-compensated axial force characterization method for steel cables based on the magnetoelastic effect, aiming to ensure the measurement accuracy of magnetoelastic sensors. The principle of the magnetoelastic measurement method involves magnetizing the steel cable. When subjected to tensile forces, the magnetization characteristics of the steel cable change, thereby altering the detection signal of the magnetoelastic sensor. By analyzing the relationship between steel cable tension and variations in the detection signal, effective force measurement can be achieved. First, experiments are conducted to investigate the influence of temperature on the detection signals of a magnetoelastic sensor under zero-load conditions. Then, additional tests are performed to examine the combined effects of a tensile force and temperature on the sensor’s signals. Finally, based on the experimental data, axial force prediction models are constructed using both surface fitting and a backpropagation neural network (BPNN). The results demonstrate that, compared to the resistance values, inductance exhibits superior stability under temperature variations. In the temperature range of 20–50 °C, the inductance variation is approximately 0.15 μH, which indicates improved suitability for characterizing the axial force of steel cables. It is also shown that under isothermal conditions, the inductance increases linearly with the applied tensile force, exhibiting a slope of approximately 0.025 μH/kN. Both the surface fitting-based and BPNN-based axial force prediction models demonstrate high accuracy, with absolute prediction errors consistently below 5% compared to actual data. Full article

Accurate and real-time monitoring of low-frequency electromagnetic field (EMF) is essential in power and industrial environments, yet most conventional approaches still suffer from limited spatial coverage, manual operation, and insufficient digitization. To address these challenges, this paper proposes an intelligent EMF monitoring system that integrates six-axis magnetic field sensing, temperature compensation, vector synthesis, Sub-1 GHz wireless communication, and real-time data visualization. The system supports simultaneous measurement of both AC and DC magnetic fields across the 30 Hz–100 kHz range, with specific optimization for power-frequency conditions (50/60 Hz). Designed with modular integration and low power consumption, it is suitable for portable deployment in field scenarios. Comprehensive laboratory and substation tests demonstrate high accuracy, with maximum measurement errors of 1.17% under zero-field and 1.42% under applied-field conditions—well below the ±5% tolerance defined by international standards. Wireless performance tests further confirm stable long-distance communication, achieving ranges of up to 5 km without significant transmission errors, while overall system measurement error reached as low as 0.015%. These results verify the system’s robustness, fidelity, and compliance with international safety standards. Overall, the proposed platform provides a practical and scalable solution for intelligent EMF monitoring, offering strong potential for deployment in industrial environments and infrastructure-critical applications. Full article

Municipal solid waste incineration (MSWI) plays a key role in advancing environmental sustainability. However, the current main furnace temperature control methods are difficult to solve the problems of strong coupling, equipment wear, and frequent disturbances. To solve the above problems, in this article, we propose a static and dynamic hybrid event-triggering mechanism-based interval type-II fuzzy broad adaptive compensation model predictive control (SDHETM-IT2FB-ACMPC). Firstly, a furnace temperature prediction model based on the interval type-2 fuzzy broad learning system (IT2FBLS) is constructed, and the IT2FB-MPC method is obtained, which solve the problem of variable coupling. Secondly, DETM based on historical error information is designed using sliding window method and combined with SETM to form SDHETM to drive the update of control variable to reduce the problem of equipment wear. Finally, the adaptive compensation control law of the adaptive compensation optimization control (ACOC) algorithm can compensate for the influence of the disturbance and the event-triggered mechanism on the control effect, and overcome the problem of frequent disturbances. Experimental results show that the proposed method reduces ISE to 0.2821, IAE to 0.1930, and DEV^max to 6.6269—reductions of 79%, 59%, and 8% compared to traditional NMPC—while cutting control actions by 71%. The results prove that IT2FB-MPC has excellent control performance for furnace temperature, and that SDHETM and ACOC can effectively reduce the triggering times and effectively compensate for the influence caused by disturbances and the lack of control variable updates. The proposed method successfully solves the control difficulties of furnace temperature in the MSWI process. Full article

The thermal deformation of high-precision CNC machine tools has long been a significant barrier to improving machining accuracy. Accurately characterizing the thermal properties of the spindle, especially the convective heat transfer coefficients (CHTC), is essential for precise thermal analysis. However, due to the lack of dedicated instruments for directly measuring the CHTC, thermal analysis of the spindle faces substantial challenges. This study presents an innovative approach that combines multi-sensor data with intelligent optimization algorithms to address this issue. A distributed temperature monitoring network is constructed to capture real-time thermal field data across the spindle. At the same time, an improved Grey Wolf Optimization (IGWO) algorithm is employed to dynamically and accurately identify the CHTC. The proposed algorithm introduces an adaptive weight adjustment mechanism, which overcomes the limitations of traditional optimization methods in dynamic operating conditions. Experimental results show that the proposed method significantly outperforms conventional approaches in terms of temperature prediction accuracy across a broad operating range. This research provides a novel technical solution for machine tool thermal error compensation and establishes a scalable intelligent indirect measurement framework, even in the absence of specialized measurement instruments. Full article

This paper presents an analysis of the convergence of a numerical procedure used to evaluate the dynamic accuracy of measurement transducers, with particular emphasis on their application in energy and electromechanical systems. The main objective of the study is to assess the effectiveness of a fixed-point algorithm designed to determine test signals that satisfy time and amplitude constraints while maximizing an integral quality criterion of the “energy-optimal” type. The analysis employs numerical modeling of two types of temperature transducers: an NTC-type resistance temperature transducer and a K-type thermocouple. These models are based on a polynomial approximation method, enabling the estimation of the upper bound of the dynamic error—a key parameter in applications involving rapid changes in physical conditions, typical of energy and electromechanical systems operating under variable loads, such as industrial drives, clutches, bearings, and cooling systems, as well as in automation systems, control loops, and diagnostic frameworks. From the perspective of theoretical mechanics, temperature transducers can be modeled as a dynamic system characterized by thermal inertia, whose behavior is governed by first-order differential equations analogous to the equations of motion of a mass in a mechanically damped system. The results are presented graphically, illustrating the algorithm’s convergence behavior and computational stability. The practical application of the proposed approach can contribute to improving the accuracy of temperature transducers, enhancing error compensation algorithms, and optimizing the design of measurement systems in the energy sector and electromechanical industry, as well as in mechanical and electrical systems, especially where fast and reliable measurements under variable thermal loads on machine components are crucial. Full article

The hot deformation discipline of typical 45CrNi steel under a strain rate ranging from 0.01 s⁻¹ to 1 s⁻¹ and deformation temperature between 850 °C and 1200 °C was investigated through isothermal hot compression tests. The activation energy involved in the high-temperature deformation process was determined to be 361.20 kJ·mol⁻¹, and a strain-compensated constitutive model, together with dynamic recrystallization (DRX) kinetic models, was successfully established based on the Arrhenius theory. An improved second-phase (SP) cellular automaton (CA) model considering the influence of the pinning effect induced by SP particles on the DRX process was developed, and the established SP-CA model was further utilized to predict the evolution behavior of parent austenite grain in regard to the studied 45CrNi steel. Results show that the average absolute relative error (AARE) associated with the austenite grain size and the DRX volume fraction achieved through the simulation and experiment was overall below 5%, indicating good agreement between the simulation and experiment. The pinning force intensity could be controlled by regulating the size and volume fraction of SP particles involved in the established SP-CA model, and the DRX behavior and the average grain size of the studied 45CrNi steel treated by high-temperature compression could also be predicted. The established SP-CA model exhibits significant potential for universality and is expected to provide a powerful simulation tool and theoretical foundation for gaining deeper insights into the microstructural evolution of metals or alloys during high-temperature deformation. Full article

This paper presents a design method for a continuous tension detection sensor based on a cantilever beam structure and compensates for the temperature drift of a SAW sensor based on a neural network algorithm. Firstly, a novel cantilever beam roller structure is proposed to significantly enhance the sensitivity of the transmission of silk thread tension to a SAW tension sensor. Secondly, to improve the sensitivity of the SAW tension sensor, the COMSOL finite element method (FEM) is employed for simulation to determine the optimal IDT placement. An unbalanced split IDT design is utilized to suppress potential parasitic responses. Finally, the designed sensor is tested, and a GA-PSO-BP algorithm is employed to fit the test data for temperature compensation. The experimental results demonstrate that the temperature sensitivity coefficient of the data optimized by the GA-PSO-BP algorithm is reduced by an order of magnitude compared to the raw data, with reductions of $6.0409\times {10}^{-3}{°C}^{-1}$ and $3.0312\times {10}^{-3}{°C}^{-1}$ compared to the BP neural network and the PSO-BP algorithm, respectively. The average output error of the optimized data is reduced by 5.748% compared to the sensor measurement data, and it is also lower than both the BP neural network and the PSO-BP algorithm. It provides new design ideas for the development of tension sensors. Full article

On-machine measurement is a highly effective approach for enhancing machining accuracy and efficiency. A critical factor influencing the accuracy of on-machine measurements is the straightness error of the linear guideway. However, this error is significantly affected by environmental factors such as temperature, vibration, and gravity deformation. To improve the measurement accuracy of machine tools, this study investigates the impacts of these factors on straightness errors and proposes an innovative separation and compensation model for linear guideway straightness. A thermo-mechanical coupling simulation is employed to establish a model that quantifies the influence of thermal errors on straightness. The results demonstrate that thermal gradients cause the straightness error to bend to varying degrees, depending on the temperature distribution. Furthermore, a vibration error model is developed, revealing that the vibration period is approximately twice the ball diameter. Notably, vibration errors can be effectively mitigated using a band-stop filter to eliminate the corresponding frequency components. The study also addresses the effect of gravity deformation, comparing the deformation under different support conditions, highlighting the significance of precise support positioning. Through experimental validation of the straightness error separation and compensation model, it is shown that the straightness error of a conventional linear guideway can be reduced by 95%, and the compensated straightness error is less than 0.2 μm. This novel approach not only improves the accuracy of on-machine measurement but also provides valuable insights for optimizing machine tool performance under dynamic operating conditions. Full article

As a high-frequency and essential type of special electromechanical equipment, a vertical elevator has a significant societal implication for their safe operation. The load-weighing module, serving as the core component for overload warning, is susceptible to precision degradation due to the nonlinear deformation of rubber buffers installed at the base of the elevator car. This deformation arises from the coupled effects of environmental factors such as temperature, humidity, and material aging, leading to potential safety risks including missed overload alarms and false empty status detections. To address the issue of accuracy deterioration in elevator load-weighing systems, this study proposes an online self-calibration method based on multimodal information fusion. A reference detection model is first constructed to map the relationship between applied load and the corresponding relative compression of the rubber buffers. Subsequently, displacement data from a draw-wire sensor are integrated with target detection model outputs, enabling real-time extraction of dynamic rubber buffers’ deformation characteristics under empty conditions. Based on the above, a displacement-based compensation term is derived to enhance the accuracy of load estimation. This is further supported by a dynamic error compensation mechanism and an online computation framework, allowing the system to self-calibrate without manual intervention. The proposed approach eliminates the dependency on manual tuning inherent in traditional methods and forms a highly robust solution for load monitoring. Field experiments demonstrate the effectiveness of the proposed method and the stability of the prototype system. The results confirm that the synergistic integration of multimodal perception and adaptive calibration technologies effectively resolves the challenge of load-weighing precision degradation under complex operating conditions, offering a novel technical paradigm for elevator safety monitoring. Full article

The on-orbit geometric calibration accuracy of high-resolution space cameras directly affects the application value of Earth observation data. Conventional on-orbit geometric calibration methods primarily rely on ground calibration fields, making it difficult to simultaneously achieve high precision and real-time monitoring. To address this limitation, we, in collaboration with Tsinghua University, propose a high-precision, real-time, on-orbit geometric calibration system based on active optical monitoring. The proposed system employs reference lasers to integrate the space camera and the star tracker into a unified optical system, enabling real-time monitoring and correction of the camera’s exterior orientation parameters. However, during on-orbit operation, the space camera is subjected to a complex thermal environment, which induces thermal deformation of optical elements and their supporting structures, thereby degrading the measurement accuracy of the geometric calibration system. To address this issue, this article analyzes the impact of temperature fluctuations on the focal plane, the reference laser unit, and the laser relay folding unit and proposes athermalization design optimization schemes. Through the implementation of a thermal-compensated design for the collimation optical system, the pointing stability and divergence angle control of the reference laser are effectively enhanced. To address the thermal sensitivity of the laser relay folding unit, a right-angle cone mirror scheme is proposed, and its structural materials are optimized through thermo–mechanical–optical coupling analysis. Finite element analysis is conducted to evaluate the thermal stability of the on-orbit geometric calibration system, and the impact of temperature variations on measurement accuracy is quantified using an optical error assessment method. The results show that, under temperature fluctuations of 5 °C for the focal plane and the reference laser unit, 1 °C for the laser relay folding unit, and 2 °C for the star tracker, the maximum deviation of the system’s measurement reference does not exceed 0.57″ (3σ). This enables long-term, stable, high-precision monitoring of exterior orientation parameter variations and improves image positioning accuracy. Full article

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

A single-crystal magnesium oxide (MgO) dual-Fabry–Perot (FP)-cavity sensor based on MEMS technology and laser micromachining is proposed for simultaneous measurement of temperature and pressure. The pressure sensitive cavity is processed by wet chemical etching and direct bonding, which can improve machining efficiency, ensure the quality of the reflection surface and achieve thermal stress matching. Femtosecond laser and micromachining technologies are used to fabricate a rough surface and a through hole to reduce the reflect surface and fix the optical fiber. The bottom surface of the pressure cavity and the upper surface of the MgO wafer form a temperature cavity. A cross-correlation signal demodulation algorithm combined with a temperature decoupling method is proposed to achieve dual-cavity demodulation and eliminate the cross-sensitivity between temperature and pressure, improving the accuracy of pressure measurement. Experimental results show that the proposed sensor can stably operate at an ambient environment of 22–800 °C and 0–0.5 MPa with a pressure sensitivity of approximately 0.20 µm/MPa (room temperature), a repeatability error of 2.06% and a hysteresis error of 1.90%. After temperature compensation, thermal crosstalk is effectively eliminated and the pressure measurement accuracy is 2.01%F.S. Full article

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R² = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K. Full article

In sectors that require large components with tight tolerances, the control of machine thermal deformations as a result of ambient temperature variations, motor consumption, and heating of moving components is essential. There are many alternatives for modelling and trying to compensate for this deformation, but structural components are rarely analysed independently to study their influence on positioning errors. This study analysed component temperature and deformation measurements using 49 thermocouples and 14 integral deformation sensors (IDS) installed on a large-scale machine tool. The effect of each heat source on component deformations was studied and those with a predominant effect were identified. The results can ease thermal effect prediction models development and new machine design process to maximise accuracy by focusing effort on the most critical components and most important heat sources. It was found that ambient temperature variations lead to greater but more uniform deformations than internal heat sources, reaching a 60% of total deformations with smaller temperature changes (8.7 °C, against 15–35 °C due to internal heat sources). These deformations are localized mainly in the machine bed (100 $\mu$m in X direction and 170 $\mu$m in the Y direction) and column (150 $\mu$m in the Z direction) and in the axis ball screw bearings (reaching 55 °C). Consequently, it is concluded that improving bearing and motor refrigeration could significantly reduce thermally-induced deformations. Full article