Interval Uncertainty-Oriented Optimal Control Method for Spacecraft Attitude Control

Yang, Chen; Xia, Yuanqing

doi:10.1109/taes.2023.3257777

Cited by 27 publications

(11 citation statements)

References 34 publications

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“…The experimental values of deflection achieved by NIST, and measured with sensors [47,48], are very similar to the numerical results, although at the right end of the bridge main support there is a small divergence.…”

Section: Deflection Analysissupporting

confidence: 77%

Direct Energy Deposition Parametric Simulation Investigation in Gear Repair Applications

et al. 2023

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Additive manufacturing technologies have numerous advantages over conventional technologies; nevertheless, their production process can lead to high residual stresses and distortions in the produced parts. The use of numerical simulation models is presented as a solution to predict the deformations and residual stresses resulting from the printing process. This study aimed to predict the tensions and distortions imposed in the gear repair process by directed energy deposition (DED). First, the case study proposed by National Institute of Standards and Technology (NIST) was analyzed to validate the model and the numerically obtained results. Subsequently, a parametric study of the influence of some of the parameters of DED technology was carried out. The results obtained for the validation of the NIST benchmark bridge model were in agreement with the results obtained experimentally. In turn, the results obtained from the parametric study were almost always in line with what is theoretically expected; however, some results were not very clear and consistent. The results obtained help to clarify the influence of certain printing parameters. The proposed model allowed accounting for the effect of residual stresses in calculating the stresses resulting from gear loading, which are essential data for fatigue analysis. Modeling and simulating a deposition process can be challenging due to several factors, including calibrating the model, managing the computational cost, accounting for boundary conditions, and accurately representing material properties. This paper aimed to carefully address these parameters in two case studies, towards reliable simulations.

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Section: Deflection Analysissupporting

confidence: 77%

Direct Energy Deposition Parametric Simulation Investigation in Gear Repair Applications

et al. 2023

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“…When considering the robustness of the method used to establish a tool twin model, factors such as model sensitivity and accuracy play a vital role [27,28]. The accuracy value is calculated by comparing the actual and the predicted results.…”

Section: Resultsmentioning

confidence: 99%

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Natarajan

Mohanraj

Sakthivel

et al. 2023

Sensors

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Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.

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“…The waveform and spectrum of the impact elastic wave are analyzed to determine the location, size and boundary of the defect [55,56]. In recent years, due to the rapid development of machine learning, many scholars have used machine learning to optimize the impact-echo method [57,58]. Chen et al [59] carried out the void detection test on a CFST based on the impact-echo method.…”

Section: Void Monitoringmentioning

confidence: 99%

A Review on Damage Monitoring and Identification Methods for Arch Bridges

et al. 2023

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The damage monitoring and identification of arch bridges provide an important means to ensure the safe operation of arch bridges. At present, many methods have been developed, and the applicability and effectiveness of these methods depend on the damage type, structural configuration and available data. To guide the practical application of these methods, a systematic review is implemented in this paper. Specifically, the damage monitoring and identification methods of arch bridges are divided into the damage monitoring of local diseases and damage identification of overall performance. Firstly, the research on the damage monitoring of the local diseases of arch bridges is reviewed. According to the disease type, it is divided into four categories, including suspender inspection, void monitoring, stress detection and corrosion detection. For each disease, this paper analyzes the principles, advantages and shortcomings of various methods. Then, the damage identification methods of the overall performance of arch bridges are reviewed, including masonry arch bridges, steel arch bridges, reinforced concrete arch bridges and concrete-filled steel tubular arch bridges. And the commonly used damage indexes of damage identification methods are summarized. This review aims to help researchers and practitioners in implementing existing damage detection methods effectively and developing more reliable and practical methods for arch bridges in the future.

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Interval Uncertainty-Oriented Optimal Control Method for Spacecraft Attitude Control

Cited by 27 publications

References 34 publications

Direct Energy Deposition Parametric Simulation Investigation in Gear Repair Applications

Direct Energy Deposition Parametric Simulation Investigation in Gear Repair Applications

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

A Review on Damage Monitoring and Identification Methods for Arch Bridges

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