Dimensional Variation Analysis for Rigid Part Assembly With an Improvement of Monte Carlo Simulation

Tang, Wenbin; Xu, Guowen; Zhang, Shoujing

doi:10.1109/access.2019.2963400

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…This method simulates a large number of actual processed parts through random sampling. After that, by assembling the parts with errors together, the assembly clearances are analyzed [30]. Different clearances will cause the precision spool valve to have different oil leakage.…”

Section: Key Methodsmentioning

confidence: 99%

“…The geometrical topography is modeled as follows: first of all, the assembly interface is extracted from the CAD model of precision spool valves and then discretized; secondly, in view of the dimensional tolerance and cylindricity tolerance of assembly interface, the DCT method is used to establish the error field of each discrete point on the assembly interface; in the end, the error field is fused with the original assembly interface to construct the geometrical topography. This paper applied the Monte Carlo method to generate a large number of qualified spool valves in different shapes and form digital twin spool valve models, thereby providing more comprehensive and accurate input for analyzing the oil leakage features of spool valves in the design stage [30].…”

Section: Modeling Of Geometrical Topography Of the Assembly Interfacementioning

confidence: 99%

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Digital Twin-Driven Mating Performance Analysis for Precision Spool Valve

Tang

Zhang

et al. 2021

Machines

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The precision spool valve is the core component of the electro-hydraulic servo control system, and its performance has an important influence on the flight control of aviation and aerospace products. The non-uniform surface topography error causes a non-uniform mating gap field inside the spool valve, which causes oil leakage and leads to deterioration of the spool valve performance. However, the current oil leakage calculation method only considers the influence of size errors, which is not comprehensive. Thus, how to characterize the mating behavior of the spool valve and its effect on oil leakage with consideration of surface topography errors is the key to evaluating the performance of the spool valve. This paper proposes a new way of analyzing the mating performance of precision spool valves, which considers the surface topography errors based on digital twin technology. Firstly, a general framework for the analysis of mating performance of precision spool valve based on a digital twin is proposed. Then, key technologies of assembly interface geometry modeling, matching behavior modeling and performance analysis are studied. Finally, a quantitative correlation between the mating parameters and the oil leakage of the precision spool valve is revealed. The method is tested on a practical case. This proposed method can provide theoretical support for the accurate prediction and evaluation of the mating performance of the precision spool valve.

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Section: Key Methodsmentioning

confidence: 99%

Section: Modeling Of Geometrical Topography Of the Assembly Interfacementioning

confidence: 99%

Digital Twin-Driven Mating Performance Analysis for Precision Spool Valve

Tang

Zhang

et al. 2021

Machines

Self Cite

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“…In the CIOPB framework, uncalibrated measurement tools and human error allow tree-level measurement errors to propagate into the anisotropic growth model [47]; furthermore, the randomness in plot sampling and testing set delineation is also an important error source, which may generate potential uncertainty in prediction results. Based on the methods and results of previous studies [47,[64][65][66][67][68]72,73], this paper analyzed the uncertainty generated in the CIOPB framework (Section 3.4) and added the uncertainty layer of the output AGB map (Figure 6) in a relatively ideal state [74]. The results revealed that the uncertainty of the CIOPB framework on the testing set samples ranged from 17.5354% to 29.7257% (Category-1: 23.4777% to 29.7257%; Category-2: 17.5354% to 20.4968%), with lower uncertainty using the optimal feature group than other groups.…”

Section: Uncertainty Of Agb Inversion Using the Ciopb Frameworkmentioning

confidence: 99%

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

Zhang,

Zhou,

Zhang

et al. 2024

Forests

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The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel framework based on freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data. Firstly, we designed new indices through the formula analogous with vegetation index calculation to integrate multidimensional spectral and structural information. Then, leveraging the simplicity of computational principles, a pigeon-inspired optimization algorithm (PIO) was introduced into a bi-directional long short-term memory neural network (PIO-BiLSTM), which achieved the set objective function through repeated iteration and validation to obtain the optimal model parameters. Finally, to verify the framework’s effect, we conducted experiments in two different tree species and compared another seven classical optimization algorithms and machine learning models. The results indicated that the new indices significantly improved the inversion accuracy of all models in both categories, and the PIO-BiLSTM model achieved the highest accuracy (Category-1: R2 = 0.8055, MAE = 8.8475 Mg·ha−1, RMSE = 12.2876 Mg·ha−1, relative RMSE = 18.1715%; Category-2: R2 = 0.7956, MAE = 1.7103 Mg·ha−1, RMSE = 2.2887 Mg·ha−1, relative RMSE = 9.3000%). Compared with existing methods, the proposed framework greatly reduced the labor costs in parameter selection, and its potential uncertainty also decreased by up to 9.0%. Furthermore, the proposed method has a strong generalization ability and is independent of tree species, indicating its great potential for future forest AGB inversion in wider regions with diverse forest types.

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“…We therefore rely on the Monte Carlo model in quantifying the prediction uncertainty. The underlying principle of the Monte Carlo model is the repeated simulation of the occurrence of a random event and the subsequent estimation of its probability features based on the frequency of the said random event [98]. With repeated simulations of the Monte Carlo samples (in our case, 500 iterations), the probability distribution of biomass estimates, and errors are obtained from the series of iterations which resulted in a stable and reliable quantification of biomass and the error map [99].…”

Section: Satellite Climatic and Topographic Variablesmentioning

confidence: 99%

Quantification of Above-Ground Biomass over the Cross-River State, Nigeria, Using Sentinel-2 Data

et al. 2022

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Higher-resolution wall-to-wall carbon monitoring in tropical Africa across a range of woodland types is necessary in reducing uncertainty in the global carbon budget and improving accounting for Reducing Emissions from Deforestation and forest Degradation Plus (REDD+). This study uses Sentinel-2 multispectral imagery combined with climatic and edaphic variables to estimate the regional distribution of aboveground biomass (AGB) for the year 2020 over the Cross River State, a tropical forest region in Nigeria, using random forest (RF) machine learning. Forest inventory plots were collected over the whole state for training and testing of the RF algorithm, and spread over undisturbed and disturbed tropical forests, and woodlands in croplands and plantations. The maximum AGB plot was estimated to be 588 t/ha with an average of 121.98 t/ha across the entire Cross River State. AGB estimated using random forest yielded an R2 of 0.88, RMSE of 40.9 t/ha, a relRMSE of 30%, bias of +7.5 t/ha and a total woody regional AGB of 0.246 Pg for the Cross River State. These results compare favorably to previous tropical AGB products; with total AGB of 0.290, 0.253, 0.330 and 0.124 Pg, relRMSE of 49.69, 57.09, 24.06 and 56.24% and −41, −48, −17 and −50 t/ha bias over the Cross River State for the Saatchi, Baccini, Avitabile and ESA CCI maps, respectively. These are all compared to the current REDD+ estimate of total AGB over the Cross River State of 0.268 Pg. This study shows that obtaining independent reference plot datasets, from a variety of woodland cover types, can reduce uncertainties in local to regional AGB estimation compared with those products which have limited tropical African and Nigerian woodland reference plots. Though REDD+ biomass in the region is relatively larger than the estimates of this study, REDD+ provided only regional biomass rather than pixel-based biomass and used estimated tree height rather than the actual tree height measurement in the field. These may cast doubt on the accuracy of the estimated biomass by REDD+. These give the biomass map of this current study a comparative advantage over others. The 20 m wall-to-wall biomass map of this study could be used as a baseline for REDD+ monitoring, evaluation, and reporting for equitable distribution of payment for carbon protection benefits and its management.

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Dimensional Variation Analysis for Rigid Part Assembly With an Improvement of Monte Carlo Simulation

Cited by 7 publications

References 39 publications

Digital Twin-Driven Mating Performance Analysis for Precision Spool Valve

Digital Twin-Driven Mating Performance Analysis for Precision Spool Valve

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

Quantification of Above-Ground Biomass over the Cross-River State, Nigeria, Using Sentinel-2 Data

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