Reconstruction of the Statistical Characteristics of Electric Fields in Enclosures With an Aperture Based on Random Forest Regression

Yuan, Zhao; Zhao, Xiang; Yan, Liping; Liu, Zhengyang; Liu, Qiang; Liu, Changjun; Zhou, Haijing

doi:10.1109/temc.2019.2926521

Cited by 10 publications

(1 citation statement)

References 19 publications

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“…The random forest algorithm was applied to establish the mapping relationship between cutting parameters

and fitting function key parameters

, and predict the residual stress profile under the desired cutting parameters on the basic of the training data sets, i.e., the cutting parameters and the key parameters in Table 5 and Table 6 . In the literature [ 32 ], the R 2 values of the test sets are in the range of 0.797 and 0.890. Therefore, in the present study, R 2 values were controlled between 0.80 and 0.85 to avoid over fitting.…”

Section: Resultsmentioning

confidence: 99%

Semi-Empirical Prediction of Residual Stress Distributions Introduced by Turning Inconel 718 Alloy Based on Lorentz Function

et al. 2020

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The residual stress of machined surface has a crucial influence on the performance of parts. It results in large deviations in terms of the position accuracy, dimension accuracy and service life. The purpose of the present study is to provide a novel semi-empirical residual stress prediction approach for turning Inconel 718. In the method, the bimodal Lorentz function was originally applied to express the residual stress distribution. A statistical model between the coefficients of the bimodal Lorentz function and cutting parameters was established by the random forest regression, in order to predict the residual stress distribution along the depth direction. Finally, the turning experiments, electrolytic corrosion peeling, residual stress measurement and correlation analysis were carried out to verify the accuracy of predicted residual stress. The results show that the bimodal Lorentz function has a great fitting accuracy. The adjusted R2 (Ad-R2) are ranging from 95.4% to 99.4% and 94.7% to 99.6% in circumferential and axial directions, respectively. The maximum and minimum errors of the surface residual tensile stress (SRTS) are 124.564 MPa and 18.082 MPa, those of the peak residual compressive stress (PRCS) are 84.649 MPa and 3.009 MPa and those of the depth of the peak residual compressive stress (DPRCS) are 0.00875 mm and 0.00155 mm, comparing three key feature indicators of predicted and simulated residual stress. The predicted residual stress is highly correlated with the measured residual stress, with correlation coefficients greater than 0.8. In the range of experimental measurement error, the research in the present work provides a quite accurate method for predicting the residual stress in turning Inconel 718, and plays a vital role in controlling the machining deformation of parts.

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“…The random forest algorithm was applied to establish the mapping relationship between cutting parameters

and fitting function key parameters

Section: Resultsmentioning

confidence: 99%

Semi-Empirical Prediction of Residual Stress Distributions Introduced by Turning Inconel 718 Alloy Based on Lorentz Function

et al. 2020

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A Method for optimizing Impedance Bandwidth of Vivaldi Antenna Based on Random Forest-Differential Evolution Algorithm

Chang,

Zhu,

et al. 2023

2023 IEEE 11th Asia-Pacific Conference on Antennas and Propagation (APCAP)

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A Reconstruction Method for Missing Data of electricity users Using Extremely Randomized Tree

Liu

et al. 2021

2021 International Conference on Computer, Internet of Things and Control Engineering (CITCE)

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Reconstruction of the Statistical Characteristics of Electric Fields in Enclosures With an Aperture Based on Random Forest Regression

Cited by 10 publications

References 19 publications

Semi-Empirical Prediction of Residual Stress Distributions Introduced by Turning Inconel 718 Alloy Based on Lorentz Function

Semi-Empirical Prediction of Residual Stress Distributions Introduced by Turning Inconel 718 Alloy Based on Lorentz Function

A Method for optimizing Impedance Bandwidth of Vivaldi Antenna Based on Random Forest-Differential Evolution Algorithm

A Reconstruction Method for Missing Data of electricity users Using Extremely Randomized Tree

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