Generalized SCF Formula of Out-Of-Plane Gusset Welded Joints and Assessment of Fatigue Life Extension by Additional Weld

Wang, Yixun; Luo, Yan; Kotani, Yoshihisa; Tsutsumi, Sadami

doi:10.3390/ma14051249

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Alternatively, numerical simulations may be utilized to generate a large dataset of computational results, which can be used to fit semi-empirical or empirical formulas. For example, in the case of predicting SCF under different weld seam shapes, Luo et al [6,7] and Wang et al [8,9] calculated SCFs for various combinations of shape parameters and misalignments. And parameter fitting methods are then employed to obtain empirical formulas for the SCFs for butt welded joints, cruciform joints, T-shaped joints, and so on.…”

Section: Application Of Machine Learning Models In Structural Design ...mentioning

confidence: 99%

Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model

Dai,

Du,

Luo

et al. 2023

Materials

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The flexible high-neck flange is connected to the circular hollow section (CHS) tube through welding, and the placement of the weld seam and corresponding stress concentration factor (SCF) are crucial determinants of the joint’s fatigue performance. In this study, three hybrid models combining ant colony optimization (ACO), a genetic algorithm (GA), and grey wolf optimization (GWO) with a random forest (RF) model were developed to predict the stress distribution on the inner and outer walls of the CHS tube under different flange parameter combinations. To achieve this, an automated finite element (FE) analysis program for flexible high-neck flange joints was initially developed based on ABAQUS 2020 software. Parameter combinations were randomly selected within a reasonable range to simulate the nonlinear mechanical behavior of the joint under uniform tension, generating a dataset comprising 5417 sets of data. The accuracy of the FE model was validated through experimental data from the literature. Based on this, feature importance analysis was conducted to reveal the influence of different variable parameters on the stress distribution in the tube of the joint. The flange parameters and tube stress distribution are considered as inputs and outputs, respectively. Three hybrid RF models, specifically ant colony optimization-based random forest (ACO-RF), genetic algorithm-based random forest (GA-RF), and grey wolf optimization-based random forest (GWO-RF), are trained for regression prediction. The results demonstrate that the three hybrid models outperform the original machine learning model in predictive accuracy. The ACO-RF model achieved the highest accuracy with average coefficients of determination (Rmean2) of 0.9983 and 0.9865 on the testing and training sets, respectively. Building upon this foundation, the study developed a corresponding open-source graphical user interface (GUI) as a tool for facilitating computations and visualizing results. Finally, a case study on fatigue damage assessment of a flexible high-neck flange joint in a wind-turbine tower is presented to demonstrate the application of the proposed model in this study.

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Section: Application Of Machine Learning Models In Structural Design ...mentioning

confidence: 99%

Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model

Dai,

Du,

Luo

et al. 2023

Materials

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“…The fatigue initiation life of welded joints can usually be assessed based on stress ranges or strain ranges, namely, the stress method and the strain method. The existing stress method includes nominal stress [ 10 ], hot spot stress [ 11 ] and effective notch stress [ 12 ], indicating sustained efforts to approximate a real local stress state. The disadvantage of assessing fatigue life by stress method is that it is difficult to calculate the local stress at the weld toe or weld root.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Life Assessment of Welded Joints by Combined Measurements Using DIC and XRD

Wang

Ueda

Ryota³

et al. 2021

Materials

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The existing methods of assessing the fatigue life of welded joints fail to consider local strain ranges and mean stress at the weld toe. The present work proposes a novel approach to assessing the fatigue life of welded joints by conducting measurements with digital image correlation (DIC) and X-ray diffraction (XRD) in combination. Local strain ranges at the weld toe of gusset welded joints were measured by DIC. Hammer peening was conducted on the welded joints to introduce different initial stresses. The influence of mean stress was investigated by considering initial residual stress measured by XRD and a perfect plastic material model. The fatigue experiment was carried out on specimens with and without hammer peening. The results showed that hammer peening could offset adverse welding deformation effectively, and introduce significant residual compressive stress. The fatigue failure life increased by more than 15 times due to hammer peening. The fatigue initiation life assessed by the proposed method was close to that based on nominal stress, indicating that the proposed method is reliable for predicting the fatigue initiation life of welded joints.

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“…The authors wish to revise in the text of Appendix A , pages 19–21 [ 1 ], due to the presence of incorrect information.…”

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confidence: 99%

Correction: Wang et al. Generalized SCF Formula of Out-Of-Plane Gusset Welded Joints and Assessment of Fatigue Life Extension by Additional Weld. Materials 2021, 14, 1249

et al. 2021

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Generalized SCF Formula of Out-Of-Plane Gusset Welded Joints and Assessment of Fatigue Life Extension by Additional Weld

Cited by 6 publications

References 29 publications

Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model

Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model

Fatigue Life Assessment of Welded Joints by Combined Measurements Using DIC and XRD

Correction: Wang et al. Generalized SCF Formula of Out-Of-Plane Gusset Welded Joints and Assessment of Fatigue Life Extension by Additional Weld. Materials 2021, 14, 1249

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