A multi-axial vibration fatigue evaluation procedure for welded structures in frequency domain

Pei, Xianjun; Ravi, Sandipp Krishnan; Dong, Ping; Li, Xiangwei; Zhou, Xiaokun

doi:10.1016/j.ymssp.2021.108516

Cited by 51 publications

(22 citation statements)

References 49 publications

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“…As reviewed in the introduction, interpolation‐based hot‐spot stress, 51 1‐mm stress, 19 notch stress, 6 and traction stress 52 are all capable candidates to replace point stress to capture the stress state at the weld toe and solve the singularity issue. However, characterizing the stress state at weld root is more challenging since there are different potential crack paths for weld root failure, as illustrated in Figure 4.…”

Section: Traction Stress‐based Weld Failure Mode Criterionmentioning

confidence: 99%

“…Figure 5 demonstrates the overall procedure for applying the traction stress‐based weld failure mode criterion. The traction stresses at the weld leg (Figure 5A) and weld toe (Figure 5B) are first computed by post‐processing the finite element results using the nodal force method 52,53 . Here,

n_{leg}, s_{leg}

, and

n_{toe}

are normal and shear components at weld leg and normal traction stress at weld toe, respectively.…”

Section: Traction Stress‐based Weld Failure Mode Criterionmentioning

confidence: 99%

“…The traction stresses at the weld leg (Figure 5A) and weld toe (Figure 5B) are first computed by postprocessing the finite element results using the nodal force method. 52,53 Here, n leg , s leg , and n toe are normal and shear components at weld leg and normal traction stress at weld toe, respectively. With the weld leg traction stresses n leg and s leg , a closed-form analytical solution is derived to predict the critical angle, at which the weld root traction maximizes (Figure 5C).…”

Section: Traction Stress-based Weld Failure Mode Criterionmentioning

confidence: 99%

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A data‐driven analysis for fatigue failure mode identification in load‐carrying fillet welded joint with mechanical data augmentation

Zhao

Zhang

Song

et al. 2022

Fatigue Fract Eng Mat Struct

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Weld toe and weld root failures dominate the fatigue failures of the loadcarrying cruciform fillet welded joints. Compared to weld toe failure, the weld root failure is strongly affected by its inherent weld quality and throat size, which is more unpredictable and should be eliminated. This work proposes a weld sizing criterion in terms of two weld leg sizes and weld penetration to avoid root failure. The criterion is obtained by a data-driven approach with the help of the mechanical-directed data augmentation technique. The tractionstress-based method is applied for the data augmentation to identify the fatigue failure mode, which is statistically tested against 372 experimentally obtained fatigue data.cruciform joint, data augmentation, data driven, weld root failure, weld sizing | INTRODUCTIONThe load-carrying cruciform fillet welded joint (LCFWJ) is broadly applied to connect secondary components to main members in metal structures. [1][2][3] Two failure modes dominate the fatigue fractures of LCFWJ, namely, weld toe and root failure, depending on the load conditions and the joint geometry, as illustrated in Figure 1. Fatigue crack initiating from the weld root and propagating into the fillet weld is referred to as weld root failure, which should be eliminated during the design stage of the welded structures because of two reasons. (1) Fatigue lives according to weld root failure is usually much lower than that associated with weld toe failure. As stipulated in EN-1993-1-9, 4 the S-N curve recommended for weld root failure falls into the worst possible category, namely, FAT36, which is lower than that of the weld toe failure. 5 Ziqi Zhao and Tairui Zhang contributed equally to this work and should be considered co-first authors.

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Section: Traction Stress‐based Weld Failure Mode Criterionmentioning

confidence: 99%

n_{leg}, s_{leg}

, and

n_{toe}

are normal and shear components at weld leg and normal traction stress at weld toe, respectively.…”

Section: Traction Stress‐based Weld Failure Mode Criterionmentioning

confidence: 99%

Section: Traction Stress-based Weld Failure Mode Criterionmentioning

confidence: 99%

See 1 more Smart Citation

A data‐driven analysis for fatigue failure mode identification in load‐carrying fillet welded joint with mechanical data augmentation

Zhao

Zhang

Song

et al. 2022

Fatigue Fract Eng Mat Struct

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“…The predictive capabilities of trained AI\ML models are extensively used to evaluate properties of new material system and structures, getting insights on response surfaces [33]. One prominent example of the same is given for multi-axial fatigue analysis [34][35][36][37][38]. Though several strides have been made in applying ML framework in the material science domain, one strong limitation that has been pointed out (as mentioned in the previous section) is the black box nature of the ML models and lack of associated interpretability.…”

Section: Application Of Explainable Ai In Materials Sciencementioning

confidence: 99%

Elucidating Precipitation in FeCrAl Alloys through Explainable AI: A Case Study

Roy

Ravi

Roychowdhury

et al. 2022

Preprint

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Primary challenge of using FeCrAl in high temperature industrial setting is the formation of α’- precipitates that causes brittleness in the alloy, resulting in failure through fracture. The precipitation causes hardness change during thermal aging which is sensitive to both alloy composition and experimental condition (i.e., temperature and time of heat treatment). A Gaussian Process Regression (GPR) model is built on the hardness data collected at GE Research. Subsequently, for the first time, SHapley Additive exPlanations (SHAP) built upon the GPR is used as an Explainable Artificial Intelligence (XAI) tool to understand the effect of feature values in driving the hardness change. Several key insights are affirmed and obtained from a material science perspective.

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“…25 The recommended evaluation methods on the fatigue properties of OBD are associated with some limitations. 15,[26][27][28][29] Pei and Dong 30 further extended this method to a lowcycle fatigue regime and came up with a structural strain method, which can correlate both high-and low-cycle fatigue data weldments made from different base metals into a master E-N curve. Such problems can be tackled using the unified master S-N curve method, [31][32][33][34][35][36] which is based on the traction structural stress theory [37][38][39] and used in the pressure vessel and marine welded structures.…”

Section: Introductionmentioning

confidence: 99%

Modeling of fatigue failure mode in U‐rib to deck joints in orthotropic bridge structures

Yang

Wang

Qian

et al. 2022

Fatigue Fract Eng Mat Struct

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Various fatigue failure modes (i.e., cracking position and orientation with respect to a weld) can develop in welded rib to deck connections in orthotropic bridge deck structures. After demonstrating its effectiveness in correlating fatigue test data covering different failure modes, the master S-N curve method was then adopted in this study for determining the critical failure mode in welded U-rib to deck connections. These include considerations of additional failure modes potentially present in double-sided welds between U-rib and deck versus the traditional single-sided weld design. The effects of weld penetrations and test loading conditions on failure mode development have been quantitatively established by means of the master S-N curve method.

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A multi-axial vibration fatigue evaluation procedure for welded structures in frequency domain

Cited by 51 publications

References 49 publications

A data‐driven analysis for fatigue failure mode identification in load‐carrying fillet welded joint with mechanical data augmentation

A data‐driven analysis for fatigue failure mode identification in load‐carrying fillet welded joint with mechanical data augmentation

Elucidating Precipitation in FeCrAl Alloys through Explainable AI: A Case Study

Modeling of fatigue failure mode in U‐rib to deck joints in orthotropic bridge structures

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