Fatigue behaviour of load-carrying fillet-welded cruciform joints of austenitic stainless steel

Peng, Yang; Zhang, Dai; Chen, Jie; Ju, Xiaochen; Dong, Jun

doi:10.1016/j.jcsr.2021.106798

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…The fatigue fracture of austenitic stainless steel LCFW cruciform connections was discovered to be a semi-elliptic crack, one that was distinct from the fracture of LCFW structural steel, which implies higher fatigue and lower SIF [28]. However, it was demonstrated that in accordance with the guidelines of the International Institute of Welding (IIW) and Eurocode 3 (EC3), the fatigue strength of the LCFW cruciform joints made of austenitic stainless steel is higher than that of structural steel [35,36].…”

Section: Resultsmentioning

confidence: 99%

“…Peng et al [35] analyzed the fatigue behavior of austenitic stainless steel Load-Carrying Fillet-Welded (LCFW) cruciform welds through experimental tests coupled with regression to the S-N curve with reference to Eurocode 3 [36].…”

Section: Regression Based Methodsmentioning

confidence: 99%

“…Steel [21][22][23][24][25][28][29][30]38,45], metal composite [29], stainless steel [34,35], titanium [39].…”

Section: Regressionmentioning

confidence: 99%

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Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures.

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Section: Resultsmentioning

confidence: 99%

Section: Regression Based Methodsmentioning

confidence: 99%

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Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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“…To investigate the relationship between the joint geometry and the fatigue failure mode, we investigated 383 LCFWJ fatigue data, including 193 toe failure data, 179 root failure data, and 11 runout data; 321 of the experimental data were obtained from the fatigue tests conducted by the co‐authors of this paper, containing weldments made from HLSA‐80/DH‐36 steel, 37,38 10CrNi3MoV steel, 23,42 and AA5083, AA7005 aluminum alloys 29 . The other 62 fatigue data were collected from the literature 3,39,43–45 . Table 1 tabulates the failure mode, joint geometry parameter, and other related information of the fatigue data.…”

Section: Experiments Data Of Lcfwjsmentioning

confidence: 99%

“…29 The other 62 fatigue data were collected from the literature. 3,39,[43][44][45] Since the co-authors conducted the first three group fatigue tests, the detailed joint dimensions were carefully measured and documented. The actual fillet sizes of HLSA-80/DH-36 LCFWJs (the 1st group in Table 1) were measured by a Wiki-SCAN wireless weld inspection system before fatigue at four different positions along each weld.…”

Section: Summary the Joint Dimensions Of The Lcfwsmentioning

confidence: 99%

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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Traction stress-based fatigue failure mode identification of load-carrying welded cruciform joints

Wei

Pei

Qian

et al. 2022

International Journal of Fatigue

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Fatigue behaviour of load-carrying fillet-welded cruciform joints of austenitic stainless steel

Cited by 13 publications

References 22 publications

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

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

Traction stress-based fatigue failure mode identification of load-carrying welded cruciform joints

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