Characterisation of actual weld geometry and stress concentration of butt welds exhibiting local undercuts

Ottersböck, Markus; Leitner, Martin; Stoschka, Michael

doi:10.1016/j.engstruct.2021.112266

Cited by 23 publications

(20 citation statements)

References 23 publications

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“…Weld toe radii seem to follow a lognormal distribution. This agrees with a number of recent studies [20,36,37], but also rejects mainly older investigations.…”

supporting

confidence: 93%

“…Here, the weld toe radii seem to be better described by a lognormal distribution. This agrees with a number of recent studies [20,36,37]. As the sample distributions of the measured weld toe angles are symmetrical, it is possible that this parameter is indeed well-described by a normal distribution; however, more data should be assessed to verify this result.…”

supporting

confidence: 91%

“…In the past, parametric SCF equations offered a quick estimation tool to determine SCFs for notched structures such as welded joints [19]; however, a number of recent studies have shown that these equations are less accurate than direct finite element (FE) calculations, see [20][21][22][23][24]. Performing direct FE calculations is, however, quite time consuming, especially for 3D weld geometries.…”

Section: Characterization Of Stress Concentrations Along Weld Seamsmentioning

confidence: 99%

“…Gamma distributions are frequently used for such tasks; however, more research is required. Ottersböck et al [20] and Schork et al [36,37] used a lognormal distribution to fit their data, which also accommodates very large numbers of small undercut depths; however, a lognormal distribution is not suited to describe distributions for which the maximum of data is equal to zero. In general, data for undercut depths are seldom presented nor considered in the literature.…”

mentioning

confidence: 99%

“…Based on Figure 10, weld reinforcement heights could either be normally distributed or described by a lognormal, gamma, or inverse gamma distribution. In the literature, the height of welds is often described by a normal distribution [20,36,37,43].…”

mentioning

confidence: 99%

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Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks

et al. 2022

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In order to ensure high weld qualities and structural integrity of engineering structures, it is crucial to detect areas of high stress concentrations along weld seams. Traditional inspection methods rely on visual inspection and manual weld geometry measurements. Recent advances in the field of automated measurement techniques allow virtually unrestricted numbers of inspections by laser measurements of weld profiles; however, in order to compare weld qualities of different welding processes and manufacturers, a deeper understanding of statistical distributions of stress concentrations along weld seams is required. Hence, this study presents an approach to statistically characterize different types of butt joint weld seams. For this purpose, an artificial neural network is created from 945 finite element simulations to determine stress concentration factors at butt joints. Besides higher quality of predictions compared to empirical estimation functions, the new approach can directly be applied to all types welded structures, including arc- and laser-welded butt joints, and coupled with all types of 3D-measurement devices. Furthermore, sheet thickness ranging from 1 mm to 100 mm can be assessed.

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“…Weld toe radii seem to follow a lognormal distribution. This agrees with a number of recent studies [20,36,37], but also rejects mainly older investigations.…”

supporting

confidence: 93%

supporting

confidence: 91%

Section: Characterization Of Stress Concentrations Along Weld Seamsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks

et al. 2022

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Effect of Process Parameters on Arc Behavior and Weld Formation in Weaving Gas Tungsten Arc Welding

Xie

Zhou

Nian

et al. 2023

J. of Materi Eng and Perform

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The arc welding with weaving has been used widely to obtain better weld quality by avoiding lack of side wall fusion and improve the weld e ciency by obtaining the wide weld bead. But the effect of weaving process parameter change on the weld is not clear. The aim of this work is to study the effect of process parameters on arc behavior and weld formation in Weaving Gas Tungsten Arc Welding(W-GTAW), those parameters include welding currents, tungsten electrode heights from the electrode tip to upper surface of workpiece, weave angles, weave speeds, and weave stop time on the left and right sides. The instantaneous arc shape and electrical signal data were collected by high-speed camera and electrical signal acquisition system respectively. Furthermore, the weld morphology was also systematically analyzed. This result shows that the bottom surface radius of the arc changed with weaving in the W-GTAW. When the weave speed increased to 0.40 × 10 -1 rad/s, the change of the radius was the least, with only 0.10 mm drift, and the difference between the arc forces in the middle and the two sides of the molten pool was smallest. Compared with the stability of each welding process, decreasing the tungsten electrode heights, weave angle and speed could signi cantly enhance the stability of welding. The forming coe cient of weld with a weave angle of 1.9° was 3.11, which might help reduce stress concentration and hot crack tendency of the weld. This shows that increasing reasonable the weave angle and speed can increase the weld penetration from another point of view. Furthermore, the W-GTAW technology shows great application potential in weld forming control by adjusting process parameters.

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Determination of notch factors for transverse non-load carrying stiffeners based on numerical analysis and metamodeling

Neuhäusler

Rother

2022

Weld World

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Stress concentration factors (SCFs) at weld toes and weld roots as required for the effective notch stress concept (see [1, 2]) are usually computed using finite element analysis (FEA) which requires a certain amount of effort for model generation, the solving process, and postprocessing. Regression functions of many FEAs within given parameter bounds provide the possibility of a fast prediction of SCFs. This paper provides new and accurate regression formulae for the estimation of notch stresses at idealized weld geometries on the basis of multiple linear-elastic FEAs for the transverse stiffener (non-load carrying T-joint) under tension and bending of the load carrying slab. Regression of sampled finite element results has been performed using (a) second-order polynomial regression with coupling terms (PRC) and (b) artificial neural networks (ANN). The presented formulae are compared with several existing estimations of stress concentration factors. The new methods appear to show a higher quality of prognosis as well as apply to significant larger ranges of the geometrical parameters of the weld joint. The formulae presented here for the transverse stiffener add another welded joint to a series of similar surrogate models presented from Munich University of Applied Sciences in earlier publications and made available for use by the web-based tool SCF-Predictor.

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Characterisation of actual weld geometry and stress concentration of butt welds exhibiting local undercuts

Cited by 23 publications

References 23 publications

Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks

Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks

Effect of Process Parameters on Arc Behavior and Weld Formation in Weaving Gas Tungsten Arc Welding

Determination of notch factors for transverse non-load carrying stiffeners based on numerical analysis and metamodeling

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