Determination of notch factors for welded butt joints based on numerical analysis and metamodeling

Oswald, Markus; Neuhäusler, Josef; Rother, Klemens

doi:10.1007/s40194-020-00982-4

Cited by 11 publications

(16 citation statements)

References 7 publications

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“…An alternative way is the use of surrogate models obtained from regression or the application of artificial neural networks (ANN) [24][25][26]38]. The ANN used for our application by Oswald et al is based on 4136 linear elastic FE-simulations of parametrized design samples and has proven high accuracy compared to former existing empirical formulas as well as a significant increase of applicable parameter ranges.…”

Section: Stress Concentration Factormentioning

confidence: 99%

“…Therefore, the critical value is clearly the better parameter, because the weld tends to the crack initiation at the most critical location [27]. The critical stress concentration factors are the most suitable parameters compared to the other geometrical parameters to describe the fatigue performance along a weld seam because it combines the measured parameters [24,25]. A problematic aspect of the critical value is that this value is only meaningful when all slices are taken into account.…”

Section: Fig 12: Scatter Of the Normalised Data Normalised By The Cor...mentioning

confidence: 99%

“…Other investigations also show a high influence of the weld leg length [23]. The stress concentration factor represents local stress concentration at the weld toe and can be estimated from weld toe radius and flank angle [24][25][26].…”

mentioning

confidence: 99%

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An algorithm for statistical evaluation of weld toe geometries using laser triangulation

Renken

Polach

Schubnell

et al. 2021

International Journal of Fatigue

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Section: Stress Concentration Factormentioning

confidence: 99%

Section: Fig 12: Scatter Of the Normalised Data Normalised By The Cor...mentioning

confidence: 99%

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An algorithm for statistical evaluation of weld toe geometries using laser triangulation

Renken

Polach

Schubnell

et al. 2021

International Journal of Fatigue

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“…Two new surrogate models based on a large amount of samples processed using FEA of transverse non-load carrying stiffeners with varying geometrical parameters of flank angle α, reference radius ρ, sheet thicknesses t 1 , t 2 , and weld seam thickness a are provided. The paper extends an already investigated and published collection of the joint types (a) cruciform joints [14], (b) butt joints [15], and (c) load carrying T-joints [16].…”

Section: Introductionmentioning

confidence: 75%

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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“…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%

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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Determination of notch factors for welded butt joints based on numerical analysis and metamodeling

Cited by 11 publications

References 7 publications

An algorithm for statistical evaluation of weld toe geometries using laser triangulation

An algorithm for statistical evaluation of weld toe geometries using laser triangulation

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

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

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