Statistical characterization of stress concentrations along butt joint weld seams using deep neural networks

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.

Subjects

local weld toe geometry

weld classification

3-D scans

non-destructive testing

statistical assessment

machine learning

fatigue strength

stress concentration factor

weld quality

artificial neural network

MLE@TUHH

DDC Class

600: Technik

620: Ingenieurwissenschaften

More Funding Information

This research received no external funding.

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

applsci-12-06089-v2.pdf

Size

3.74 MB

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Adobe PDF

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Statistical characterization of stress concentrations along butt joint weld seams using deep neural networks