Work

Unsupervised machine learning for local stress identification in fatigue analysis of welded joints

Year: 2024

Type: article

Abstract: In the underlying study, a method has been proposed to automatically extract finite element (FE) peak stresses of welded components to alleviate human errors and increase the calculation accuracy. The approach is based on the K-means and DBSCAN (density-based spatial clustering of applications with noise) methods as the unsupervised machine learning approaches. Data points, in this case, nodal coordinates and their corresponding stress magnitudes, are grouped within different clusters. The peak stress in each dense region (cluster) is then highlighted and reported automatically. Parametric and comparative studies have also been carried out in order to detect optimised parameters of the K-means and DBSCAN algorithms. The methodology will ultimately be used for more reliable stress analysis in fatigue assessment of welded structures.

Source: Welding in the World

DOI: 10.1007/s40194-024-01868-5

Authors: Mehdi Ghanadi, Manoranjan Kumar, Per-Olof Danielsson, Gustav Hultgren, Zuheir Barsoum

Institutions: Volvo (Sweden), KTH Royal Institute of Technology, Linnaeus University

Language: English

Topic: Welding Techniques and Residual Stresses

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,090

Funder: Kungliga Tekniska Högskolan