Abstract
Fatigue is the main cause of structural failure of large engineering structures. Welds, with their geometry leading to high local stresses, are especially vulnerable. Traditional fatigue assessment methods, which factor in material properties, load levels, and idealized weld geometries, can be inaccurate. To address this, data-driven approaches, using machine learning (ML) algorithms and 3D-laser scanners for weld geometry, have been successful in predicting fatigue life for butt-welded joints; however, it remains uncertain whether these methods are adaptable to different welding techniques and welds with imperfections. This presentation addresses the generalizability of machine learning approaches for fatigue strength assessment for welded joints by assessing data, which differs from the training dataset in various ways. The new data contains results for a different welding procedure, and of welded joints with imperfections and weld defects. By comparing prediction accuracies between the original data and the new data, the study aims to determine the adaptability of the data-driven approach to new, divergent data. The focus is on assessing how anomalous weld geometries impact prediction accuracy, ultimately establishing the limitations of applying this method to varying data. To this goal, explainable artificial intelligence is applied.
Talk in the series “Train Your Engineering Network”.
