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Item type:Publication, Access status: with files , Comparison of machine learning and stress concentration factors-based fatigue failure prediction in small-scale butt-welded jointsFatigue behavior of welded joints is significantly influenced by numerous factors, for example, local weld geometry. A representative quantity for the influence of the notch effect created by the local weld geometry is the stress concentration factor (SCF). Thus, SCFs are often used to estimate fatigue failure locations and fatigue strength; however, this simplifies the mutual effect of other influencing factors. Consequently, fatigue strength estimates for welded joints may deviate from experimental results. Machine learning techniques offer an alternative to traditional fatigue assessment approaches based on SCFs. This study presents a comparison of failure location predictions and number of cycles to failure for 621 fatigue tests of small-scale butt-welded joints. In addition, an understanding of importance and mutual influence of the factors is desired. We used gradient boosted trees in combination with the SHapley Additive exPlanation framework to identify influential features and their interactions.Publicationtype: Journal ArticleScopus© Citations 11 85 339 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Statistical characterization of stress concentrations along butt joint weld seams using deep neural networks(Multidisciplinary Digital Publishing Institute (MDPI), 2022-06-15); ; ; ; 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.Publicationtype: Journal ArticleScopus© Citations 10 86 148 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Determination of weld geometry parameter and stress concentration distributions for butt joints using deep neural networksPublicationtype: Conference Paper42 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Prediction of fatigue failure in small-scale butt-welded joints with explainable machine learningButt-welded joints are common in many industries. The fatigue behavior of such joints depends on numerous factors, e.g. load level, local weld geometry, or parent material strength. To make things worse, these factors often interact, yet mutual influence can hardly be quantified by multivariate studies, i.e. varying one factor at a time out of many factors, due to the large number of required tests and the statistical nature of weld geometry. Consequently, fatigue assessment of such joints often deviates significantly between prediction and experimental result. Thus, alternative methods are desirable in order to take various influencing factors into account. To this end, machine learning techniques were used to predict failure locations and number of cycles to failure of fatigue tests performed on small-scale butt-welded joint specimens. In addition to accurate predictions, an understanding of importance and mutual influence of the factors is desired, e.g. a ranking of the most important factors; however, capturing the influence of several possibly interacting factors usually requires complex nonlinear machine learning models. We used gradient boosted trees. Since these are black box models, the SHapley Additive exPlanations (SHAP) framework was used to explain the predictions, i.e. identify influential features and their interactions. Lastly, the model explanations are linked back to domain knowledge.Publicationtype: Journal ArticleScopus© Citations 13 67 148 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Establishing a common database of ice experiments and using machine learning to understand and predict ice behaviorIce material models often limit the accuracy of ice related simulations. The reasons for this are manifold, e.g. complex ice properties. One issue is linking experimental data to ice material modeling, where the aim is to identify patterns in the data that can be used by the models. However, numerous parameters that influence ice behavior lead to large, high dimensional data sets which are often fragmented. Handling the data manually becomes impractical. Machine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and small scale experimental database is established.Publicationtype: Journal Article290