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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 86 147 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Analyzing the complexity of ice with explainable machine learning for the development of an ice material model(2022-01); Seasonally or partially ice-covered sea areas are attractive to many stakeholders. Ships and structures operating in these areas must be designed to withstand ice-induced loads. This is done with empirical methods and model-scale ice tests, but these have various drawbacks. Numerical simulations are a desirable remedy, but their accuracy is limited because current ice material models only partially capture ice behavior. Improving ice material models is difficult due to gaps in ice mechanics theory and full-scale measurements. Small-scale experimental data is analyzed with explainable machine learning for an improved understanding of ice. An elastic-brittle material model is developed and applied in several studies.Publicationtype: Doctoral Thesis514 2714 - 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: with files , Study on the cohesive edge crack in a square plate with the cohesive element methodThe size of the fully developed process zone (FDPZ) is needed for the arrangement of displacement sensors in fracture experiments and choosing element size in numerical models using the cohesive element method (CEM). However, the FDPZ size is generally not known beforehand. Analytical solutions for the exact FDPZ size only exist for highly idealised bodies, e.g. semi-infinite plates. With respect to fracture testing, the CEM is also a potential tool to extrapolate laboratory test results to full-scale while considering the size effect. A numerical CEM-based model is built to compute the FDPZ size for an edge crack in a finite square plate of different lengths spanning several magnitudes. It is validated against existing analytical solutions. After successful validation, the FDPZ size of finite plates is calculated with the same numerical scheme. The (FDPZ) size for finite plates is influenced by the cracked plate size and physical crack length. Maximum cohesive zone sizes are given for rectangular and linear softening. Further, for this setup, the CEM-based numerical model captures the size effect and can be used to extrapolate small-scale test results to full-scale.Publicationtype: Journal ArticleScopus© Citations 8 158 225