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- Optimization of a cruciform specimen for fatigue crack growth under in and out-of-phase in-plane biaxial loading conditionsPublication . Baptista, R.; Infante, Virginia; Madeira, JFAMixed-mode loading conditions are present in different mechanical components. Understanding the influence of in-plane biaxial loading paths parameters allows for fatigue crack growth (FCG) prediction and component fatigue life assessment. Cruciform specimens are used to simulate these conditions, but large specimen dimensions are required in order to keep crack propagation unaffected by specimen geometry. This article describes the procedure used to optimize a new cruciform specimen geometry, with small dimensions. Having identified the specimen arms fillet as a major source of crack growth interference, this effect was kept to a minimum, while using arm slots with different widths and lengths. Individual slot dimensions were optimized using a Direct MultiSearch (DMS) algorithm, minimizing the stress intensity factor (SIF) difference between the optimal specimen and an infinite plate. FCG on the optimized specimen was simulated under in and out-of-phase loading conditions. Due to crack closure effects, fatigue propagation under fully out-of-phase loading is less sensitive to specimen geometry. Therefore, the final geometry was chosen considering the required biaxial loading ratio under in-phase loading.
- Optimal cruciform specimen design using the direct multi-search method and design variable influence studyPublication . Miguel Gomes Simões Baptista, Ricardo; Cláudio, R. A.; Reis, L.; Madeira, JFA; Freitas, M.Nowadays the development of new testing machines and the optimization of new specimen geometries are two very demanding activities. In order to study complex material stress and strain distributions, as in-plane biaxial loading, one must develop new technical solutions. A new type of testing machine has been developed by the present authors, for the fatigue testing of cruciform specimens, but the low capacity of the testing machine requires the optimization of the specimen in order to achieve higher but uniform stress and strain distributions on the specimen center. In this paper, the authors describe the procedure to optimize one possible geometry for cruciform specimens, able to determine the fatigue initiation life of material subjected to out of phase in-plane biaxial fatigue loadings. The high number of design variables were optimized using the direct multi-search method, considering two objective functions, the stress level on the specimen center and the uniformity of the strain distribution on a 1.0 mm radius of the specimen center. Several Pareto Fronts were obtained for different material thickness, considering the commercially available sheet metal thickness. With the optimal solution, the influence of every design variable was studied in order to provide others with a powerful tool that allows selecting the optimal geometry for the desired application. The results are presented in the form of design equations considering that the main design variable, the material thickness, was chosen from a Renard series of preferred numbers. The end user is then able to configure the optimal specimen for the required fatigue test.
- Numerical study of fatigue crack initiation and propagation on optimally designed cruciform specimensPublication . Baptista, R.; Cláudio, Ricardo; Reis, L.; Madeira, JFA; Freitas, M.A new generation of smaller and more efficient biaxial fatigue testing machines has arrived on the market. Using electrical motors these machines are not able to achieve the higher loads their hydraulic counterparts can, and therefore the cruciform specimen needs to be optimized Following the authors previous work, several different optimal specimens' configurations were produced, using the base material sheet thickness as the main design variable. Every design variable was optimized in order to produce the highest stress level on the specimen center, while the stress distribution is still uniform on a 1 mm radius of the specimen center. Also it was guaranteed that the stress level on the specimen arms was always considerably lower, in order to achieve failure at the specimen center. In this paper traditional criteria like Findley, Brown-Miller, Fatemi-Socie, Smith, Watson e Topper (SWT), Liu I and Chu were considered to determine crack initiation direction for several loads in this biaxial in-plane specimens. In order to understand the fatigue propagation behavior, the stress intensity factors for mode I and mode II was determined for different cracks introduced on the geometry. Several crack and loading parameters were studied, including the starting crack length and angle, and different loading paths. Several biaxial loads were applied to the model, including 30, 45, 60, 90 and 180 out-of-phase angles.
- Non-proportional mixed mode plastic zones via finite elements and artificial neural networksPublication . Infante, Virgínia; Miguel Gomes Simões Baptista, RicardoThe plastic zone developed around a fatigue crack tip can affect both fatigue and fracture material behaviour. Predicting the plastic zone shape and size under cyclic conditions can, therefore, enhance fatigue crack propagation analysis. While there are theoretical solutions for the elastic stress and strain fields under pure or mixed mode conditions around the crack tip, solutions for plastic fields must be determined using experimental or numerical approaches. The Compact Tension Shear (CTS) specimen has been extensively used to analyse plastic zones under proportional conditions, but when non-proportional conditions are applied the number of necessary analyses for a reasonable understanding of the plastic zone shape and size around the crack tip can increase exponentially. To address this problem, a combined approach was used to reduce the number of required plastic zone simulations. First, a hand selected number of loading configurations were simulated using the Finite Element Method (FEM), predicting the plastic zone shape and size. Then, an Artificial Neural Network (ANN) was trained to predict the plastic zone under different conditions. Using only 18 configurations for 3 different loading conditions, the trained ANN was able to accurately predict the plastic zone shape and size for both tensile and shear propagation modes. The network can now be used to predict the plastic zone influence on fatigue and fracture behaviour, without the need for further numerical analysis. The paper results also show that crack propagation direction can be correlated and predicted using the applied loads and the resulting plastic zone.
- Interpolating CTS specimens' mode I and II stress intensity factors using artificial neural networksPublication . Miguel Gomes Simões Baptista, Ricardo; Infante, Virgínia; Borrego, L. F. P.; Sérgio, Edmundo R.; Neto, Diogo M.; Antunes, F. V.Fracture mechanics parameters, such as the stress intensity factor (SIF), are fundamental for the analysis of fracture, fatigue crack growth and crack paths. SIFs of a cracked body can be determined either experimentally or numerically. Analytical solutions of SIF are very useful, but their determination from discrete values can be extremely complex when there are many independent variables. In this paper, artificial neural networks (ANN) are proposed to predict mode I and II stress intensity factors in a CTS specimen under mixed mode loading conditions. Trained with numerical data, the performance of different network architectures and backpropagation algorithms was assessed. Using at least 10 neurons, in the hidden layers, made it possible for the designed solution to match the performance of analytical solutions. Increasing the number of neurons, allowed the model performance to improve up to 90%, when compared with previous analytical solutions. This increases the quality of fracture and fatigue studies done with the CTS sample.
- Combining artificial intelligence with diferente plasticity induced crack closure criteria to determine opening and closing loads on a three-dimensional centre cracked specimenPublication . Miguel Gomes Simões Baptista, Ricardo; Infante, VirgíniaFracture due to fatigue crack growth remains a significant failure mode in both brittle and ductile materials. When dealing with crack tip plasticity induced phenomena, characterized by high strain and stress field gradients, only highly refined meshes around the crack tip can produce accurate results. Therefore, optimized mesh parameters must be used, in order to achieve high quality models with low computational costs. In this study, artificial intelligence models and a numerical three-dimensional model for a middle tension specimen were combined to enhance crack closure and opening loads assessment. The numerical accuracy was analysed based on the estimated stress and strain fields, plastic zone shape and size and crack closure and opening load values. Two artificial neural networks were trained using four different crack lengths, mesh sizes and simulated plastic wakes. The networks were capable of stress and strain field predictions and crack opening and closure load determination. It was verified that the crack stress criterion is strongly correlated with the principal strain field and the displacement field around the crack tip, providing a viable way to analyse plasticity induced crack closure.
- Study of fatigue crack propagation on modified CT specimens under variable amplitude loading using machine learningPublication . Santos, B.; Infante, Virginia; Barros, T.; Miguel Gomes Simões Baptista, RicardoThis study focuses on predicting fatigue crack paths and fatigue life in modified compact tension specimens, under mixed mode and variable amplitude loading conditions, using Machine Learning techniques. Mixed-mode conditions were induced by using specimens that incorporated holes with different radii and center coordinates. Initially, multiple Finite Element Method (FEM) simulations were conducted to determine the fatigue crack path for different configurations. Subsequently, several configurations were selected for experimental fatigue testing, in which the fatigue crack path was monitored and recorded. The final phase of the study involved Machine Learning (ML) techniques, specifically Artificial Neural Networks (ANN) and k-Nearest Neighbors (kNN), to predict fatigue crack propagation. The models were trained using different numerical and experimental data. Predicted results were then compared with experimentally tested data, and the behavior and accuracy of the models were evaluated. Overall, the implemented models demonstrated the ability to predict fatigue crack path with average deviations (ANN - 1.19 mm; kNN - 1.10 mm) closely resembling results obtained through Finite Element simulations (1.65 mm). The models were also able to predict fatigue life with average errors of 10.1 % (ANN) and 16.7 % (kNN), all achieved with a reduction of computational costs greater than 90 %.