Predict Fatigue Crack Propagation Research Articles

Investigation of individual lack-of-fusion defects in the fatigue performance of laser-powder bed fusion Ti6Al4V alloys: A finite element analysis

Laser-Powder Bed Fusion (L-PBF) techniques have revolutionized the production of Ti6Al4V alloys across various industries. However, the widespread adoption of L-PBF Ti6Al4V alloys is impeded by their inadequate fatigue performance, particularly in high cycle regimes. A significant contributing factor to this limitation is the presence of internal defects inherent to the L-PBF process, act as sites for fatigue crack initiation. Previous investigations have focused on the fatigue performance of L-PBF Ti6Al4V alloys with gas porosities, while research on lack-of-fusions (LOFs) which are recognized as the most detrimental defects, remains limited. In order to deepen our understanding of the factors influencing the reduced fatigue performance observed in L-PBF Ti6Al4V alloys associated with inherent LOFs, this study employed three-dimensional (3D) finite element analysis approaches. Such approaches allow to assess fatigue indicator parameters to evaluate fatigue life and predict fatigue crack propagation. A 3D average Smith-Watson-Topper method has been proposed, which is able to rationally estimate the fatigue life of L-PBF Ti6Al4V. In addition, a novel finite element method has been developed to accurately calculate stress intensity factors along irregular shaped crack front of a notch-like feature embedded on a LOF. Additionally, parametric studies were conducted to gain further insights into the influence of LOFs on fatigue performance. The results shown the presence of embedded humps and notch-like features, and their topologies play key roles in the fatigue performance of LOF predominated L-PBF Ti6Al4V alloys.

Research on Fatigue Crack Propagation Prediction for Marine Structures Based on Automated Machine Learning

Research on Fatigue Crack Propagation Prediction for Marine Structures Based on Automated Machine Learning

High-temperature thermal fatigue of Ni3Al-based single crystal alloy affected by wall thickness and peak temperature

Complex thin-walled structures are commonly employed in turbine components to improve cooling efficiency. However, thin-walled blades are prone to thermal fatigue failure due to the high thermal stress induced by frequent and abrupt temperature fluctuations during duty cycles. The thermal fatigue behavior of Ni3Al-based single crystal alloy with thin-walled structure during 25 °C ↔ 1000 °C/1100 °C was investigated by combining thermal fatigue tests and finite element simulation. The thermal fatigue demonstrated a significant thickness debit effect, with thermal fatigue property dropping as peak temperature and wall thickness increased. Wall thickness had a more pronounced effect than peak temperature. Visible slip traces were found on the wall thickness surface, and their density was positively correlated with wall thickness. Further investigation revealed that thermal fatigue is primarily driven by the octahedral slip system ({111}<110>), with significant contributions from (111)[101‾] and (1‾1‾1)[101]. Simulations of thermal stress distribution and J-integral at the crack tip during crack initiation and propagation stages indicated that wall thickness influences thermal fatigue properties by affecting thermal stress concentration at the notch and crack tip. Higher peak temperatures increased thermal stress and reduced yield strength, thus diminishing thermal fatigue performance. A thermal fatigue crack propagation prediction model was constructed incorporating Paris' law and the J-integral. The model revealed an unfavorable correlation between material Cj/ nj and both peak temperature/wall thickness, indicating that both factors adversely affect thermal fatigue resistance.

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.

Study of fatigue crack propagation on modified CT specimens under variable amplitude loadings using machine learning

This 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 %.

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Analytical study of CFRP strengthening steel plates with a semi-elliptical surface crack

Flaws and defects are unavoidable in the manufacturing process of steel members. Surface cracks are a common type of defect in steel structures that can introduce fatigue failure under cyclic loading. In this study, stress analysis of carbon fiber reinforced polymer (CFRP) repaired specimens with a semi-elliptical surface crack was conducted by theoretical analysis. The analysis was used to calculate stress intensity factors at crack tips. Finite element analysis was carried out to verify the proposed equations considering CFRP strengthening. Both single-sided and double-sided strengthening methods were considered. Thereafter, a modified Paris Law model was adopted to predict fatigue crack propagation life. Several parameters, which are crack aspect ratio, crack depth ratio, layers of CFRP, CFRP width and stress range are considered to analyze their influence on fatigue life. It was found that the augmentation of crack aspect ratio, layers of CFRP and CFRP width can significantly increase fatigue crack propagation life of repaired specimens. Furthermore, single-sided repairing on cracked side is better than double-sided repairing.

Application of digital image correlation to derive Paris' law constants in granite specimens

This study investigates the effectiveness of Digital Image Correlation (DIC) as a powerful tool for deriving Paris' law constants in black granite specimens subjected to cyclic loading. A series of monotonic and cyclic loading tests on rectangular granite specimens yields valuable insights into geological material fatigue behavior, establishing monotonic tests as foundational references for subsequent cyclic loading experiments. Innovative DIC software application enables precise tracking and analysis of Crack Mouth Opening Displacement (CMOD) values, revealing distinct, observable patterns in crack growth during cyclic loading. A notable contribution lies in the implementation of a compliance calibration technique integrating Finite Element Method (FEM), facilitating the direct linkage between CMOD values and effective crack lengths, resulting in highly accurate Paris' law constants. With an average of 1.04 E −7 for C and 8.95 for m, these constants are essential for predicting fatigue crack propagation in granite specimens. This research underscores DIC's efficacy as a pivotal fatigue analysis tool, enhancing predictive capabilities for structural safety and durability assessments in geological materials, while emphasizing the crucial role of advanced imaging and numerical techniques in material science and structural engineering. It lays a robust foundation for future geological material studies and comprehensive structural integrity assessments.

Prediction of fatigue crack propagation in center cracked steel plate strengthened with partially covered CFRP strip

Evaluating the fatigue performance of steel members strengthened with carbon fiber reinforced polymer (CFRP) is of great significance for formulating maintenance strategies and making full use of materials. Current analytical models are mainly developed for fully covered CFRP strengthening method without considering different mechanisms at each stage. In this paper, an analytical model considering different stages of crack propagation was proposed to predict the fatigue performance of center cracked steel plates strengthened with partially covered CFRP strips. The strengthening effects of CFRP strips were numerically analyzed based on the extended finite element model (XFEM) calibrated by test data. The validity of the analytical model was checked by several sets of test data and numerical simulations. The results show that the proposed analysis model can reasonably predict the fatigue life of center cracked steel plates strengthened with partially covered CFRP strips. This study may provide a reference for the application of the partially covered CFRP fatigue strengthening method in engineering structures.

Stress intensity factor and fatigue life evaluation for important points of semi-elliptical cracks in welded pipeline by Bezier extraction based XIGA and new correlation model

NURBS-based Bezier extraction in extended isogeometric analysis (XIGA) using thermo elastic-plastic equation has been proposed for evaluating the stress intensity factor (SIF) in the various relative crack depths, aspect ratios, and important points (surface points and deepest point) of the semi-elliptical surface crack in weld line of pipelines under cyclic internal pressure. In this approach, the level set technique is used for identifying the grid points around the crack front and the crack surface. Crack in the specified domain is modeled by Heaviside and crack front enrichment functions and remeshing due to crack growth does not require. Also, new correlation models are proposed to predict SIF in these points of the crack front. Based on the proposed correlations, fatigue crack propagation, and fatigue life are investigated. Modified walker equation used for predicting fatigue crack propagation rate and fatigue life in the welded pipeline. The finite element method (FEM) analysis with a similar condition was performed and compared with XIGA and correlation models to validate the presented method. The results demonstrate good agreement. Thus, the proposed approach successfully studies the 3D crack problem with acceptable accuracy.

Prediction of fatigue crack propagation in center cracked steel plate strengthened with prestressed CFRP strip

Evaluating the fatigue behavior of steel structures strengthened with prestressed carbon fiber reinforced polymer (CFRP) is of great significance for structural repair and rehabilitation. In this paper, an analytical model based on linear elastic fracture mechanics considering the additional elongation of the steel plate caused by the central crack opening was proposed to predict the fatigue behavior of prestressed CFRP strips strengthened steel plates. The finite element model calibrated by experiments was used to verify the proposed model. The results showed that the proposed model can predict the stress state of various components, the stress intensity factor SIF amplitude, the fatigue crack growth life, and the minimum prestress of CFRP strips required for crack arrest under different strengthening conditions with high accuracy. This study will be conducive to a better application of the prestressed CFRP strengthening of thin-walled steel structures under fatigue loading.

Mixed-mode fatigue crack propagation simulation by means of [formula omitted] and walker models of the structural steel S355

In this paper, a numerical simulation method of mixed-mode fatigue crack propagation was explored using the extended finite element method (XFEM) and the Virtual Crack Closure Technique (VCCT). Both 2D and 3D numerical models were selected to simulate the fatigue crack propagation of steel specimens. Two coefficients were proposed to calculate the equivalent energy release rate (Geq) for a better simulation of the mixed-mode fatigue crack propagation of S355 steel. The Walker equation and the calculation formula of Geq were realized by a user-defined subroutine. A set of optimal correction coefficients of mode II energy release rate (GII) and mode III energy release rate (GIII) were quantitatively comparing the simulation results and test data. The results will contribute to fatigue crack propagation prediction of steel structures in the civil engineering field.

Fatigue Crack Propagation Prediction of Corroded Steel Plate Strengthened with Carbon Fiber Reinforced Polymer (CFRP) Plates.

The purpose of this study is to investigate the mechanism of improving fatigue performance and the estimation model of fatigue life for corroded steel plate strengthened with CFRP plates. A new two-stage fatigue crack propagation prediction model for the corroded steel plate strengthened with CFRP plates was proposed; moreover, the identification of critical rust pits and the equivalent method of initial cracks, and the calculation method of stress intensity factor (SIF) values at the crack tip were established. The accuracy of the proposed model was verified by comparing the predicted and tested fatigue life of the corroded steel plate strengthened with CFRP plates. Finally, the proposed two-stage crack propagation model was applied to carry out a parameter analysis to investigate the effect of weight loss rate, equivalent initial crack size, adhesive thickness, CFRP stiffness and CFRP prestress level on the fatigue crack propagation of the corroded steel plate strengthened with CFRP plates. Results showed that the maximum depth and the average width of the rust pits were suggested to be taken as the equivalent dimensions of the initial semi-elliptical surface crack for the fatigue crack propagation prediction of corroded steel plate strengthened with CFRP plates. Increasing the weight loss rate of the corroded steel plate, the initial crack size or the adhesive thickness would accelerate the crack growth and reduce the fatigue life, whereas increasing the stiffness or prestress level of the CFRP plate would significantly reduce the crack growth rate and increase the fatigue life. The smaller the initial crack size, the more sensitive the crack propagation life was to the variation of equivalent initial crack size. The influence of adhesive thickness on the fatigue life was limited and convergent, and the application of prestressing could significantly improve the utilization rate of CFRP materials and the fatigue strengthening effect of the corroded steel plate.

Prediction of fatigue crack propagation based on dynamic Bayesian network

To address the problem of low prediction accuracy in the current research on fatigue crack propagation prediction, a prediction method of fatigue crack propagation based on a dynamic Bayesian network is proposed in this paper. The Paris Law of crack propagation and the extended finite element method (XFEM) are combined to establish the state equation of crack propagation. The uncertain factors of crack propagation are analyzed, and the prediction model of fatigue crack propagation based on the dynamic Bayesian network is constructed. A Bayesian inference algorithm based on the combination of Gaussian particle filter and firefly algorithm is proposed. The fatigue experiment of the specimen with the pre-cracks is carried out to test the correlation between the fatigue load cycles and the crack propagation depth. The experimental results show that the crack propagation prediction method proposed in this paper can effectively improve the prediction accuracy of crack propagation depth.

Fatigue crack propagation across grain boundary of Al-Cu-Mg bicrystal based on crystal plasticity XFEM and cohesive zone model

In this paper, a methodology integrating crystal plasticity (CP), the eXtended finite element method (XFEM) and the cohesive zone model (CZM) is developed for an Al-Cu-Mg alloy to predict fatigue crack propagation (FCP) across grain boundary (GB) of Al-Cu-Mg alloy during stage ІІ. One GB model is incorporated into FCP constitutive law to describe grain interaction at GB. A bicrystal containing GB is built up to simulate FCP behavior through L participated GBs. Modelling features including GB characteristic, cumulative plastic strain (CPS) distribution and crystal slipping evidence can be identified. The numerical results are compared with published experimental data to check the accuracy of model. This work demonstrates that the combination of CP containing GB constitutive laws, XFEM and CZM is a promising methodology in predicting twist angle-controlled crack deflection through GBs.

The influence of bending loading on surface fatigue crack growth life

Engineering structures often serve under complex fatigue loading and the most common of which are tension and bending loading, or their combination. Fatigue cracks always initiate as surface and other three dimensional (3D) cracks and most of the fatigue life of structures spends on 3D crack growth, but fatigue crack propagation curves of materials are widely obtained using through-the-thickness specimens. In this work, a 3D fracture mechanics based method is developed to evaluate surface crack propagation life under combined tension and bending loadings by considering the plasticity induced fatigue crack closure. The method is validated firstly against available experimental data of surface fatigue cracked plates under the combined loadings, and then applied to predict fatigue crack propagation life of surface cracked railway axles under rotating bending. It is shown that the method can make prediction of life within 0.80 to 1.08 of the test life, and the predicted shape evolution of surface crack agrees well with experimental observation. With a modification of short crack closure being made and an equivalent initial crack being assumed, the proposed method can be extended to fatigue life prediction. It is found that the influence of bending loading on fatigue life increasing for larger initial cracks, especially in the submillimeter range. This fracture mechanics based method can serve as a unified way to calculate fatigue crack growth life as well as fatigue life of practical structures under both tension and bending loadings by use of the material crack growth curves obtained with straight-through cracked standard specimens.

Prediction of fatigue crack propagation in metals based on IBAS-PF

Owing to particle leanness, the standard Particle Filter (PF) algorithm is prone to the problem of reduced prediction accuracy when predicting fatigue crack propagation. An improved particle filter algorithm based on the optimization algorithm of beetle antenna search (IBAS-PF) for fatigue crack propagation in metals is proposed in this paper. The discrete Paris formula was used to establish the state equation of fatigue crack propagation, in which the uncertainty of material and crack propagation process were considered. Meanwhile, the characteristics of Lamb wave signals under different crack lengths were extracted to establish the observation equation. The sampling process of the PF algorithm was optimized based on the beetle antennae search algorithm to improve the particle diversity and the prediction accuracy. Compared with the standard PF algorithm, the improved BASO-PF algorithm has higher accuracy for metal fatigue crack propagation, as well as better state estimation ability.

Prediction of fatigue crack propagation lives based on machine learning and data-driven approach

Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation (FCP) life of the metal structure based on the existing FCP model, while the prediction method based on machine learning (ML) and data-driven approach can provide a new idea for accurately predicting the FCP life of the metal structure. In response to the inconvenience of the online prediction method and the inaccuracy of the offline prediction method, an improved offline prediction method based on data feedback is presented in this paper. FCP tests of reduced scale models of balcony opening corners in a cruise ship are conducted to obtain experimental data with respect to the a-N curves. The crack length corresponding to the cycle is trained using a support vector regression (SVR) and back propagation neural network (BP NN) algorithms. FCP prediction lives of test specimens are performed according to the online, offline, and improved offline prediction methods. Effects of the number of feedback data, the sequence length (SL) in the input set, and the cycle interval on prediction accuracy are discussed. The generalization ability of the proposed method is validated by comparing the prediction results with the experimental data in the literature. The larger the number of feedback data, the higher the prediction accuracy. The results show that 1/5 and 1/2 feedback data are needed in the SVR and BP NN algorithm with SL is 5, respectively. Furthermore, the SVR algorithm and SL=5 are recommended for FCP life prediction using the improved offline prediction method.

Investigations of fatigue crack propagation in ER8 railway wheel steel with varying microstructures

In this study, fatigue crack occurrences and propagations of high-speed railway wheel steel grade ER8 were investigated through both experiments and FE simulations. Three varying microstructures of the wheel steel including as-received, ferrite- and bainite-containing microstructures, which were obtained by specific heat treatment routes, were characterized. Stress-controlled fatigue tests were carried out for determining relationships between crack propagation rate and stress intensity factor of the steels. In addition, FE simulations of fatigue crack developments on both macro- and micro-scale using representative volume element (RVE) 2D models were conducted with consideration of microstructural features. The extended finite element method (XFEM) coupled with the Paris law was applied in the cyclic simulations for predicting crack propagation. For the microstructure level, flow stress-strain curves based on micromechanics approach and corresponding fatigue damage parameters of different phases in steels were identified and afterwards calibrated with experimental results. Finally, influences of ferrite, pearlite and bainite on fatigue crack resistance in the wheel steel were precisely examined by the RVE model. It was found that fatigue micro-cracks mostly initiated close to interface regions between soft and hard phases. The bainitic phase could obviously retard fatigue crack propagation of steel, whereas the ferritic phase contrarily exhibited deteriorated crack resistance. Moreover, the proposed framework could be acceptably employed for predicting fatigue crack propagation behavior and its rate in railway wheel subjected to microstructure changes occurred.

Coupling of phase field and viscoplasticity for modelling cyclic softening and crack growth under fatigue

A coupled phase field-viscoplasticity approach was developed to model the deformation and crack growth in a nickel-based superalloy under fatigue. The coupled model has an advantage in predicting the cyclic softening behavior of the alloy caused by fatigue damage, overcoming a major limitation of the original cyclic viscoplasticity model. The coupled approach is also highly effective in predicting fatigue crack propagation under varied dwell times at peak load, an important behavior for crack growth under dwell fatigue. By incorporating the stress state factor, the coupled model is further utilized to investigate the growth behavior of 3D cracks under fatigue. Both the geometrical feature of the 3D crack front and the overall crack growth rate are well captured, confirming the predicative capability of the coupled model.