Neural networks for fatigue crack propagation predictions in real-time under uncertainty

Crack propagation analyses are fundamental for all mechanical structures for which safety must be guaranteed, e. g. as for the aviation and aerospace fields. The estimation of life for structures in presence of defects is a process inevitably affected by numerous and unavoidable uncertainty and variability sources, whose effects need to be quantified to avoid unexpected failures or excessive conservativism. In this work, residual fatigue life prediction models have been created through neural networks for the purpose of performing probabilistic life predictions of damaged structures in real-time and under stochastically varying input parameters. In detail, five different neural network architectures have been compared in terms of accuracy, computational runtimes and minimum number of samples needed for training, so to determine the ideal architecture with the strongest generalization power. The networks have been trained, validated and tested by using the fatigue life predictions computed by means of simulations developed with FEM and Monte Carlo methods. A real-world case study has been presented to show how the proposed approach can deliver accurate life predictions even when input data are uncertain and highly variable. Results demonstrated that the “H1-L1” neural network has been the best model, achieving an accuracy (Mean Square Error) of 4.8e-7 on the test dataset, and the best and the most stable results when decreasing the amount of data. Additionally, since requiring only very few parameters, its potential applicability for Structural Health Monitoring purposes in small cost-effective GPU devices resulted to be attractive

Electromagnetic nondestructive inspection of aircraft structures by using a magnetic flux leakage method

Aging of aircraft structures is mostly associated with fatigue cracking, de-bonding and corrosion. Detection and characterization of the structural defects at the initiation stages makes it a great challenge for any inspection technology. This study proposes a new solution for the nondestructive evaluation problem by using a magnetic flux method for non-ferromagnetic materials and provides a new neural network tool that predicts crack profiles in three dimensions by solving the inverse problem, where available neural networks can solve it in two dimensions only.;The discontinuity resulting from a crack produces disturbance to the distribution of electrical current density in the structure and as a result the magnetic field around the crack will change. The magnitude of the disturbance is determined by the size and shape of the crack. Therefore, it is possible to evaluate the crack area by magnetic field measurements. The magnetic fields from the plate edges and the wires that carry the current are very strong compared to the magnetic field produced by the crack. A new plate, called a dummy plate, is used to minimize the effect of the magnetic fields produced by the plate edges. This study proves the effectiveness of the dummy plate and shows the measurable change in the magnetic signal around the crack.;As a result of this work, a tool is now available that can solve the nondestructive evaluation problem and the inverse problem in three dimensions and has the capability to provide an enhanced assessment tool for judgment and decision-making which will improve the safety of metallic structures and save people lives

Impact of earthquake’s epicenter distance to failure of the embankment – A seismic prediction

Cracks in clayey soil cause a reduction in the seismic loading capacity which can lead to structural failures. Seismic acceleration is the primary cause of crack propagation and damage to the earth's structure. This study investigated the impact of the earthquake's epicenter distance on the embankment model with a pre-existing crack in the embankment's core. The research adopted the numerical modeling method of soil categorized as a no-tensile material to explain displacement in selected points of the model using the extended finite element method (XFEM). Artificial Neural Networks (ANNs) were used to predict displacement obtained by XFEM. It was observed that the failure pattern and the maximum displacement time of the model change with the associated distance of the earthquake's epicenter. The key study objective is to understand the model's failure mode and introduce a new classification in earthquake damage prediction

Modeling and simulation of continuous fiber-reinforced ceramic composites

Finite element modeling framework based on cohesive damage modeling, constitutive material behavior using user-material subroutines, and extended finite element method (XFEM), are developed for studying the failure behavior of continuous fiber-reinforced ceramic matrix composites (CFCCs) by the example of a silicon carbide matrix reinforced with silicon carbide fiber (SiC/SiCf) composite. This work deals with developing comprehensive numerical models for three problems: (1) fiber/matrix interface debonding and fiber pull-out, (2) mechanical behavior of a CFCC using a representative volume element (RVE) approach, and (3) microstructure image-based modeling of a CFCC using object oriented finite element analysis (OOF). Load versus displacement behavior during a fiber pull-out event was investigated using a cohesive damage model and an artificial neural network model. Mechanical behavior of a CFCC was investigated using a statistically equivalent RVE. A three-step procedure was developed for generating a randomized fiber distribution. Elastic properties and damage behavior of a CFCC were analyzed using the developed RVE models. Scattering of strength distribution in CFCCs was taken into account using a Weibull probability law. A multi-scale modeling framework was developed for evaluating the fracture behavior of a CFCC as a function of microstructural attributes. A finite element mesh of the microstructure was generated using an OOF tool. XFEM was used to study crack propagation in the microstructure and the fracture behavior was analyzed. The work performed provides a valuable procedure for developing a multi-scale framework for comprehensive damage study of CFCCs --Abstract, page iv

Fatigue Life Prediction of Edge-Welded Metal Bellows Using Neural Networks and Multiple Linear Regression

Edge-welded metal bellows present an ongoing challenge: the prediction of an accurate cycle life. Current methods rely on physical leak detection to determine a bellow\u27s cycle life to failure. It is known, however, that crack initiation begins many cycles before a leak path is present. Bellows manufacturers require a method for detection of fatigue cracks when they initiate but before they result in leak rates large enough to contaminate a process. Acoustic emission (AE) testing is one method which can meet this need and is a proven, reliable technique for detecting crack initiation and monitoring fatigue crack growth. Four sets of metal bellows samples were fatigue tested and AE parameter data recorded. The data sets were analyzed and the determination made that amplitude, duration, and time of occurrence were the AE data variables required for separation of the various failure mechanisms. For two of the four materials, an expanded set of tests were performed. Fourteen tests were used to train and test a back-propagation neural network for prediction of bellows cycle life. The input data consisted of a material identifier, AE parameter data consisting of the amplitude distribution (50-100 dB) of the first 250 hits, and the final cycle life. The network was structured with an input layer consisting of the identifier and amplitude data, two hidden layers for mapping failure mechanisms, and an output layer for predicting cycle life. The network required training on four samples for the Inconel 718 and five samples for the 350 stainless steel. Once trained the network was able to predict cycle life with a worst case error of-4.45 percent and 2.66 percent for the Inconel 718 and 350 stainless steel, respectively. Finally, through the use of multiple linear regression, a statistical analysis was made to develop a model capable of accurate prediction. Applying a natural log transformation to the independent variables of amplitude and energy resulted in a model capable of explaining 95 percent of the variability in cycle life prediction

Corrosion Behaviour of Cupronickel 90/10 Alloys in Arabian Sea Conditions and its Effect on Maintenance of Marine Structures

The composition of seawater plays a very significant role in determining the severity of corrosion process in marine assets. The influential contributors to the general and pitting corrosions in marine structures include temperature, dissolved oxygen (DO), salinity, PH, chlorides, pollutants, nutrients, and microbiological activities in seawater. The Cu-Ni (90/10) alloy is increasingly used in marine applications such as heat exchangers and marine pipelines because of its excellent corrosion resistant properties. Despite the significant advancements in corrosion shielding procedures, complete stoppage of corrosion induced metal loss, especially under rugged marine environments, is practically impossible. The selection of appropriate metal thickness is merely a multifaceted decision because of the high variability in operating conditions and associated corrosion rate in various seawater bodies across the globe. The present research study aims to analyze the early phase of corrosion behavior of Cu-Ni (90/10) alloy in open-sea conditions as well as in pollutant-rich coastal waters of the Arabian Sea. Test samples were placed under natural climatic conditions of selected sites, followed by the mass loss and corrosion rate evaluation. The corrosion rate in the pollutant-rich coastal waters was around five times higher than in the natural seawater. A case study on marine condenser (fitted with of Cu-Ni 90/10 alloy tubes) is presented, and a risk-based inspection (RBI) plan is developed to facilitate equipment designers, operators, and maintainers to consider the implications of warm and polluted seawater on equipment reliability, service life, and subsequent health inspection/ maintenance

Fatigue life prediction on nickel base superalloys

Neural networks have been used extensively in material science with varying success.It has been demonstrated that they can be very effective at predicting mechanical properties such as yield strength and ultimate tensile strength. These networks require large amounts of input data in order to learn the correct data trends. A neural network modelling process has been developed which includes data collection methodology and subsequent filtering techniques in conjunction with training of a neural network model.It has been shown that by using certain techniques to ‘improve’ the input data a network will not only fit seen and unseen Ultimate Tensile Strength (UTS) and Yield Strength (YS) data but correctly predict trends consistent with metallurgical understanding.Using the methods developed with the UTS and YS models, a Low Cycle Fatigue (LCF) life model has been developed with promising initial results.Crack initiation at high temperatures has been studied in CMSX4 in both air and vacuum environments, to elucidate the effect of oxidation on the notch fatigue initiation process. In air, crack initiation occurred at sub-surface interdendritic pores in all cases.The sub-surface crack grows initially under vacuum conditions, before breaking out to the top surface. Lifetime is then dependent on initiating pore size and distance from the notch root surface. In vacuum conditions, crack initiation has been observed more consistently from surface or close-to-surface pores - indicating that surface oxidation is in-filling/”healing” surface pores or providing significant local stress transfer to shift initiation to sub-surface pores. Complementary work has been carried out using PWA1484 and Rene N5. Extensive data has been collected on initiating pores for all 3alloys. A model has been developed to predict fatigue life based upon geometrical information from the initiating pores. A Paris law approach is used in conjunction with long crack propagation data. The model shows a good fit with experimental data and further improvements have been recommended in order to increase the capability of the model

A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning

An image-based deep learning framework is developed in this paper to predict damage and failure in microstructure-dependent composite materials. The work is motivated by the complexity and computational cost of high-fidelity simulations of such materials. The proposed deep learning framework predicts the post-failure full-field stress distribution and crack pattern in two-dimensional representations of the composites based on the geometry of microstructures. The material of interest is selected to be a high-performance unidirectional carbon fiber-reinforced polymer composite. The deep learning framework contains two stacked fully-convolutional networks, namely, Generator 1 and Generator 2, trained sequentially. First, Generator 1 learns to translate the microstructural geometry to the full-field post-failure stress distribution. Then, Generator 2 learns to translate the output of Generator 1 to the failure pattern. A physics-informed loss function is also designed and incorporated to further improve the performance of the proposed framework and facilitate the validation process. In order to provide a sufficiently large data set for training and validating the deep learning framework, 4500 microstructural representations are synthetically generated and simulated in an efficient finite element framework. It is shown that the proposed deep learning approach can effectively predict the composites' post-failure full-field stress distribution and failure pattern, two of the most complex phenomena to simulate in computational solid mechanics