Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods.

Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.

Mesoscale bicontinuous networks in self-healing hydrogels delay fatigue fracture.

Load-bearing biological tissues, such as muscles, are highly fatigue-resistant, but how the exquisite hierarchical structures of biological tissues contribute to their excellent fatigue resistance is not well understood. In this work, we study antifatigue properties of soft materials with hierarchical structures using polyampholyte hydrogels (PA gels) as a simple model system. PA gels are tough and self-healing, consisting of reversible ionic bonds at the 1-nm scale, a cross-linked polymer network at the 10-nm scale, and bicontinuous hard/soft phase networks at the 100-nm scale. We find that the polymer network at the 10-nm scale determines the threshold of energy release rate G0 above which the crack grows, while the bicontinuous phase networks at the 100-nm scale significantly decelerate the crack advance until a transition Gtran far above G0 In situ small-angle X-ray scattering analysis reveals that the hard phase network suppresses the crack advance to show decelerated fatigue fracture, and Gtran corresponds to the rupture of the hard phase network.

Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel.

Evaluating the condition of a Hadfield steel crossing nose using existing inspection methods is subject to accessibility and geographical constraints. Thus, the use of conditional monitoring techniques to complement the existing inspection methods has become increasingly necessary. This paper focuses on the study of acoustic emission (AE) behaviour and its correlation with fatigue crack growth in Hadfield steel during bending fatigue tests. The probability density function for acoustic emission parameters was analysed based on the power law distribution. The results show that a sharp increase in the moving average and cumulative sum of the AE parameter can give early warning against the final failure of Hadfield steel. Two parts (Part 1 and Part 2) can be identified using the change in the slope of duration rate (dD/dN) vs. ΔK plot during the stable fatigue crack growth (FCG) process where Paris's law is valid. The fitted power law exponent of AE parameters is smaller in Part 2 than in Part 1. The novelty of this research lies in the use of the fitted power law distribution of AE parameters for monitoring fatigue damage evolution in Hadfield steel, unlike existing AE fatigue monitoring methodology, which relies solely on the analysis of AE parameter trends.

Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking.

The acoustic emission (AE) method is a popular and well-developed method for passive structural health monitoring of metallic and composite structures. The current study focuses on the analysis of one of its processes, sound source or signal propagation. This paper discusses the principle of plate wave signal sensing using piezoelectric transducers, and derives an analytical expression for the response of piezoelectric transducers under the action of stress waves, to obtain an overall mathematical model of the acoustic emission signal from generation to reception. The acoustic emission caused by fatigue crack extension is simulated by a finite element method, and the actual acoustic emission signal is simulated by a pencil lead break experiment. The results predicted by the mathematical model are compared with the experimental results and the simulation results, respectively, and show good agreement. In addition, the presence of obvious S0 mode Lamb waves is observed in the simulation results and experimental results, which further verifies the correctness of the analytical model prediction.

Cycling-induced structural damage/degradation of electrode materials-microscopic viewpoint.

Most analyses of the mechanical deformation of electrode materials of lithium-ion battery in the framework of continuum mechanics suggest the occurring of structural damage/degradation during the de-lithiation phase and cannot explain the lithiation-induced damage/degradation in electrode materials, as observed experimentally. In this work, we present first-principle analysis of the interaction between two adjacent silicon atoms from the Stillinger-Weber two-body potential and obtain the critical separation between the two silicon atoms for the rupture of Si-Si bonds. Simple calculation of the engineering-tensile strain for the formation of Li-Si intermetallic compounds from the lithiation of silicon reveals that cracking and cavitation in lithiated silicon can occur due to the formation of Li-Si intermetallic compounds. Assuming the proportionality between the net mass flux across the tip surface of a slit crack and the migration rate of the crack tip, we develop analytical formulas for the growth and healing of the slit crack controlled by lithiation and de-lithiation, respectively. It is the combinational effects of the state of charge, the radius of curvature of the crack tip and local electromotive force that determine the cycling-induced growth and healing of surface cracks in lithiated silicon.

Fatigue crack growth in epoxy polymer nanocomposites.

The present paper describes detailed analyses of experimental data for the cyclic-fatigue behaviour of epoxy nanocomposite polymers. It has been shown that the data may be interpreted using the Hartman-Schijve relationship to yield a unique, 'master', linear relationship for each epoxy nanocomposite polymer. By fitting the experimental data to the Hartman-Schijve relationship, two key materials parameters may be deduced: (i) the term A, which may be thought of as the fatigue equivalent to the quasi-static value of the fracture energy, Gc, and (ii) the fatigue threshold value, [Formula: see text], below which no significant fatigue crack growth (FCG) occurs. It has then been established that the values of these parameters, together with the slope, n, and intercept, D, of the Hartman-Schijve master relationship, may be used (i) to compute the experimental results measured for the fatigue behaviour of the epoxy nanocomposite polymers, (ii) to understand the observed fracture and fatigue behaviour of these materials with respect to the structure of the epoxy nanocomposite polymers, and (iii) to deduce the 'upper-bound', i.e. 'worst-case', FCG rate curve which may be used by industry as a material development, material selection, design and service-life prediction tool when these epoxy nanocomposite polymers are used in engineering applications such as structural adhesives and/or as matrices in fibre-reinforced composites. This article is part of a discussion meeting issue 'A cracking approach to inventing new tough materials: fracture stranger than friction'.

Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis.

Mooring systems are an integral and sophisticated component of offshore assets and are subject to harsh conditions and cyclic loading. The early detection and characterisation of fatigue crack growth remain a crucial challenge. The scope of the present work was to establish filtering and alarm criteria for different crack growth stages by evaluating the recorded signals and their features. The analysis and definition of parametrical limits, and the correlation of their characteristics with the crack, helped to identify approaches to discriminate between noise, initiation, and growth-related signals. Based on these, a filtering criterion was established, to support the identification of the different growth stages and noise with the aim to provide early warnings of potential damage.

A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation.

Accurate damage detection in engineering structures is a critical part of structural health monitoring. A variety of non-destructive inspection methods has been employed to detect the presence and severity of the damage. In this research, machine learning (ML) algorithms are used to assess the dynamic response of the system. It can predict the damage severity, damage location, and fundamental behaviour of the system. Fatigue damage data of aluminium and ABS under coupled mechanical loads at different temperatures are used to train the model. The model shows that natural frequency and temperature appear to be the most important predictive features for aluminium. It appears to be dominated by natural frequency and tip amplitude for ABS. The results also show that the position of the crack along the specimen appears to be of little importance for either material, allowing simultaneous prediction of location and damage severity.

Fatigue-Crack Detection and Monitoring through the Scattered-Wave Two-Dimensional Cross-Correlation Imaging Method Using Piezoelectric Transducers.

Piezoelectric transducers are convenient enablers for generating and receiving Lamb waves for damage detection. Fatigue cracks are one of the most common causes for the failure of metallic structures. Increasing emphasis on the integrity of critical structures creates an urgent need to monitor structures and to detect cracks at an early stage to prevent catastrophic failures. This paper presents a two-dimensional (2D) cross-correlation imaging technique that can not only detect a fatigue crack but can also precisely image the fatigue cracks in metallic structures. The imaging method was based on the cross-correlation algorithm that uses incident waves and the crack-scattered waves of all directions to generate the crack image. Fatigue testing for crack generation was then conducted in both an aluminum plate and a stainless-steel plate. Piezoelectric wafer transducer was used to actuate the interrogating Lamb wave. To obtain the scattered waves as well as the incident waves, a scanning laser Doppler vibrometer was adopted for acquiring time-space multidimensional wavefield, followed with frequency-wavenumber processing. The proof-of-concept study was conducted in an aluminum plate with a hairline fatigue crack. A frequency-wavenumber filtering method was used to obtain the incident wave and the scattered wave wavefields for the cross-correlation imaging. After this, the imaging method was applied to evaluate cracks on a stainless-steel plate generated during fatigue loading tests. The presented imaging method showed successful inspection and quantification results of the crack and its growth.

Analytical and Experimental Study of Fatigue-Crack-Growth AE Signals in Thin Sheet Metals.

The acoustic emission (AE) method is a very popular and well-developed method for passive structural health monitoring of metallic and composite structures. AE method has been efficiently used for damage source detection and damage characterization in a large variety of structures over the years, such as thin sheet metals. Piezoelectric wafer active sensors (PWASs) are lightweight and inexpensive transducers, which recently drew the attention of the AE research community for AE sensing. The focus of this paper is on understanding the fatigue crack growth AE signals in thin sheet metals recorded using PWAS sensors on the basis of the Lamb wave theory and using this understanding for predictive modeling of AE signals. After a brief introduction, the paper discusses the principles of sensing acoustic signals by using PWAS. The derivation of a closed-form expression for PWAS response due to a stress wave is presented. The transformations happening to the AE signal according to the instrumentations we used for the fatigue crack AE experiment is also discussed. It is followed by a summary of the in situ AE experiments performed for recording fatigue crack growth AE and the results. Then, we present an analytical model of fatigue crack growth AE and a comparison with experimental results. The fatigue crack growth AE source was modeled analytically using the dipole moment concept. By using the source modeling concept, the analytical predictive modeling and simulation of the AE were performed using normal mode expansion (NME). The simulation results showed good agreement with experimental results. A strong presence of nondispersive S0 Lamb wave mode due to the fatigue crack growth event was observed in the simulation and experiment. Finally, the analytical method was verified using the finite element method. The paper ends with a summary and conclusions; suggestions for further work are also presented.

Fatigue Testing of Pipeline Welds and Heat-Affected Zones in Pressurized Hydrogen Gas.

Several welds and associated heat-affected zones (HAZs) on two API X70 and two API X52 pipes were tested to determine the fatigue crack growth rate (FCGR) in pressurized hydrogen gas and assess the area of the pipe that was most susceptible to fatigue when subjected to hydrogen gas. The relationship between FCGRs for welds and HAZs compared to base metal is discussed relative to local residual stresses, differences in the actual path of the crack, and hydrogen pressure effects.

Gigacycle fatigue in high strength steels.

This paper reviews the research results to date on gigacycle fatigue caused by internal fractures in high strength steels. Firstly, accelerated fatigue testing was realized using ultrasonic fatigue testing at 20 kHz, which completes 109 cycles in one day, unlike the 3-4 months needed for conventional fatigue testing. Although the frequency effect was anticipated to be problematic, it proved negligible under conditions in which internal fractures occurred. Later, many unique characteristics of internal fractures were elucidated. For example, hydrogen has dramatically greater effects on internal fractures than on conventional surface fractures. Mean stress effects are more serious in titanium alloys than in high strength steels. Size effects were notable in high strength steels. These distinctive characteristics required a unique model to be able to predict gigacycle fatigue strength, which first required elucidation of its mechanisms. To this aim, the author attempted to measure the crack growth rates of small internal cracks using the beach mark method. The results revealed that the crack growth of small internal cracks controls internal fractures. In calculating the crack growth life, however, it was found that the conventional crack growth law overestimates the effects of inclusion size. To rectify this problem, a new model using a new crack growth law was proposed, which predicts more realistic fatigue life curves. As a result, predictions were derived for several grades of high strength steels.

Lamb Wave-Minimum Sampling Variance Particle Filter-Based Fatigue Crack Prognosis.

Fatigue cracks are one of the common failure types of key aircraft components, and they are the focus of prognostics and health management (PHM) systems. Monitoring and prediction of fatigue cracks show great application potential and economic benefit in shortening aircraft downtime, prolonging service life, and enhancing maintenance. However, the fatigue crack growth process is a non-linear non-Gaussian dynamic stochastic process, which involves a variety of uncertainties. Actual crack initiation and growth sometimes deviate from the results of fracture mechanics analysis. The Lamb wave-particle filter (LW-PF) fatigue-crack-life prediction based on piezoelectric transducer (PZT) sensors has the advantages of simple modeling and on-line prediction, making it suitable for engineering applications. Although the resampling algorithm of the standard particle filter (PF) can solve the degradation problem, the discretization error still exists. To alleviate the accuracy decrease caused by the discretization error, a Lamb wave-minimum sampling variance particle filter (LW-MSVPF)-based fatigue crack life prediction method is proposed and validated by fatigue test of the attachment lug in this paper. Sampling variance (SV) is used as a quantitative index to analyze the difference of particle distribution before and after resampling. Compared with the LW-PF method, LW-MSVPF can increase the prediction accuracy with the same computational cost. By using the minimum sampling variance (MSV) resampling method, the original particle distribution is retained to a maximum degree, and the discretization error is significantly reduced. Furthermore, LW-MSVPF maintains the characteristic of dimensional freedom, which means a broader application in on-line prognosis for more complex structures.

Prediction of Fatigue Crack Growth Rate Based on Entropy Generation.

This paper presents the assessment of fatigue crack growth rate for dual-phase steel under spectrum loading based on entropy generation. According to the second law of thermodynamics, fatigue crack growth is related to entropy gain because of its irreversibility. In this work, the temperature evolution and crack length were simultaneously measured during fatigue crack growth tests until failure to ensure the validity of the assessment. Results indicated a significant correlation between fatigue crack growth rate and entropy. This relationship is the basis in developing a model that can determine the characteristics of fatigue crack growth rates, particularly under spectrum loading. Predictive results showed that the proposed model can accurately predict the fatigue crack growth rate under spectrum loading in all cases. The root mean square error in all cases is 10-7 m/cycle. In conclusion, entropy generation can accurately predict the fatigue crack growth rate of dual-phase steels under spectrum loading.

In Situ Scanning Electron Microscope (SEM) Observations of Damage and Crack Growth of Shale.

To better understand the formation and evolution of hierarchical crack networks in shales, observations of microscopic damage, and crack growth were conducted using an in situ tensile apparatus inside a scanning electron microscope. An arched specimen with an artificial notch incised into the curved edge was shown to afford effective observation of the damage and crack growth process that occurs during the brittle fracturing of shale. Because this arched specimen design can induce a squeezing effect, reducing the tensile stress concentration at the crack tip, and preventing the brittle shale from unstable fracturing to some extent. Both induced and natural pores and cracks were observed at different scales around the main crack path or on fractured surfaces. Observations indicate that the crack initiation zone develops around the crack tip where tensile stresses are concentrated and micro/nanoscale cracks nucleate. Crack advancement generally occurs by the continuous generation and coalescence of damage zones having intermittent en echelon microscopic cracks located ahead of the crack tips. Mineral anisotropy and pressure build-up around crack tips causes crack kinking, deflection, and branching. Crack growth is often accompanied by the cessation or closure of former branch cracks due to elastic recovery and induced compressive stress. The branching and interactions of cracks form a three-dimensional hierarchical network that includes induced branch cracks having similar paths, as well as natural structures such as nanopores, bedding planes, and microscopic cracks.

An Optical Crack Growth Sensor Using the Digital Sampling Moiré Method.

High-accuracy crack growth measurement is crucial for the health assessment of concrete structures. In this work, an optical crack growth sensor using the digital sampling moiré (DSM) method is developed for two-dimensional (2D) crack growth monitoring. The DSM method generates moiré fringes from a single image through digital image processing, and it measures 2D displacements using the phase difference of moiré fringes between motion. Compared with the previous sensors using traditional photogrammetric algorithms such as the normalized cross-correlation (NCC) method, this new DSM-based sensor has several advantages: First, it is of a higher sensitivity and lower computational cost; second, it requires no prior calibration to get accurate 2D displacements which can greatly simplify the practical application for multiple crack monitoring. In addition, it is more robust to the change of imaging distance, which is determined by the height difference between two sides of a concrete crack. These advantages break the limitation of the NCC method and broaden the applicability of the crack growth sensor. These advantages have been verified with one numerical simulation and two laboratory tests.

Fatigue crack closure: a review of the physical phenomena.

Plasticity-induced, roughness-induced and oxide-induced crack closures are reviewed. Special attention is devoted to the physical origin, the consequences for the experimental determination and the prediction of the effective crack driving force for fatigue crack propagation. Plasticity-induced crack closure under plane stress and plane strain conditions require, in principle, a different explanation; however, both types are predictable. This is even the case in the transition region from the plane strain to the plane stress state and all types of loading conditions including constant and variable amplitude loading, the short crack case or the transition from small-scale to large-scale yielding. In contrast, the prediction of roughness-induced and oxide-induced closures is not as straightforward.

Real-time, high-resolution study of nanocrystallization and fatigue cracking in a cyclically strained metallic glass.

Metallic glasses (MGs) exhibit greater elastic limit and stronger resistance to plastic deformation than their crystalline metal counterparts. Their capacity to withstand plastic straining is further enhanced at submicrometer length scales. For a range of microelectromechanical applications, the resistance of MGs to damage and cracking from thermal and mechanical stress or strain cycling under partial or complete constraint is of considerable scientific and technological interest. However, to our knowledge, no real-time, high-resolution transmission electron microscopy observations are available of crystallization, damage, and failure from the controlled imposition of cyclic strains or displacements in any metallic glass. Here we present the results of a unique in situ study, inside a high-resolution transmission electron microscope, of glass-to-crystal formation and fatigue of an Al-based MG. We demonstrate that cyclic straining progressively leads to nanoscale surface roughening in the highly deformed region of the starter notch, causing crack nucleation and formation of nanocrystals. The growth of these nanograins during cyclic straining impedes subsequent crack growth by bridging the crack. In distinct contrast to this fatigue behavior, only distributed nucleation of smaller nanocrystals is observed with no surface roughening under monotonic deformation. We further show through molecular dynamics simulation that these findings can be rationalized by the accumulation of strain-induced nonaffine atomic rearrangements that effectively enhances diffusion through random walk during repeated strain cycling. The present results thus provide unique insights into fundamental mechanisms of fatigue of MGs that would help shape strategies for material design and engineering applications.

Microstructural mechanisms of cyclic deformation, fatigue crack initiation and early crack growth.

In this survey, the origin of fatigue crack initiation and damage evolution in different metallic materials is discussed with emphasis on the responsible microstructural mechanisms. After a historical introduction, the stages of cyclic deformation which precede the onset of fatigue damage are reviewed. Different types of cyclic slip irreversibilities in the bulk that eventually lead to the initiation of fatigue cracks are discussed. Examples of trans- and intercrystalline fatigue damage evolution in the low cycle, high cycle and ultrahigh cycle fatigue regimes in mono- and polycrystalline face-centred cubic and body-centred cubic metals and alloys and in different engineering materials are presented, and some microstructural models of fatigue crack initiation and early crack growth are discussed. The basic difficulties in defining the transition from the initiation to the growth of fatigue cracks are emphasized. In ultrahigh cycle fatigue at very low loading amplitudes, the initiation of fatigue cracks generally occupies a major fraction of fatigue life and is hence life controlling.

Sensitivity Analysis of Fatigue Crack Growth Model for API Steels in Gaseous Hydrogen.

A model to predict fatigue crack growth of API pipeline steels in high pressure gaseous hydrogen has been developed and is presented elsewhere. The model currently has several parameters that must be calibrated for each pipeline steel of interest. This work provides a sensitivity analysis of the model parameters in order to provide (a) insight to the underlying mathematical and mechanistic aspects of the model, and (b) guidance for model calibration of other API steels.