Acoustic Emission of Metallic Specimen with Surface Defect During Fatigue Crack Growth

Acoustic emission is defined as the phenomena whereby transient elastic waves are generated by the rapid release of localized sources within a material. During fatigue crack growth, the formation of new crack surfaces is associated with a sudden release of energy, which constitutes acoustic sources for acoustic emission. This paper investigates the acoustic emission signature arising from fatigue test of a metallic specimen under tensile fatigue test. In this experimental study, dog-bone aluminium alloy specimen with a surface defect was fatigued to failure. It is found that the acoustic emission characteristics are different during the propagation of surface crack, because the source is changing. The results provide a useful guide in identifying source origin based on the characteristics of the acoustic emission waveform.

Estimation of the mechanical properties for bone – titanium biocomposite based on computed tomography data and finite element modeling

The goal of this work is to study the effect of bone ingrowth into open pores of the implant and estimate of the mechanical characteristics for obtained biocomposite. Reconstruction of the isotropic model based on data acquired from computed tomography allows us to study the metallic and bone components integration under compressive load. Results are compared to performed mechanical tests of the porous specimen. The finite element modeling allows obtaining a stress-stain curve for the bone – titanium biocomposite. Young’s modulus of the metallic specimen is increased by 29 % after pores is filled with bone tissues. The conditional yield strength of the bone – titanium biocomposite is 2 times higher than that of porous open-pore titanium.

Self-Similar Mode of Metals Fragmentation under Severe Plastic Deformation

In the framework of nonequilibrium evolution thermodynamics, the influence of additive fluctuations on the kinetics of structural defects under severe plastic deformation has been studied. The applied method is a new one for the description of fragmentation modes and corresponding self-organization processes. It is found that a fragmented metallic specimen demonstrates a self-similar behavior, which results in the formation of a grain structure with various grain sizes. Such a behavior takes place provided that the probability distribution for the grain boundary density has a power-law dependence. A comparison of the results obtained in the Itˆo and Stratonovich forms demonstrates the absence of qualitative changes in the behavior of the system.

Numerical Verification of the Schroeder–Webster Surface Types and Friction Compensation Models for a Metallic Specimen in Axisymmetric Compression Test

Three types of surfaces in the Schroeder–Webster (SW) theory, i.e., sliding, mixed, and sticking surfaces, have been verified via finite element analysis of an axisymmetric compression test for a metallic specimen. Judging from (i) the radial profile of the pressure at the top elements and (ii) the radial displacement at the top nodes, the three types of SW surfaces are not manifested in the numerical simulation. However, the SW friction compensation model developed for the SW-sliding surface is remarkably reliable in predicting the measured stress–strain curve of the barreled specimen down to the height-to-diameter ratio of 0.1. The origin of this reliability is discussed along with recommendations for using the SW friction compensation model for the SW-sliding surface.

Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks

The research of fractographic images of metals is an important method that allows obtaining valuable information about the physical and mechanical properties of a metallic specimen, determining the causes of its fracture, and developing models for optimizing its properties. One of the main lines of research in this case is studying the characteristics of the dimples of viscous detachment, which are formed on the metal surface in the process of its fracture. This paper proposes a method for detecting dimples of viscous detachment on a fractographic image, which is based on using a convolutional neural network. Compared to classical image processing algorithms, the use of the neural network significantly reduces the number of parameters to be adjusted manually. In addition, when being trained, the neural network can reveal a lot more characteristic features that affect the quality of recognition in a positive way. This makes the method more versatile and accurate. We investigated 17 models of convolutional neural networks with different structures and selected the optimal variant in terms of accuracy and speed. The proposed neural network classifies image pixels into two categories: “dimple” and “edge”. A transition from a probabilistic result at the output of the neural network to an unambiguously clear classification is proposed. The results obtained using the neural network were compared to the results obtained using a previously developed algorithm based on a set of filters. It has been found that the results are very similar (more than 90% similarity), but the neural network reveals the necessary features more accurately than the previous method.

Sequential Monte Carlo: Enabling Real-time and High-fidelity Prognostics

Uncertainty quantification and propagation form the foundation of a prognostics and health management (PHM) system. Particle filters have proven to be a valuable tool for this reason but are generally restricted to state-space damage models and lack a natural approach for quantifying model parameter uncertainty. Both of these issues tend to inhibit the real-world application of PHM. While Markov chain Monte Carlo (MCMC) sampling methods avoid some of these restrictions, they are also inherently serial, and, thus, MCMC can become intractable as model fidelity increases. Over the past two decades, sequential Monte Carlo (SMC) methods, of which the particle filter is a special case, have been adapted to sample from a single, static posterior distribution, eliminating the state-space requirement and providing an alternative to MCMC. Additionally, SMC samplers of this type can be run in parallel, resulting in drastic reductions in computation time. In this work, a potential path toward real-time, highfidelity prognostics using a combination of surrogate modeling and a parallel SMC sampler is explored. The use of SMC samplers to enable tractable parameter estimation for full-fidelity (i.e., non-surrogate-assisted) damage models is also discussed. Both of these topics are studied in the context of fatigue crack growth in a geometrically complex, metallic specimen subjected to variable amplitude loading.

Interaction of a Sliding Wedge and a Metallic Specimen With a Near-Surface Inhomogeneity

The problem of a hard wedge sliding against a metal substrate has been studied extensively for its importance in tribo-plasticity and deformation processing. Here we explore the effect of introducing a single, near-surface plastic inhomogeneity (termed as a pseudograin) in a metal substrate using Lagrangian finite element (FE) analysis. The pseudograin is allowed to be softer or harder than the surrounding material. The effects of sliding parameters like the size and location of the pseudograin, friction and indenter geometry are also studied. Interestingly, the introduction of the pseudograin can lead to production of surface folds / self-contacts, and acutely-inclined, near-surface, crack-like features, which cannot be reproduced by homogeneous specimens. In fact, this tribosystem is phenomenologically very rich, despite differing from classical triboplastic systems of Challen, Oxley and Torrance only by way of the inhomogeneity. Despite its simplicity, the model replicates several experimentally observed features of surface folding, and is a minimal model to obtain folding in sliding. The occurrence of surface folds and concomitant residual surface damage points to the important role played by microstructure-related inhomogeneities in determining surface quality in deformation processing operations (e.g. repeated sliding to generate UFG surfaces) and is also a potentially new mode of sliding wear.