Statistical Size Distribution Research Articles

Towards a Fracture Mechanics-Based Assessment for Fatigue Life Prediction of Ni–Ti Stents

The fatigue failure of Ni–Ti peripheral stents still represents an open issue of major concern due to the non-linear material behavior, the complex loads acting in vivo, and the manufacturing process. The fatigue assessment currently exploits total-life methodologies devoted to preventing crack nucleation. This work investigates a complementary fracture mechanics-based approach accounting for crack propagation from pre-existing manufacturing defects. Fatigue crack growth tests were performed on rolled Ni–Ti samples with a thickness and microstructure comparable to that of stents. A fracture mechanics-based assessment was implemented to predict the fatigue durability of surrogate samples tested at different mean and alternate strains. The fracture surfaces of the samples were inspected to determine a statistical distribution of defect size at the fracture origin. The cyclic J-integral was adopted as the crack driving force parameter, and it allowed to account for the complex response of the material, undergoing energy dissipations during phase transformation. Encouraging fatigue life predictions conforming to experimental data were obtained in the finite-life regime, whereas conservative estimates were computed below the fatigue threshold. This approach can be reverted to determine the maximum acceptable material defects for specific applications, providing a useful tool to manufacturing companies.

Automated screening of precipitation polymerizations and evaluation using image recognition for divinylbenzene and methacrylic acid

AbstractBy applying automated high‐throughput experimentation, 63 precipitation polymerizations of divinylbenzene and methacrylic acid were performed with a total of 1638 samples analyzed by gas chromatography (GC), nuclear magnetic resonance (NMR) spectroscopy, and scanning electron microscopy (SEM). The conversion of each reaction was investigated revealing the best substrate concentrations within the current setup. The GC evaluation was performed automatically via a new custom‐made Python script significantly reducing the time to evaluate the results. Furthermore, the particle growth was monitored by utilizing an innovative image recognition tool to identify particles and their respective sizes using SEM images. Furthermore, a statistical particle size distribution analysis was performed, which is hardly achievable in reasonable time by classical evaluation methods. Using this new procedure, the highest conversion (70%) as well as the largest particles (3700 nm) have been obtained utilizing a high initial monomer (5 vol%) and initiator (5 mol%) concentration. Accordingly, the smallest particles (245 nm) yielded from the lowest starting concentration (1 vol% monomer and 1 mol% initiator).

Optimization of Dropwise Condensation of Steam over Hybrid Hydrophobic–Hydrophilic Surfaces via Enhanced Statistically Based Heat Transfer Modelization

Steam condensation over a hybrid hydrophobic–hydrophilic surface is modeled via simplified heat transfer modelization. Filmwise condensation is assumed over the hydrophilic region. The standard film model is improved, accounting for the liquid flow rate crossing the hydrophobic–hydrophilic boundaries. A threshold for flooding occurrence is also presented. Dropwise condensation is assumed over the hydrophobic region. Compared to the heat transfer models in the literature, based on the statistical drop size distribution, a novel correlation is used for the size distribution of small droplets. The correlations of both the liquid flow rate crossing the hydrophobic–hydrophilic boundary and the size distribution of small drops are derived via Lagrangian simulations, using an in-house code previously developed and validated by the authors. The heat transfer model is validated with experimental data in the literature involving a hybrid surface, composed by alternate vertical hydrophobic–hydrophilic stripes. Then, the optimization of the hybrid surface geometry is performed in terms of hydrophobic width and hydrophilic width, with the aim of enhancing the heat flux.

Absorbing capabilities of additively manufactured lattice structure specimens for crash applications: Damage tolerant design and simulations

AbstractIn the present work, the influence of defects on the compressive response of octet‐truss AlSi10Mg lattice structure specimens produced with a selective laser melting process is investigated. The defect population in one cell, in two cells, and cubic specimens composed of 27 cells has been assessed with micro‐computed tomography (micro‐CT) analyses. The statistical distributions of the characteristic defect sizes, i.e., the equivalent diameter, the volume, and the surface, assessed in the lattice structure specimens and in volumes randomly extracted from a rectangular bar have been compared. Finally, the compressive behavior of lattice structure specimens has been simulated with a simplified damage‐tolerant finite element model accounting for the influence of defects and compared with experimental results. The analyses have proven that the defect population in volumes extracted from a rectangular bar can provide reliable simulated results, even if micro‐CT inspections of a unit cell or specimens made of several cells are suggested.

Combustion of iron particles in solid propellants at elevated pressure

Metal fuels, such as aluminum (Al) and iron (Fe), can be added to composite solid propellants to improve their performance, such as specific impulse, density, and burning rate. In comparison to aluminum, iron can theoretically provide improved density specific impulse and higher flame temperatures; reduce condensed combustion product (CCP) concentration and the associated two-phase flow losses; and eliminate hydrochloric acid (HCl) in the exhaust products. A fundamental and quantitative understanding of metal particle aggregation and agglomeration processes in solid propellants is required to understand the underlying combustion mechanisms in these systems. In the current study, composite strand and laminate AP/HTPB/AP propellant samples loaded with Fe microparticles (∼45 μm in diameter) were burned at elevated pressures in an optically accessible strand bomb. Combustion processes were monitored with transient pressure diagnostics and a high-speed camera fitted with a high-magnification lens system (3.83 μm/pixel resolution) for the laminate propellant experiments. An automated image processing algorithm was developed to measure burning rates and ejected particle/agglomerate sizes and velocities. Time-resolved statistical distributions of both particle size and velocity are presented at elevated pressure for multiple laminate propellant experiments with a high degree of repeatability and low measurement error estimated as < ±5% and < ±1.5% for particle size and velocity, respectively. The incorporation of iron microparticles into the composite strand propellants yielded over a 20% increase in the global burning rate over the range of pressures evaluated (3.45–13.8 MPa). Similarly, the addition of iron to the fuel lamina in laminate propellant samples led to an approximately 30% increase in the global burning rate at the evaluated pressure (3.45 MPa). Additive particles were observed to eject near the oxidizer/fuel interface, or to melt, aggregate, coalesce, and agglomerate on the fuel lamina surface prior to ejection. Particle velocities are controlled by a balance of gravitational forces, drag forces imparted by expanding combustion product gases, and particle inertia. The observed combustion enhancements are attributed to the combined effects of catalytic mechanisms, increased radiation heat transfer, and local energy release from reacting iron particles. In addition, discussions on the image processing methods developed in the current study, corresponding potential sources of error, and prospective areas of improvement are provided. The experimental approach developed enables high-speed and high-magnification visualization of propellant combustion at high pressures and can be utilized to better understand the fundamental combustion behavior of energetic systems.

Numerical simulation of droplet dispersion within meso-porous membranes

Analysis of membrane processes in fluid processing, and their main influencing operating conditions are relevant in a variety of industrial applications. Increasing regulatory scrutiny and environmental considerations are forcing industries across all sectors, from food and pharma to oil and gas, to further understand and optimise the handling and formulation of liquid systems for efficient process design. In a generic setup for emulsification and liquid formulation the flow and dispersion behaviour of a liquid oil droplet on its way through a porous water filled membrane is analysed. A set of high-resolution numerical simulations of a single oil droplet dispersed in water through a porous membrane structure with varying contact angles is performed. In this work cluster analysis of volume-of-fluid simulation results to obtain statistical droplet size distributions is conducted and further analysed to highlight the effect of the contact angle as well as pressure drop on the dynamics of the system. It is observed that based on the membrane surface activity the droplet behaviour changes from filtration with coalescence when the membrane is lipophilic to emulsification with droplet break-up when the membrane is lipophobic. Furthermore, the pressure drop is identified as a key factor for the dynamics of the droplet process and the frame in which it occurs. These results highlight that the membrane wettability is a determining factor for the emulsification or filtration effectiveness of a membrane for various applications.

Fully spherical 3D datasets on sedimentary particles: Fast measurement and evaluation

AbstractRecently it became increasingly evident that the statistical distributions of size and shape descriptors of sedimentary particles reveal crucial information on their evolution and may even carry the fingerprints of their provenance as fragments. However, to unlock this trove of information, measurement of traditional geophysical shape descriptors (mostly detectable on 2D projections) is not sufficient; fully spherical 3D imaging and mathematical algorithms suitable to extract new types of inherently 3D shape descriptors are necessary. Available 3D imaging technologies force users to choose either speed or full sphericity. Only partial morphological information can be extracted in the absence of the latter (e.g., LIDAR imaging). In the case of fully spherical imaging, speed was proved to be prohibitive for obtaining meaningful statistical samples, and inherently 3D shape descriptors were not extracted. Here we present a new method by complementing a commercial, portable 3D scanner with simple hardware to quickly obtain fully spherical 3D datasets from large collections of sedimentary particles. We also present software for the automated extraction of 3D shapes and automated measurement of inherently 3D-shape properties. This technique allows for examining large samples without the need for transportation or storage of the samples, and it may also facilitate the collaboration of geographically distant research groups. We validated our software on a large sample of pebbles by comparing previously hand-measured parameters with the results of automated shape analysis. We also tested our hardware and software tools on a large pebble sample in Kawakawa Bay, New Zealand.

Effect of the Density Ratio on Emulsions and Their Segregation: A Direct Numerical Simulation Study

Using direct numerical simulation (DNS) in combination with the volume of fluid method (VoF), we investigate the influence of the density ratio between the carrier and dispersed phase on emulsions, where the baseline simulation approximately corresponds to the ratio of water-in-gasoline emulsions. For this purpose, homogeneous isotropic turbulence (HIT) is generated using a linear forcing method, enhanced by a proportional–integral–derivative (PID) controller, ensuring a constant turbulent kinetic energy (TKE) for two-phase flows, where the TKE balance equation contains an additional term due to surface tension. Then, the forcing is stopped, and gravitational acceleration is activated. The proposed computational setup represents a unique and well-controlled configuration to study emulsification and segregation. We consider four different density ratios, which are applied in industrial processes, to investigate the influence of the density ratio on the statistically steady state of the emulsions, and their segregation under decaying turbulence and constant gravitational acceleration. At the statistically steady state, we hold the turbulence constant and study the effects of the density ratio ρd/ρc, on the interface area, the Sauter mean diameter (SMD), and the statistical droplet size distribution. We find that all are affected by the density ratio, and we observe a relation between the SMD and ρd/ρc. Furthermore, we assume a dependence of the critical Weber number on the density ratio. In the second part of our work, we study the segregation process. To this end, we consider the change in the center of mass of the disperse phase and the energy release, to analyze the dependence of segregation on the density difference Δρ/ρd. We show that segregation scales with the density difference and the droplet size, and a segregation time scale has been suggested that collapses the height of the center of mass for different density ratios.

Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms

The design against fatigue failures of Additively Manufactured (AM) components is a fundamental research topic for industries and universities. The fatigue response of AM parts is driven by manufacturing defects, which contribute to the experimental scatter and are strongly dependent on the process parameters, making the design process rather complex. The most effective design procedure would involve the assessment of the defect population and the defect size distribution directly from the process parameters. However, the number of process parameters is wide and the assessment of a direct relationship between them and the defect population would require an unfeasible number of expensive experimental tests. These multivariate problems can be effectively managed by Machine Learning (ML) algorithms. In this paper, two ML algorithms for assessing the most critical defect in parts produced by means of the Selective Laser Melting (SLM) process are developed. The probability of a defect with a specific size and the location and scale parameters of the statistical distribution of the defect size, assumed to follow a Largest Extreme Value Distribution, are estimated directly from the SLM process parameters. Both approaches have been validated using literature data obtained by testing the AlSi10Mg and the Ti6Al4V alloy, proving their effectiveness and predicting capability.

On the impact of differential diffusion between soot and gas phase species in turbulent flames

The molecular diffusivities of larger PAHs and soot particles approach zero leading to differential diffusion with gas-phase species. The present work systematically quantifies the impact on soot moments, soot related statistical correlations and Particle Size Distributions (PSDs) using a fully coupled transported joint probability density function (JPDF) method featuring a 78-dimensional joint-scalar space, including enthalpy, gas phase species with the PSD discretised using 62 size classes via a mass and number density preserving sectional method. Differential diffusion of soot (DDS) is treated via a gradual decline of diffusivity among soot sections maintaining realisability and the expected exponential decay of variance. The solution of the flow field features a time-dependent second moment closure and an elliptic solver. The turbulent non-premixed Sandia C2H4 flame from the International Sooting Flame (ISF) data base was selected as a target along with the KAUST (C2H4/N2) variant of the same flame. Results show that reduced soot diffusion leads to a significant increase in the soot volume fraction RMS and that the correlation coefficient between soot volume fraction and temperature is further reduced in a particle-size-dependent manner. Similar observations are made for correlations between the soot volume fraction and the mass fractions of gas-phase species such as CO, OH, H and C2H2. The results suggest that computational methods that presume explicit (e.g. flame structure related) correlations between such scalars and with soot face leading order modeling challenges. It is also shown that the correlation between CO and soot increases due to oxidation of soot and that DDS leads to a modest downstream shift of PSDs towards larger particles.

Breakdown of Reye’s theory in nanoscale wear

Building on an analogy to ductile fracture mechanics, we investigate the energetic cost of debris particle creation during adhesive wear . Macroscopically, Reye proposed in 1860 that there is a linear relation between frictional work and wear volume at the macroscopic scale. Earlier work suggested a linear relation between tangential work and wear debris volume also exists at the scale of a single asperity, assuming that the debris size is proportional to the micro contact size multiplied by the junction shear strength. However, the present study reveals deviations from linearity at the microscopic scale. These deviations can be rationalized with fracture mechanics and imply that less work is necessary to generate debris than what was assumed. Here, we postulate that the work needed to detach a wear particle is made of the surface energy expended to create new fracture surfaces, and also of plastic work within a fracture process zone of a given width around the cracks. Our theoretical model, validated by molecular dynamics simulations, reveals a super-linear scaling relation between debris volume (Vd) and tangential work (Wt): Vd∼Wt3/2 in 3D and Vd∼Wt2 in 2D. This study provides a theoretical foundation to estimate the statistical distribution of sizes of fine particles emitted due to adhesive wear processes.

Особливості розвитку ринку банківських послуг України: ефект розміру банку та закон Гібрата

Quantitative description of the development of the banking services market is important for making optimal decisions in the regulation of the banking sector. The importance of the question lies in the fact that such a description makes it possible to take into account the transformation of banks, sources of risks, to identify the main trends or regularities in the process of evolution. The aim of this article is to test Gibrat's law as a hypothesis for the development of the banking services market of Ukraine, by an empirical experiment where all existing banks are tracked over time on an annual basis and the distribution of banks by the size of assets or income is constructed annually. The work assumes the possibility of establishing a statistical size (assets or income) distribution of banks with certain average values and variances according to the lognormal distribution of banks. The research methodology is based on statistical analysis and verification of the correspondence of the empirical size distribution of banks to the logarithmically normal one provided by Gibrat's law. The study period covers the banking sector from 2014 to 2023 and includes all operating banks. The information base for the analysis was the annual official reporting data (report on the financial state) of banks, published by the National Bank of Ukraine and on the banks' official websites. The data set defines the assets and income of all banks and each bank separately, annually. It has been proven that Gibrat's law is not fulfilled for banks of Ukraine, the size distributions do not obey the Gaussian distribution law. The rate of growth of banks is not random (as it is assumed by Gibrat's hypothesis). The trend of gradual approximation of income distributions to the lognormal type and the potential possibility of implementation of Gibrat's law for the period of 2022–2023 has been revealed.

A reference methodology for microplastic particle size distribution analysis: Sampling, filtration, and detection by optical microscopy and image processing

AbstractMicroplastic (MP) contamination in natural water circulation is a concern for environmental issues and human health. Various types of polymer materials have been identified and were detected in MP analytic test procedures. Beyond MP polymer type, particle size and form play a major role in water analysis due to possible negative toxicologic effects on flora and fauna. However, the correct quantitative measurement of MP size distribution over several orders of magnitude is strongly influenced by sample preparation, filtration materials and processes, and microanalytical techniques, as well as data acquisition and analysis. In this paper, a reference methodology is presented aiming at an improved quantitative analysis of MP particles. An MP analysis workflow is demonstrated including all steps from reference materials to sample preparation, filtration handling, and MP particle size distribution analysis. Background‐corrected particle size distributions (1–1000 µm) have been determined for defined polyethylene (PE) and polyethylene terephthalate (PET) reference samples. Microscopically measured particle numbers and errors have been cross‐checked with the total initial mass. In particular, defined reference MP samples (PE, PET) are initially characterized and applied to filtration experiments. Optical microscopy imaging on full‐area Si filters with subsequent image analysis algorithms is used for statistical particle size distribution analysis. To quantify the effects of handling and filtration, several blind tests with distilled water are carried out to determine the particle background for data evaluation. Particle size distributions of PE and PET reference samples are qualitatively and quantitatively reproduced with respect to symmetry, and maximum and cut‐off diameter of the distribution. It is shown that especially MP particles with a radius of &gt;50 µm can be detected and retrieved with high reliability. For particle sizes &lt;50 µm, a significant interference with background contamination is observed. Data from blank samples allows a correction of background contaminations. Furthermore, for enhanced sampling statistics, the recovery of the initial amount of MP will be qualitatively shown. The results are intended as an initial benchmark for MP analytics quality. This quality is based on statistical MP particle distributions and covers the complete analytic workflow starting from sample preparation to filtration and detection. Microscopic particle analysis provides an important supplement for the evaluation of established spectroscopic methods such as Fourier‐transform infrared spectroscopy or Raman spectroscopy.

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

Slope units (SUs) are sub-watersheds bounded by ridge and valley lines. A slope unit reflects the physical relationship between landslides and geomorphological features and is especially useful for landslide sensitivity modeling. There have been significant algorithmic advances in the automatic delineation of SUs. But the intrinsic difficulties of determining input parameters and correcting for unreasonable SUs have hindered their wide application. An improved method of the evaluation and local multi-scale optimization for the automatic extraction of SUs is proposed. The Sus’ groups more consistent with the topographic features were achieved through a stepwise approach from a global optimum to a local refining. First, the preliminary subdivisions of multiple SUs were obtained based on the r.slopeunit software. The optimal subdivision scale was obtained by a collaborative evaluation approach capable of simultaneously measuring objective minimum discrepancies and seeking a global optimum. Second, under the selected optimal scale, unreasonable SUs such as over-subdivided slope units (OSSUs) and under-subdivided slope units (USSUs) were further distinguished. The local average similarity (LS) metric for each SU was designed based on calculating the SU’s area, common boundary and neighborhood variability. The inflection points of the cumulative frequency curve of LS were calculated as the distinguishing intervals for those unrealistic SUs by maximum interclass variance threshold. Third, a new effective optimization mechanism containing the re-subdivision of USSUs and merging of OSSUs was put into effect. We thus obtained SUs composed of terrain subdivisions with multiple scales, which is currently one of the few available methods for non-single scales. The statistical distributions of density, size and shapes demonstrate the excellent performance of the refined SUs in capturing the variability of complex terrains. Benefiting from the sufficient integrating approach of diverse features for each object, it is a significant advantage that the processing object can be transferred from general entirety to each precise individual.

WCu composites fabrication and experimental study of the shielding efficiency against ionizing radiation

A comprehensive study of the present work aimed to investigate the radiation shielding features for W–Cu composites materials with high density. Isostatic hot pressing technique was applied to obtain W85 wt%Cu15 wt% and W75 wt%Cu25 wt% composites with various thickness (0.6, 0.9, 1.2, 1.5 and 2.7 cm). Composites were the pollycristalline samples with well packed microstructure. The statistical grain size distribution performed that the size of an agrolemerated copper grains increases with the Cu content rising. X-ray diffraction (XRD) analysis showed that the main spectrum lines of the composites are corresponded to the bcc phase of tungsten and fcc phase of copper. The linear attenuation coefficient (LAC) was detected experimentally using a NaI detector at various incoming photon energy. The highest values of LAC are found 2.11 and 4.33 cm−1 for W75 wt%Cu25 wt% and W85 wt%Cu15 wt%, respectively, at low energy 0.266 MeV while the lowest values (0.44 and 0.48 cm−1 for W75 wt%Cu25 wt% and W85 wt%Cu15 wt%, respectively) are observed at high energy 1.25 MeV. From the obtained results, it can deduce the discussed W–Cu composites are suitable to be used in several applications of radiation protection.

The effective mechanical properties of solids with distributed rough cracks

A statistical multi-step self-consistent (SMSSC) micromechanical model for predicting elastic and plastic properties of a three-dimensional Representative Volume Element (RVE) is proposed in this research. A body with randomly distributed rough penny-shaped microcracks is considered for the first time in which the distribution of cracks is included through different well-known statistical distributions. Initially, the solution for a rough cohesive penny-shaped crack is developed, and the relationship between crack nominal length and roughness is derived. Consequently, Crack Opening Displacement (COD) and the corresponding volume crack opening are calculated as a function of surface roughness. Next, the SMSSC is used to study the effects of nominal crack length distribution on a fractured medium’s mechanical properties with a known statistical distribution of crack sizes. This is done by determining the energy release and evaluating its share in material degradation (elastic, plastic). The elastic properties calculated using SMSSC indicated that the fractality of crack trajectories leads to lower values of volume crack opening and, consequently, resulted in a lower level of stiffness degradation compared with previous smooth crack approximations. It is also reached that the statistical distribution of rough cracks influences the mechanical properties due to the interrelation between length and roughness. Regarding the plastic behavior, the proposed micromechanical methodology is implemented to construct a capped pressure-sensitive yielding potential function that explicitly highlights the influence of crack size statistical distribution on the yielding of cracked solids. It is evident that the statistical distribution of crack sizes which are interrelated with crack roughness, can significantly alter the corresponding yield surface of fractured media.

Statistical capillary tube model for porous filter media: An application in modeling of gasoline particulate filter

A new statistical capillary tube model is presented to predict both filtration efficiency and permeability of particulate filters. The model considers the porous structure of the filter media as an assembly of curved capillary tubes with a statistical size distribution. The key model descriptors of pore microstructure are porosity, pore size, pore size distribution, and tortuosity. The model was validated by permeability and size dependent filtration efficiency measurements of bare and washcoated gasoline particulate filters (GPFs), using porosimetry data of the filter media as model inputs. Comparing with a more widely used spherical model for particulate filters with typical porosity range of 30–60%, the capillary tube model has similar input parameters but provides additional permeability prediction as well as better filtration predictions over a wider porosity range including the lower porosity of washcoated filters. Furthermore, it offers a more visually straightforward and intuitive correlation between filtration paths and filter performance. This allows convenient adoption of the capillary tube model in numerous applications that are currently based on spherical model assumption and provides improved guidance for filter optimization. Beyond ceramic automotive exhaust filters, this geometric model offers a new framework for examining porous filter media in general.

Multiscale modeling of ferroelectrics with stochastic grain size distribution

Macroscopic properties of ferroelectrics are controlled by processes on the microscale, in particular the switching of crystal unit cells and the movement of domain walls, respectively. Besides these microscopic levels, the grains of a polycrystalline material constitute the mesoscopic scale. Interactions of grains with statistically distributed orientations, as a consequence of mechanical and electrostatic mismatch, give rise to for example, residual stress which in turn affects domain switching. A multiscale modeling thus has to incorporate at least three interacting scales. In this context, the condensed method has recently been elaborated as an efficient tool with low computational cost and effort of implementation. It is extended toward statistical distributions of grain sizes in a representative material volume element and amended with regard to the modeling of domain evolution. Each of the few parameters of the constitutive approach has a unique physical meaning and is adapted to available experimental values of macroscopic quantities of barium titanate taken from various sources.

Dynamical assessment of altitudinal trend of drop size distributions in a tropical region of Nigeria

A comparative assessment of different statistical drop size distributions in a tropical location in Nigeria is presented in this paper. The wet season months of the year 2014 rain data, measured using vertically-pointing Micro Rain Radar (MRR) installed at the Department of Physics, The Federal University of Technology Akure, Nigeria was considered. The data were archived at the Communication Research Group, Department of Physics, The Federal University of Technology Akure. The drop sizes were estimated from the fall velocity. The stratiform rainfall type was considered in this study due to its preponderance of occurrence. Three heights (160 m, 1440 m, and 2240 m) were reviewed for this study. These heights were given due consideration because of the density of the data. All the statistical distributions considered which include Lognormal, Gamma, and Weibull distributions performed better at the 160 m height compared to other heights. It was however observed that the Gamma and Weibull distribution performed better than the Lognormal distribution at this height. The results obtained in this study will be fundamental for the application of remote sensing and the propagation field in this area. The results will be useful to radio communication engineers to properly design a good fade margin in a radio communication system in this region.

Metastable high entropy alloys: An excellent defect tolerant material for additive manufacturing

Fatigue failure is ubiquitous in structural components. In additively manufactured (AM) components, the processing induced defects limit the fatigue performance. Further, the stochastic nature of defects in laser-powder bed fusion (L-PBF) make it difficult to predict the fatigue life in these components. In this work, we explored exceptional work hardening (WH) of a metastable Fe40Mn20Co20Cr15Si5 high entropy alloy (CS-HEA) to obtain high fatigue-resistance with L-PBF. Further, a fatigue life estimation tool based on the statistical size distribution of microstructural entities such as grains, pores and solid-state inclusions and, their mutual interaction was used to estimate the fatigue life of as-printed material. Upon deformation, CS-HEA exhibited γ (f.c.c.) → ε (h.c.p.) martensitic transformation and subsequent twinning in ε (h.c.p.) phase. Such deformation behavior resulted in sustained WH and is specifically beneficial in the vicinity of critical pores. A high normalized fatigue strength of 0.65 with respect to the yield strength was thus obtained in as-printed condition. Further, the model accurately predicted extended crack initiation life for CS-HEA. The current work therefore provides guidance towards developing defect-tolerant alloys for L-PBF and presents a tool for estimation of fatigue life of AM alloys with unconventional WH behavior.