Etching methods for revealing nanoscale precipitates and carbides in Ni-based superalloys.

Microstructural degradation and its corresponding mechanical property of wrought superalloy GH4037 caused by very high temperature

Stress corrosion crack (SCC) initiation testing has been performed on a 15% cold-worked UNS N06600 (Alloy 600) heat in mill-annealed (MA), solution annealed (SA), and thermally treated (TT) conditions to assess the role of grain boundary (GB) carbides on stress-assisted intergranular attack (IGA) and short crack nucleation and growth. The SCC initiation tests were conducted at a constant load equivalent to the materials’ yield stress in 360oC simulated pressurized water reactor primary water. Results revealed the highest SCC initiation susceptibility occurred in the Alloy 600 MA material, followed by the TT and SA materials, suggesting that GB carbide distribution did not have a controlling effect on SCC initiation resistance. Quantitative assessments of IGA and short cracks were conducted to help understand this phenomenon, and the role of GB carbides in precursor damage development that leads to differences in macroscopic SCC initiation behavior are discussed.

The modified sessile drop method (MSDM) and the modified pycnometric method (MPM) have been employed to measure precisely the densities of liquid nickel-based ternary (Ni-Co-Al) and quaternary (Ni-Co-Al-Cr, Ni-Co-Al-Mo, Ni-Cr-Al-Mo) alloys and four commercial superalloys in liquid and mushy states. There was a good agreement between the values measured by the two methods. The measured densities of liquid model alloys and commercial superalloys in liquid and mushy states decreased linearly with increasing temperature for the experimental temperature range. The temperature coefficient of the density of liquid ternary Ni-Co-Al alloys measured in this work can be represented as a quadratic function of the aluminium concentration in the alloys. The density of liquid Ni-Co-Al alloys can be expressed as a function of both temperature and aluminium concentration in the alloys. The recommended equations for the densities of liquid nickel-based ternary and quaternary alloys and four commercial superalloys in liquid and mushy states were obtained by analysing density values measured by both MSDM and MPM. The calculated values from the recommended density equations show good agreement with those measured by both MSDM and MPM. The density (� ) and its temperature dependency (d�= dT) are important input data for simulation of the solidification of Ni-based superalloys, e.g. the prediction of defects such as gas porosity and microsegregation, as well as the evaluation of the convective contribution to heat transport. Accurate experimental values of the densities of nickel and Ni-based model and commercial alloys (including their dependencies on composition and temperature) are needed to develop models to predict the densities of Ni-based superalloys. In order to predict the densities of Ni-based commercial alloys, it is necessary to have an understanding of the effects of the various alloy elements (such as Cr, Co, Ta, W, Al, Mo, Re, Hf, Nb, Ti, etc.) on the densities of liquid nickel alloys. This can be obtained from accurate measurements of the densities of Ni-based binary alloys in relation to the concentration and temperature. Comparisons of the densities of liquid Ni-based ternary alloys with those for the corresponding binary alloys provide useful information on the effects of the second alloy element on the properties of liquid alloys. For some alloy elements, such as Re, their concentrations in the Ni-based superalloys are relatively low but they are very reactive, which may make the accurate density measurement of their Ni-based binary alloys difficult. The effect of these elements on the densities of Ni-based superalloys can be obtained by comparing the densities of liquid Ni-based commercial alloys with those of liquid Ni- based multi-component model alloys with similar chemical compositions to those of the Ni-based commercial alloys. Furthermore, accurate density values of both Ni-based model and commercial alloys can be used to verify the validity of the models developed to predict the densities of liquid Ni- based superalloys. However, there is a large scatter in the reported density values for liquid nickel and its temperature dependence because of the experimental uncertainties associated with the different techniques. Furthermore, there are very few reports on the measurement of the density of Ni-based alloys in relation to the effects of composition and temperature. 1-7) There are no reports on the measurement of the densities of liquid Ni-based multi-component model alloys such as ternary and quaternary alloys. Therefore, in order to develop a model to predict the densities of commercial Ni-based superalloys, we have used two accurate methods, namely, the modified sessile drop method (MSDM) and the modified pycnometric method (MPM) to measure the densities of liquid nickel and Ni-based model alloys and commercial alloys in the liquid state and in the mushy (ie ''liquid-solid coexistence'') state for some alloys. The principles, advantages and experimental proce- dures of the two methods have been described previously. 5-7) The densities of liquid nickel and liquid Ni-Cr, Ni-Co, Ni-Ta, Ni-W and Ni-Al alloys have been measured precisely with both methods, and density values for Ni-Cr and Ni-Al alloys in mushy state have also been determined. 5-7) In this paper, both MSDM and MPM were employed to measure the densities of liquid ternary model alloys (Ni-Co- Al, molar fraction xNi:xCo � 86:14, Al mass% = 0� 11), liquid quaternary model alloys (Ni-Co-Al-Cr, Ni-Co-Al-Mo and Ni-Cr-Al-Mo), and four commercial Ni-based super- alloys in liquid and mushy states. The chemical compositions of these model alloys were similar to those of commercial Ni- based superalloys. The recommended density values for these alloys have been obtained by combining the measured values determined with the two methods.

The Ni-based Udemit720Li superalloy tends to form large γ/γ' eutectic on grain boundaries (GBs) during solidification due to the addition of excessive Al and Ti elements, which provides convenience to study the effect of carbide and γ/γ' eutectic on crack initiation and propagation during tensile process. In this paper, Udemit720Li superalloy samples were prepared by induction melting casting method, arc melting and suction casting method. The microstructure, tensile properties and mechanism of crack initiation and propagation in Ni-based superalloy fabricated by two methods are investigated. The results exhibit γ/γ' eutectic accelerates the stress concentration at GB and thus leads to premature fracture failure. The samples with grain-boundary eutectic have higher strain hardening rate, but their cumulative and local misorientations are lower. For samples without eutectic at GB, the primary crack initiates at grain-boundary carbide along GB and extends along GB or into grain matrix, and exhibits better deformation performance and dislocation storage capacity within grains.

The influence of Re on the grain boundary (GB) strength and 982 ℃/200 MPa creep properties of fine-grain Mar-M247superalloy was investigated. Quantitative statistical analysis showed that an increase of Re content in Mar-M247 resulted in a decrease of the size, and an increase in the number of GB carbides. The GB strength increased with increasing the number of fine GB carbides. The results of 982 ℃/200 MPa creep tests showed that creep life increase with an increase in Re content up to a maximum at 3 wt.%. The addition of 1-3 wt.% Re reduced steady-state creep rates and postponed the onset of the acceleration stage in three ways: (1) by increasing the amount of primary cuboidal γ′ phase; (2) by increasing the strength of γ matrix; and (3) by increasing the development of γ′ raft. It also prolonged the duration of the accelerating creep stage by refinement and an increase in the number of GB carbides. The above mentioned factors resulted in a prolongation of creep life. However, addition of greater amounts of Re, such as 5 wt.% Re, causes deterioration of the creep properties due to the formation of P phases.

Grain boundary engineering (GBE) describes the various approaches to control the fraction and composition of special boundaries in designing engineering materials. For example, complex thermo-mechanical processing routes are harnessed to increase the fraction of Σ3 twin boundaries, bringing about improvements to strength, toughness, and corrosion properties in austenitic stainless steels. However, high-temperature gas turbine engine components designed for the most demanding applications must be manufactured from Ni-based superalloys. To provide the required high-temperature strength, their microstructures are highly complex and consist of an austenitic γ-matrix, γ' precipitates, and grain boundary (GB) carbides, and/or borides. GBE approaches for Ni-based superalloys have been reported to reduce in-service GB cracking via targeted engineering of the GB microstructure. However, the same complex microstructures severely limit the processability of the most advanced Ni-based superalloys required to develop the next generation gas turbine engines.To tackle this challenge, we propose precipitation-controlled GBE for Ni-based superalloys with limited formability, via formation of GB serrations and precipitation upon slow cooling. We showcase our approach by achieving controlled GB precipitation of GB-γ' and M6C carbides in the cast & wrought Ni-based superalloy René 41 via a simple heat treatment. Ductility improvements are predicted via crystal plasticity modeling due to increased slip transmission compatibility. Instead of generating additional Σ3 twin boundaries only, these interfaces are directly incorporated into the GB network. It is shown that precipitation-controlled GBE is enabled by GB-γ' nucleating heterogeneously on M6C whereby competitive coarsening of GB-γ' provides the driving force. The fundamental mechanisms of our GBE approach are discussed and summarized in a qualitative microstructural model.

Study on the micro-structure and the nano-indentation responses of laser solid formed epitaxial Ni-based superalloy

The advent of small-scale testing procedures coupled with scanning electron microscopy (SEM) imaging allow for high-resolution digital image correlation (DIC) studies to examine strain localization at the grain size length scale. A systematic study was performed to determine how speckle patterning parameters (speckle density and shape) affect strain resolution of DIC using SEM imaging. Strain resolution increased with increased speckle density from 23 to 58 % area fraction. Patterns with less than 23 % area fraction exhibited significant signal noise, and a loss in strain resolution due to inadequate correlation. It was also observed that when the edges of square speckles were aligned with SEM rastering directions, the noise in the e yy data was double the noise in the e xx data. Rotating the speckle to eliminate edge alignment with the rastering direction significantly decreased the e yy strain noise. Competing optimization requirements for the correlation parameters were needed to minimize strain intensity noise or maximize spatial resolution. Application of the optimization techniques to high temperature in-situ studies of Ni-based superalloys will also be presented.

In order to perform nano-manipulation more accurately, it is necessary to study the relationship between the scanning electron microscope (SEM) image and the actual length. In this paper, based on the secondary electron image, the displacement of 1.81nm in SEM was measured the actual displacement was obtained according to the change of the pixels, so as to realize the nanometer displacement calibration and real-time measurement of the nano robot manipulator in SEM. According to the magnification, resolution and scale bar of SEM image, the mapping relationship between pixels and actual length was established. The obvious feature point and area in the SEM image were selected to be marked. The driving signal was applied to the manipulator of the nano robot, and the template matching algorithm was used to track the feature point and area in real time. The change of the feature point in the SEM image was obtained, and the actual displacement of the manipulator was calculated according to the mapping relationship between the pixel points and the actual displacement. We calibrated the displacement of the nanorobots and measured the actual displacement in real time, which laid a foundation for the precise control of the nanorobots in the future.

Image noise is an important factor that influences the accuracy of strain field measurements by means of digital image correlation and scanning electron microscope (SEM) imaging. We propose a new model to quantify the SEM image noise, which extends the classical photon noise model by taking into account the brightness setup in SEM imaging. Furthermore, we apply this model to investigate the impact of different SEM setting parameters on image noise, such as detector, dwell time, spot size, and pressure in the SEM chamber in the context of low vacuum imaging.

Accurate identification and classification of atmospheric particulates can provide the basis for their source apportionment. Most current research studies mainly focus on the classification of atmospheric particles based on the energy spectrum of particles, which has the problems of low accuracy and being time-consuming. It is necessary to study the classification method of atmospheric particles with higher accuracy. In this paper, a convolutional neural network (CNN) model with attention mechanism is proposed to identify and classify the scanning electron microscopy (SEM) images of atmospheric particles. First, this work established a database, Qingdao 2016–2018, for atmospheric particles classification research. This database consists of 3469 SEM images of single particulates. Secondly, by analyzing the morphological characteristics of single particle SEM images, it can be divided into four categories: fibrous particles, flocculent particles, spherical particles, and mineral particles. Thirdly, by introducing attention mechanism into convolutional neural network, an Attention-CNN model for the identification and classification of the four types of atmospheric particles based on the SEM images is established. Finally, the Attention-CNN model is trained and tested based on the SEM images database, and the results of identification and classification for four types of particles are obtained. Under the same SEM images database, the classification results from Attention-CNN are compared with those of CNN and SVM. It is found that Attention-CNN has higher classification accuracy and reduces significantly the misclassification number of particles, which shows the focusing effect of attention mechanism.

The MeX™ software is a useful tool for tridimensional data collection for surface evaluation and could be relevant to evaluate the same specimen in different phases of the study, assuming repeated measures of dental enamel roughness. The aim of this study was to evaluate the influence of sample metallization for dental enamel roughness analysis with 3D images reconstructed using MeX™ software from Scanning Electron Microscopy (SEM) images. The influence of 74.98% (%mol/mol) argon-oxygen plasma for carbon layer removal on surface roughness of the metallized specimen was also evaluated. Dental enamel specimens were prepared for SEM analysis with and without carbon metallization using conventional or environmental modes. Argon-oxygen plasma for carbon layer removal was used and surface roughness was re-evaluated. Roughness obtained by SEM and MeX™ reconstructed images, with or without metallization, did not differ. No significant alteration on surface roughness after carbon layer removal using plasma was found. SEM baseline evaluation using conventional mode without sample preparation and in environmental mode were not comparable. Roughness of enamel 3D images reconstructed with MeX™ software from SEM images, with or without metallization was similar. The 74.98% (%mol/mol) argon-oxygen plasma removed the carbon layer with no effect on enamel roughness.

A methodology for the automated analysis of metal powder scanning electron microscope (SEM) images towards material characterization is developed and presented. This software-based tool takes advantage of a combination of recent artificial intelligence advances (mask R-CNN), conventional image processing techniques, and SEM characterization domain knowledge to assess metal powder quality for additive manufacturing applications. SEM is being used for characterizing metal powder alloys, specifically by quantifying the diameter and number of spherical particles, which are key characteristics for assessing the quality of the analyzed powder. Usually, SEM images are manually analyzed using third-party analysis software, which can be time-consuming and often introduces user bias into the measurements. In addition, only a few non-statistically significant samples are taken into consideration for the material characterization. Thus, a method that can overcome the above challenges utilizing state-of-the-art instance segmentation models is introduced. The final proposed model achieved a total mask average precision (mAP50) 67.2 at an intersection over union of 0.5 and with prediction confidence threshold of 0.4. Finally, the predicted instance masks are further used to provide a statistical analysis that includes important metrics such as the particle size distinction.

Polycrystalline materials with grain boundaries (GBs), involving excess free energy because of their structural imperfection, can reduce their energy by the nanoscopic structural changes of the GBs via impurity segregation. Those local changes at GBs can stabilize non-equilibrium nanostructures, resulting in the drastic change in the macroscopic properties of those materials. The mechanism of GB segregation is, however, far from being understood due to difficulties in characterizing both crystallographic and chemical properties of the same GB at atomistic levels. We have therefore developed an analytical method to determine the impurity segregation ability on the same GB at the same nanoscopic location by a joint use of scanning transmission electron microscopy (STEM) and atom probe tomography (APT) combined with ab-initio calculations, and discussed the segregation mechanism in terms of bond distortions at the GB. In the present work, we examined the GBs in Si ingots used for solar cells. They have serious impacts on the solar cell efficiency via the segregation of detrimental impurity atoms, such as oxygen and transition metals introduced inevitably during crystal growth and cell processing, depending on their structural condition at those GBs. Accordingly, precise understanding of the segregation mechanism of impurity atoms is one important issue to produce cost-effective solar cells by engineering the structural condition of impurity atoms segregating at GBs. Three-dimensional distribution of impurity atoms was systematically determined at the typical large-angle GBs (i.e., Σ3{111}, Σ5{013}, Σ9{221}, Σ9{114}, Σ9{111}/{115}, and Σ27{552}) [1-3] and small-angle GBs [3-5] by APT with a low impurity detection limit (0.01 at.% on a GB plane) simultaneously with a high spatial resolution (about 0.4 nm), and it was correlated with the atomic stresses around the GBs estimated by ab-initio calculations based on atomic-resolution scanning TEM data (for large-angle GBs [2]) and by calculations with the elastic theory based on dark-field TEM data (for small-angle GBs [4]). It was hypothesized that oxygen atoms segregate at the bond-centered positions under tensile stresses above about 2 GPa so as to attain more stable bonding network by reducing the local stresses [6]. The number of segregating atoms per unit GB area (N GB ) is in proportion to both the number of the stressed positions per unit GB area (n bc) and the average concentration of oxygen atoms around the GB ([Oi]) with N GB ~ 50n bc[Oi]. This indicates that the probability of oxygen atoms at the segregation positions would be, on average, fifty times larger than in bond-centered positions in defect-free regions. This nanoscopic finding may provide a general guidance to control compositions and band structures at GBs via oxygen segregation.

Nanocomposites, where nanoscale inclusions are embedded in a matrix material, have attracted increasing research attention in recent years. Here, we limit our focus to polymer nanocomposites. Nano-reinforcements (for instance, nanoparticles, nanowires, and nanotubes) have largely disappointed us in manufacturing lightweight, high-strength, and high-toughness polymer composites because of their agglomeration in the matrix—one of the major drawbacks limiting their reinforcing effect. Dispersion of nano-reinforcements is not trivial and often requires expensive facilities and/or processing procedures. It has proven difficult to disperse nano-reinforcements in the matrix via conventional chemical, mechanical, and physical methods. To date, a perfect dispersion of nanoreinforcements in a matrix material has not been achieved. Despite considerable efforts, the mechanical properties of polymer nanocomposites are still far below their theoretically predicted potential. On the other hand, nano-reinforcements and their composites produced through the currently available methods are usually too expensive for use in large-scale industrial applications. To solve these two critical problems, we turned our attention to investigate new concepts for nano-reinforcement dispersion and new synthesis techniques that enable low-cost, high-throughput manufacturing of nanocomposites. Boron carbide (B4C), a lightweight refractory semiconductor, is the third hardest material known to man at room temperature and becomes the hardest above 1100 8C. It has many unique properties such as low density (2.5 g cm ), a small thermal extension coefficient (5.73 10 6 K ), a high melting point (>2400 8C), high resistance to chemical attacks, high thermal stability, a high Seebeck coefficient, and a large neutron absorption cross-section. The combination of these superior properties gives rise to numerous applications, especially under extreme conditions, for example, lightweight body armor, aircraft armor, abrasive wear-resistant materials, solid-state neutron detectors, and, potentially, power generation in deep-space flight applications. Recently, 1D B4C nanostructures, including nanowires and nanorods, have attracted significant attention. To synthesize B4C nanowires, the carbon source is essential. Among most of the available synthesis methods for 1D B4C nanostructures, artificial materials such as carbon black, carbon nanotubes, and activate carbon are selected as carbon source. A stepwise synthetic procedure was usually adopted: metal-catalyst nanoparticles were first synthesized and subsequently added to the precursors as the catalyst. Cotton has been used by mankind for at least 7000 years. It is the oldest and most commonly used nanoporous material, which is constructed from polysaccharide chains arranged into amorphous and crystalline regions. Today, the world production of cotton is approximately 20 million tons per annum, mainly for clothing, paper, and medical uses. In this work, a commercial cotton T-shirt was used as both the template and the carbon source to synthesize large quantities of radially aligned B4C nanowires on carbon microfibers. The as-synthesized B4C-nanowire/carbon-microfiber hybrid structures were then simply dipped in an epoxy resin bath to achieve a high dispersion of B4C nanowires in the matrix and highthroughput manufacturing of laminated polymer nanocomposites. A digital camera image of the 100% cotton T-shirt is shown in Figure 1a. A striking and extremely useful feature of cotton is its ability of absorbing large quantities of liquids, particularly water. Figure 1b is a representative digital camera image of a piece of the cotton T-shirt after the absorption of a solution with Ni(NO3)2 6H2O and amorphous boron powders. Figure 1c shows 6 pieces of final textiles. A typical scanning electron microscopy (SEM) image of carbon microfibers covered with B4C nanowires is shown in Figure 1d. The B4C nanowires, with a diameter ranging from 80 to 200 nm and a length greater than 4mm, grew radially on the entire length of the carbon microfiber (Fig. 1e). The SEM image (Fig. 1f) and transmission electron microscopy (TEM) image (Fig. 1g) together reveal a catalyst particle on the tip of each nanowire. Most of the catalyst particles were spherical and had a diameter distribution of 90 to 250 nm. TEM image (Fig. 1g) also shows that the diameter of the catalyst is larger than that of the corresponding B4C nanowire. Energy dispersive spectroscopy (EDS) reveals that the catalyst particle on the nanowire tip is nickel boride. This suggests that a catalyst is essential for growing such B4C nanowires. Our experimental results proved that most of the catalyst particles can be easily removed by acid treatment (HNO3þHF or HNO3). The B4C