Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine Learning

Dendrites are important microstructures in single-crystal superalloys. The distribution of dendrites is closely related to the heat treatment process and mechanical properties of single-crystal superalloys. The primary dendrite arm spacing (PDAS) is an important length scale to describe the distribution of dendrites. In this work, the second-generation single crystal superalloy HT901 with a diameter of 15 mm was imaged under a metallurgical microscope. An automatic dendrite core identification and full-field quantitative statistical analysis method is proposed to automatically detect the dendrite core and calculate the local PDAS. The Faster R-CNN algorithm combined with test time augmentation (TTA) technology is used to automatically identify the dendrite cores. The local multi-directional algorithm combined with Voronoi tessellation is used to determine the local nearest neighbor dendrite and calculate the local PDAS and coordination number. The accuracy of using Faster R-CNN combined with TTA to detect the dendrite core of HT901 reaches 98.4%, which is 15.9% higher than using Faster R-CNN alone. The algorithm calculates the local PDAS of all dendrites in H901 and captures the Gaussian distribution of the local PDAS. The average PDAS determined by the Gaussian distribution is 415 μm, which is only a small difference from the average spacing λ¯ (420 μm) calculated by the traditional method. The technology analyzes the relationship between the local PDAS and the distance from the center of the sample. The local PDAS near the center of HT901 are larger than those near the edge. The results suggests that the method enables the rapid, accurate and quantitative dendritic distribution characterization

Development and Research Regarding Stormwater Runoff Management: Bibliometric Analysis from 2001 to 2021

As a result of climate change and urbanization, human activities are placing increasing pressure on nature, including with regard to urban stormwater runoff; consequently, various concepts related to urban stormwater runoff management have been proposed to tackle this problem in multiple countries. In this study, the latest research and techniques related to stormwater runoff management are reviewed in detail. A bibliometric analysis of proposed stormwater runoff management concepts developed from 2001 to 2021 was conducted based on a screening of 1771 studies obtained from the Web of Science (WoS). Bibliometric analysis is a research method that can be used to quantitatively analyze academic literature. Visualization of the data obtained from the literature using CiteSpace software and subsequent analysis of patent data through S-curve prediction were performed. The United States, China, and Australia were the top three countries from which publications on this issue were sourced. Each country tends to study its own most relevant issues and has a particularly clear understanding of its own research landscape. The development of stormwater runoff management concepts was analyzed using reference emergence analysis. This was followed by keyword clustering and keyword emergence analysis to identify current research hotspots, trends, technological developments, and limitations. The limitations and emerging trends related to current stormwater runoff management concepts are discussed thoroughly, and suggestions for future studies are provided

A Quantitative Method for the Composition of 7B05 Cast-Rolled Aluminum Alloys Based on Micro-Beam X-ray Fluorescence Spectroscopy and Its Application in Element Segregation of Recrystallization

Microscopic content segregation is among the important reasons for the anisotropy of mechanical properties in the cast-rolled sheets of the 7B05 aluminum alloy. It is of great significance to study the uniformity of aluminum alloys in terms of the microscopic composition and structure. In this study, a precise method for composition quantification based on micro-beam X-ray fluorescence spectroscopy is established by parameter optimization and a calibration coefficient. Furthermore, this method was applied for exploring and quantifying the relationship between recrystallization and deformation microstructures. The results show that the comprehensive measurement effects of all elements are the best when the X-ray tube voltage is 50 kV, the current is 150 μA, and the single-pixel scanning time is 100 ms. After verification, the sum of differences between the measured values and the standard values for all elements using the calibration coefficient is only 0.107%, which confirms the accuracy of the optimized quantitative method. Three types of segregation indexes in national standards were used to capture small differences, and finally ensure that the segregation degrees of elements are Ti > Fe > Cr > Cu > Mn > Zr > Zn > Al. The quantitative segregation results obtained by the spatial-mapping method show that the difference in the content of Al and Zn is approximately 0.2% between the recrystallization region and the deformation region, the difference in the content of Fe and Ti is 0.018% and 0.013%, the difference in the content of Cr, Cu and Zr is approximately 0.01%, and the difference in the content of Mn is not obvious, only 0.004%

Quantitative Characterization of the γ’ Phase Distribution in the Large-Scale Area of the Second-Generation Nickel-Based Single Crystal Blade DD5

Nickel-based single crystal superalloy blades have excellent high-temperature performance as the hot end part of the aero-engine turbine. The most important strengthening phase in the single crystal blade is the γ’ phase, and its morphology and size distribution directly affect the high temperature performance of the single crystal blade. In this work, scanning electron microscopy (SEM) was used to obtain the microscopic images of the γ’ phase in multiple large continuous fields of view in the transverse sections of single crystal blades, and the quantitative statistical characterization of the γ’ phase was performed by image segmentation method based on deep learning. The 20 μm × 20 μm region was selected from the primary dendrite arm, the secondary dendrite arm, and the interdendrite to statistically analyze the γ’ phases. The statistical results show that the average size of the γ’ phase at the position of the interdendrite is significantly larger than the average size of the γ’ phase at the position of the dendrite; the sizes of the γ’ phase at the primary dendrite arm, the secondary dendrite arm and the interdendrite all obey the normal distribution; about 3.17 × 107 γ’ phases are counted in 20 positions in the 5 transverse sections of the single crystal blade in a total area of 5 mm2, and the size, geometric morphology and area fraction of all γ’ phases are respectively counted. In this work, the quantitative parameters of the γ’ phases at 4 different positions of the section of the single crystal superalloy DD5 blade were compared, the size and area fraction of the γ’ phases at the leading edge and the trailing edge were smaller, and the shape of the γ’ phase of the leading edge and the trailing edge is closer to the cube

Correlation among Composition, Microstructure and Hardness of 7xxx Aluminum Alloy Using Original Statistical Spatial-Mapping Method

The quantitative study of the relationship between material composition, microstructure and properties is of great importance for the improvement in material properties. In this study, the continuous data of elemental composition, recrystallization, hardness and undissolved phase distribution of the same sample in the range of 60 to 150 square millimeters were obtained by high-throughput testing instrument. The distribution characteristics and rules of a single data set were analyzed. In addition, each data set was divided into micro-areas according to the corresponding relationship of location, and the mapping between multi-source heterogeneous micro-area data sets was established to analyze and quantify the correlation between material composition, structure and hardness. The conclusions are as follows: (1) the average size of the insoluble phase in the middle of the two materials is larger than that of the surface, but due to the existence of central segregation, the average area of the T4 insoluble phase showed an abnormal decrease; (2) there was positive micro-segregation of Al, Cr, Ti, and Zr elements, and negative micro-segregation of Zn, Cu, and Fe elements in the recrystallized grains of the T5 middle segregation zone; (3) the growth process of the insoluble phase was synchronous with the recrystallization proportion and the size of the recrystallized grains; (4) the composition segregation and recrystallized coarse grains were the main reasons for the formation of low hardness zone in T4 and T5 materials, respectively

Fine Classification Method for Massive Microseismic Signals Based on Short-Time Fourier Transform and Deep Learning

Numerous microseismic signals are produced by rock mass fracture during earthquakes, geological disasters, or underground excavations. Moreover, a large amount of noise signals are captured during microseismic signal monitoring. Specifically, some noise signals closely resemble microseismic signals, which severely impedes the rapid and accurate detection of the latter and the assessment of geological hazards. Therefore, we propose a precise model for identifying and classifying microseismic signals based on deep learning technology and short-time Fourier transform (STFT) technology. First, the STFT time–frequency analysis reveals the unique characteristics of noise, microseismic, and blasting signals, thereby allowing noise signals that are very similar to microseismic signals in the time domain to be finely distinguished. Second, the introduced attention mechanism focuses the classification on essential signal features. Finally, because tens of thousands of actual monitoring data points are considered, the deep neural network for microseismic classification is trained and tested under complex geological engineering conditions. The results demonstrate that the neural network model has good time–frequency feature extraction ability, and the well-trained model can satisfactorily complete daily classifications. Moreover, the model performs well when classifying similar noise and low-SNR microseismic signals. We believe that this type of signal-processing method, which considers multiple perspectives, can be extended to data processing in many other data-driven fields

The Origin of Threshold Reduction in Random Lasers Based on MoS₂/Au NPs: Charge Transfer

Random lasers have attracted much attention in recent years owing to their advantages of a simple fabrication process, low processing cost, and material flexibility for any lasing wavelengths. They provide a roadmap for the design of ultra-bright lighting, displays, etc. However, the threshold reduction in random nanolasers remains a challenge in practical applications. In this work, lower-threshold random laser action from monolayer molybdenum disulfide film-encapsulated Au nanoparticles (MoS2/Au NPs) is demonstrated. The observed laser action of the MoS2/Au NPs shows a lower threshold of about 0.564 µJ/mm2, which is about 46.2% lower than the threshold of random lasers based on Au NPs. We proposed that the charge transfer between MoS2 and the gain material is the main reason for the reduction in the random laser threshold. The finite-difference time-domain (FDTD) method was used to calculate the lasing action of these two nanostructures. When charge transfer is taken into account, the theoretically calculated threshold of the MoS2/Au NPs is reduced by 46.8% compared to Au NP samples, which is consistent with the experimental results. This study provides a new mechanism to achieve low-threshold and high-quality random lasers, which has the potential to facilitate the application of random lasers and the development of high-performance optoelectronic devices

Gini feature importance of the model predicting West Nile Virus cases in the Chicago area, with the 25 variables after removing the highly correlated ones.

The higher the y-value, the more important the feature is to the model. The variables are grouped into four main categories. Blue bars represent the land cover variables. Orange bars represent the mosquito infection rates. Green bars represent the weather variables. Red bars represent the demographic variables. We found that total population is the most important variable in the model. The weather and MIRs are also strong predictors.</p