The effects of homogenization treatment on the mechanical properties and corrosion behavior of an LA126 (Mg-12.31Li-5.62Al-0.14Y, wt.%) alloy were systematically investigated. The as-cast alloy was homogenized at 250 and 400 degrees C for 1, 4, and 7 h, and its mechanical properties and corrosion behavior in 3.5 wt.% NaCl solution were evaluated. Homogenization at 400 degrees C increased strength but reduced ductility, a trade-off primarily attributed to the precipitation of MgLiAl2 within the n-Li matrix. While this intermetallic phase contributes to strengthening, its coarser morphology tends to induce local stress concentration, thereby compromising plasticity. In contrast, treatment at 250 degrees C enhanced ductility with only a slight reduction in strength, mainly by alleviating casting-induced elemental segregation. Notably, the specimen homogenized at 400 degrees C for 7 h exhibited superior corrosion resistance, characterized by a more noble corrosion potential (-1.571 V), a lower corrosion current density (5.11 & times; 10-5 A center dot cm-2), and the lowest mass-loss rate (0.67 mg center dot cm-2 center dot d-1). This enhancement is ascribed to the dissolution of anodic AlLi phases and improved microstructural uniformity, which effectively suppresses micro-galvanic coupling and localized corrosion.

Laser communication systems incorporating a Kuder optical path typically impose specific requirements on the intersection of the rotational axes of the two-axis system in a two-dimensional turntable, specifically in terms of the axis non-intersection error between the two axes.The error sources of the deviation degree between the two axes include shaft system shaking, geometric errors of the components themselves, and assembly errors. However, the main errors originate from the geometric errors and assembly errors of the components themselves. Generally, the method to improve the axis non-intersection error between the two axes is to enhance the machining accuracy, which leads to increased manufacturing costs and assembly difficulties. In order to reduce manufacturing costs and assembly difficulties, as well as to improve the accuracy of the axis non-intersection error between the two axes, this paper proposes a design and assembly method that can ultimately meet the requirements through preliminary component design and tooling design, as well as subsequent assembly. A space-free laser communication turntable using this method achieves a axis non-intersection error between the two axes of no more than 0.03mm under the conditions of azimuth axis system shaking, pitch axis system deviation not exceeding 3", and the distance between the pitch axis rotational axis and the azimuth axis mounting surface being 270mm.

Through the analysis of complex datasets, machine learning (ML) has emerged as a powerful tool for performance-driven materials design. In this study, a data-driven model was developed to predict the tensile strength and elongation of transition-metal-based high-entropy alloys (HEAs). By leveraging tensile data of as-cast HEAs along with the physicochemical parameters of specific constituent elements, we constructed predictive models using a two-step feature selection process combined with Bayesian optimization. The resulting models achieved coefficients of determination (R2) of 92.66% for strength and 85.65% for elongation. A multi-objective optimization was conducted using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), where a fitness function guided composition evaluation, and a reward mechanism was introduced to encourage the generation of compositions with higher tensile strength. Experimental validation of designed compositions revealed exceptional performance: Co5.5Cr19.2Al17.5Fe27.3Ni30.5 exhibited 1540.9 MPa strength with 8.0% elongation, while Co7.9Cr19.5Al17.3Fe24.9Ni30.3 showed 1464.6 MPa strength with 15.1% elongation. This study proposes a performance-driven HEA design methodology that significantly enhances material properties, facilitating the transition of materials science from empirical trial-and-error to rational design.

The microstructure-property relationship in PM and IM Al-Zn-Mg-Cu alloy with identical compositions was systematically investigated. PM Al-Zn-Mg-Cu exhibits an ultrafine-grained structure (~2.3 μm) with high grain boundary density and uniformly distributed defects, whereas IM Al-Zn-Mg-Cu retains coarse grains (~136.8 μm) and localized defects. These differences govern diffusion and precipitation behavior. In PM Al-Zn-Mg-Cu, short diffusion paths and abundant nucleation sites promote η′-dominated precipitation, while IM Al-Zn-Mg-Cu is limited by bulk diffusion and dominated by GP zones. Consequently, PM Al-Zn-Mg-Cu forms narrow PFZs with fine and continuous boundary precipitates, whereas IM Al-Zn-Mg-Cu shows wide PFZs and heterogeneous precipitation. This leads to superior strength-ductility synergy in PM Al-Zn-Mg-Cu (YS: 584 MPa, UTS: 672 MPa, elongation: 15.5%). The results demonstrate that diffusion-controlled precipitation, governed by processing-induced microstructural inheritance, dominates the strengthening mechanism.

In this study, machine learning (ML) models were successfully employed to predict the short-term electrochemical corrosion behavior of high-entropy alloys (HEAs) based on their chemical compositions. Considering the vast compositional space of HEAs, which restricts the development of corrosion-resistant HEAs, and the lack of non-destructive methods to qualitatively assess their corrosion resistance, this work represents a significant advancement in the field. The "three-step" method was applied to select the optimal feature set from 38 features, and six ML regression models were trained and compared. The eXtreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT) algorithms demonstrated the highest predictive accuracy (R2 = 81.02 % and 84.64 %, respectively) among the six algorithms. The model's robust generalization capabilities were confirmed through validation on an additional dataset. Moreover, the interpretability of the model was enhanced by employing two analysis methods, which revealed that pH as an environmental factor, electronegativity difference and average electronegativity as empirical parameters, and the concentrations of Cr and Cu as compositional parameters have the most significant impact on the corrosion resistance of HEAs. The proposed methodology and framework have the potential to optimize alloy composition, facilitating the design and development of new corrosion-resistant HEAs.

Rails are susceptible to deformation, wear, cracking, or even fracture under prolonged train loads, posing serious safety risks. Ultrasonic guided wave (UGW) testing offers a fast, long-range, and efficient non-destructive method for rail inspection. A key factor is the selection of the optimal frequency for minimal attenuation to maximize testing range. This paper presents a method for calculating the frequency with minimal attenuation in rails. The propagation and attenuation characteristics of UGWs are analyzed theoretically on free and supported rails, revealing that rail pads significantly affect wave propagation, especially at low-frequencies. The study shows that guided wave attenuation in fixed rails is influenced by both rail material and rail pads damping, which is 103 times greater than that of steel. Experiments confirmed the theoretical and simulation results, showing that in a free rail, the minimal attenuation occurs at 17 kHz (14.2 dB/km), while in operational rails, the lowest attenuation is at 26 kHz (21.6 dB/km), concentrating on the rail head.

The mechanisms underlying the synergistic enhancement of strength, corrosion resistance, and stress corrosion cracking (SCC) resistance in WE43 Mg alloy via T6 heat treatment are elucidated. T6 treatment reduces the quantity of coarse second-phase particles, promotes their more uniform distribution, and facilitates the formation of nano-sized precipitates. Consequently, the alloy exhibits significant mechanical strengthening, with yield strength and ultimate tensile strength increasing by 87 % and 55 %, respectively, primarily attributed to solid solution strengthening and precipitation strengthening mechanisms. Corrosion resistance, evaluated by corrosion current density (icorr) and weight loss rates, improves by approximately 68 % and 70 %, respectively. These improvements are attributed to the suppression of micro-galvanic corrosion and the formation of a dense, stable passivation film with higher rare-earth element content. SCC resistance, assessed through the stress corrosion sensitivity indices IUTS and I epsilon, exhibits enhancements of 14 % and 10 %, respectively. These improvements are attributed to the reduction in corrosion pit formation and the inhibition of microcrack initiation.

High-entropy alloys (HEAs) are known for their unique characteristics, but most material failures are subjected to the material surface, which is determined by the surface properties of the material. This study proposes an effective ultrasonic nanocrystal surface modification (UNSM) treatment to further enhance the surface mechanical properties of (FeCoNiCr)92Ti3.5Al4.5 HEA. More importantly, the deformation mechanisms of HEA treated by UNSM were revealed. Our research revealed that dislocation slip predominantly governs the deformation of coarse grains under low strain, with {100}< 001 > acting as the primary slip system. Conversely, twinning emerges as the primary deformation mechanism for fine grains under high strain. Notably, the refinement of coarse grains is particularly pronounced during the initial stages of deformation.

Laser ultrasonic (LU) testing has attracted considerable attention in the fields of material characterization and defect detection due to its non-destructive nature. However, acquiring a complete wavefield using LU typically requires significant time and resources, motivating the development of more efficient sampling strategies. In this study, a novel approach based on Physics-Informed Neural Networks (PINNs) is proposed to reconstruct the full Lamb wavefield from sparsely sampled experimental data. By embedding the governing physical laws of wave propagation into the neural network framework, the PINN model is trained to infer the wavefield characteristics from a limited number of measurements. Notably, the proposed method successfully reconstructs the complete Lamb wavefield with an accuracy of 88

This study explores the impact of varying solutionizing treatment temperatures on the mechanical properties and corrosion resistance of Mg-10.02Li-5.69Al-0.08Er (LA106) alloy. Results revealed that the alloy treated at 400 °C exhibits a good combination of comprehensive mechanical properties and corrosion resistance. After solutionizing treatment at 400 ℃, solution strengthening and the rod-like α phase shrinks along the width direction and transforms into a needle-like structure, which helps to hinder the dislocation movement and consequently enhancing the strength of the alloy. Additionally, the fine particles and needle-like α phase coordinate deformation with the β phase matrix, maintaining acceptable ductility. The alloy treated at 400 °C demonstrates superior corrosion resistance, primarily attributed to the needle-like α phase effectively impeding corrosion propagation. Furthermore, fine particles with relatively negative Volta potential, distributed within the α phase and β phase, effectively mitigate microgalvanic corrosion between the α phase and β phase. Nevertheless, it is noteworthy that the corrosion resistance of the LA106 alloy at 250 ℃ and 300 ℃ is lower than that observed at 400 ℃. This can be ascribed to the abundance of precipitates distribute along the grain boundaries, leading to galvanic corrosion and subsequently reducing the corrosion resistance.

This study combines the internal standard and the dominant factor PLS to improve the long-term stability of LIBS.

Laser-cladding technology is now widely used in repairing mechanical parts and creating functional coatings. However, interface defects are commonly found in laser-cladding coatings, significantly impacting the fatigue and mechanical properties of these coatings. Therefore, it is necessary to detect interface defects in laser-cladding coatings. In this study, we utilised laser ultrasonics (LU) method to test interface defects non-destructively. However, the signal-to-noise ratio (SNR) of LU signals was poor. To address this issue, an unsupervised deep learning method was used to enhance the SNR. The results showed that the noises were suppressed by the deep learning method. Based on the deep learning-enhanced LU method, surface acoustic waves were used to present B-scan and C-scan images. The artificial defects with a 0.5 mm diameter were detected. Consequently, the deep learning-enhanced LU method proves to be a viable approach for the contactless, online, and non-destructive testing of interface defects in laser-cladding coatings.

Laser Ultrasonic (LU) technology has emerged as a pivotal non-destructive testing method, offering a unique capability to visualise ultrasonic wavefields and identify defects without causing structural damage. However, challenges arise in certain testing scenarios where direct laser irradiation of the sample surface is hindered, resulting in incomplete LU wavefield datasets. This limitation poses a significant obstacle in accurately assessing material integrity and defect detection. This paper explores the application of Physics-Informed Neural Networks (PINNs) for LU wavefield reconstruction and prediction. PINNs are employed to reconstruct wavefields from incomplete data and predict wavefield behaviour at different time instances. Results demonstrate PINNs' effectiveness in accurately reconstructing wavefields, with correlation coefficients exceeding 0.94 between reconstructed and actual wavefields. Additionally, PINNs show promise in predicting LU wavefield data, albeit with slightly reduced accuracy beyond the training range. Moreover, PINNs effectively reduce noise in wavefield data, enhancing clarity and reliability. This study lays groundwork for further exploration of PINNs in LU defect detection.

Machine learning (ML) is progressively supplanting conventional trial-and-error approaches for designing alloys with desirable properties. In this study, four ML regression models were utilized to identify high-entropy alloys (HEAs) with high hardness within the Fe–Co–Ni–Cr system. The Bayesian optimized deep learning (BO-DL) method yielded the highest prediction accuracy (R2 = 0.93). Notably, the BO-DL method is no longer limited to a single HEA system and now can target different alloy systems composed of more elements with reliable prediction results. Furthermore, a genetic algorithm was utilized to search for HEAs with high hardness. The accuracy and reliability of the predictions were experimentally verified. As-cast Fe5Co20Ni10Cr30Al5Ti30 HEA exhibited a remarkable hardness of 890 HV, which is one of the highest for alloys in the Fe–Co–Ni–Cr system. The methodologies and framework proposed in this study can serve as a blueprint for facilitating the design of HEAs.

Laser induced breakdown spectroscopy has shown great potential for application in the online analysis of molten steel composition their effective emission lines cannot be transmitted over long distances in the air. This paper studies the influence of the argon . However, detecting the important C. P. and S component elements in molten steel has been challenging because environment on detecting C. P. and S elements in steel under a 1.5 meter long probe gun, it is found that an argon flow rate that is too large too small is not conducive to spectral measurement. When the gas flow rate is set to 11 L min, the spectrum obtained is the most stable, and the spectral line intensity is the largest. Using 14 standard steel samples, the three elements C. P. and S were detected and quantitatively analyzed under optimal gas flow. After the internal standard calibration, the limit of detection (LOD) of the three clements were 0.000%, 0.04%, and 0.015%, the relative standard deviations (RSD) were 2.34%.1.05% and 1.01% and the correlation coefficients (R) were 0.998. 0.997 and 0.987. respectively. The root square errors (RMSE) were 0.02% 0.02% and 0.03% respectively. The research results of this paper verify the effectiveness of the design of the probe gun and provide an important design basis for the online analysis of C. PP. and S in in the molten steel.

Neutron-sensitive microchannel plates (nMCPs) have applications in neutron detection, including energy spectrum measurements, neutron-induced cross sections, and neutron imaging. ^10 B-doped MCPs (B-MCPs) have attracted significant attention owing to their potential for exhibiting a high neutron detection efficiency over a large neutron energy range. Good spatial and temporal resolutions are useful for neutron energy-resolved imaging. However, their practical applications still face many technical challenges. In this study, a B-MCP with 10 mol ^10 B was tested for its response to wide-energy neutrons from eV to MeV at the Back-n white neutron source at the China Spallation Neutron Source. The neutron detection efficiency was calibrated at 1 eV, which is approximately 300 times that of an ordinary MCP and indicates the success of ^10 B doping. The factors that caused the reduction in the detection efficiency were simulated and discussed. The neutron energy spectrum obtained using B-MCP was compared with that obtained by other measurement methods, and showed very good consistency for neutron energies below tens of keV. The response is more complicated at higher neutron energy, at which point the elastic and nonelastic reactions of all nuclides of B-MCP gradually become dominant. This is beneficial for the detection of neutrons, as it compensates for the detection efficiency of B-MCP for high-energy neutrons.

The back-streaming white-neutron beamline (Back-n) of the China Spallation Neutron Source is an essential neutron-research platform built for the study of nuclear data, neutron physics, and neutron applications. Many types of cross-sectional neutron-reaction measurements have been performed at Back-n since early 2018. These measurements have shown that a significant number of gamma rays can be transmitted to the experimental stations of Back-n along with the neutron beam. These gamma rays, commonly referred to as in-beam gamma rays, can induce a non-negligible experimental background in neutron-reaction measurements. Studying the characteristics of in-beam gamma rays is important for understanding the experimental background. However, measuring in-beam gamma rays is challenging because most gamma-ray detectors are sensitive to neutrons; thus, discriminating between neutron-induced signals and those from in-beam gamma rays is difficult. In this study, we propose the use of the black resonance filter method and a CeBr_3 scintillation detector to measure the characteristics of the in-beam gamma rays of Back-n. Four types of black resonance filters, 181Ta, 59Co, natAg, and natCd, were used in this measurement. The time-of-flight (TOF) technique was used to select the detector signals remaining in the absorption region of the TOF spectra, which were mainly induced by in-beam gamma rays. The energy distribution and flux of the in-beam gamma rays of Back-n were determined by analyzing the deposited energy spectra of the CeBr_3 scintillation detector and using Monte Carlo simulations. Based on the results of this study, the background contributions from in-beam gamma rays in neutron-reaction measurements at Back-n can be reasonably evaluated, which is beneficial for enhancing both the experimental methodology and data analysis.

This study aims to investigate the impact of solutionizing treatments on the mechanical and corrosion resistance properties of the Mg-10.93Li-5.45Al-0.08Er (LA116) alloy. The as-cast LA116 alloy is primarily composed of a single beta phase along with AlLi and MgLiAl2 phases. Results demonstrate that the mechanical and corrosion resistance properties of the alloy experience considerable improvement after solutionizing treatments due to the synergistic effect of phase compositions and solid solution strengthening. Specifically, at 400 degrees C, the mechanical properties of the alloy witness a significant improvement due to the coordination of solid solution strengthening and the dissolution of the second phase. In contrast, the best corrosion resistance is observed at 250 degrees C due to the decomposition and uniform distribution of the AlLi phase.

The effects of thermo-mechanical treatment on the mechanical properties and corrosion resistance behaviour of the (FeCoNiCr) 92 Ti 3.5 Al 4.5 (at.-%) high-entropy alloy (HEA) were studied. Compared with the untreated specimen, the yield and ultimate tensile strength increased by 227.5% and 129.4% after thermo-mechanical treatment, with ductility remaining almost constant. The results indicated that the grain size refined from 233.44 to 2.31 μm, and the dislocation density considerably increased from 2.69 × 10 12 to 1.23 × 10 14 . Notably, after thermo-mechanical treatment, the formation of precipitates can narrow the passivation zone, thus increasing the tendency of pitting corrosion. The corrosion current decreased and the radius of the impedance curve increased, indicating the corrosion resistance behaviour improved, which was attributed to the grain refinement.

The increasing performance of ultra-stable lasers has made them attractive for non-laboratory and space field applications. How to reduce the vibration sensitivity of the optical reference cavity is one of the key points. To develop vibration-insensitive cavity for space applications, we investigate the vibration sensitivity of a cubic cavity with the length of 100 mm theoretically and experimentally. The functional expression of the fractional length change of the cubic cavity versus the cutting depth of the cubic spacer vertices and the compressive forces is presented for the first time. The vibration sensitivity of the 100 mm cubic cavity is measured as low as 2 x 10-11/g. Based on the vibration-insensitive cubic cavity, a 1 Hz-linewidth laser system with an envelope size of 450 mm x 541 mm x 598 mm is developed, which would be the prototype for space applications.