Effect of microwave sintering on the microstructure and mechanical properties of AA7075/B4C/ZrC hybrid nano composite fabricated by powder metallurgy techniques

AA7075 + 6%B4C+3%ZrC nano hybrid composite was successfully fabricated, with nano reinforcements composition in AA7075 alloy selected based on previous investigation, to achieve better mechanical performance. Two different sintering techniques, namely conventional and microwave, were implemented to determine the effect on microstructural and mechanical properties. Microstructural investigation was performed with the help of W-SEM. Tensile, compression, and hardness were measured with the help of UTM and Vickers microhardness machine. Porosity was calculated by using Archimedes principle. It was observed that the added nano ZrC particles formed agglomerates and the B₄C particles were distributed homogenously. Composites processed by microwave sintering showed excellent mechanical properties compared to the conventionally sintered composites. No intermetallic compounds were detected in microwave sintered composites through XRD analysis, indicating strong and clean interface bonds between matrix and reinforcement particles. High strain to fracture value of 12.24% was noted in microwave sintered nano hybrid composite, while it was 6.12% for conventional sintered one. Fractography revealed no peeling action of reinforcements from the matrix material, and the mode of failure was brittle. It was concluded that, while fabricating nano range hybrid composites, the implementation of advanced sintering technique (microwave sintering) with low sintering temperatures and low sintering times with internal heat generations, helps in eliminating defects that may develop because of high surface energies of nano range reinforcements.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Hydrogen desorption from alloys Mg–Cu(–KCl): Cu catalysis in detail

Effect of chemical composition of Mg-xCu based alloys (x = 9.94–58.00 wt %) modified by KCl upon their hydrogen storage performance was studied. Kinetic curves and pressure-concentration isotherms were measured in the ranges up to 60 bar and 388 °C, respectively. It was observed that desorption rate dc/dt is not significantly influenced by the composition. Unknown Cu-rich phase was detected that has shown a catalytic effect on desorption from a mixture with other phases. Activation energy of hydrogen desorption decreased with increasing x from 180 kJ/mol down to 98 kJ/mol. Average hydride dissociation enthalpy, ΔH, for the lowest plateau was 75 kJ/mol which is equal to literature value for pure Mg. Slightly lover average value, 67 kJ/mol was obtained for the second plateau and ΔH for the third one decreased from 70 kJ/mol for the lowest to 49 kJ/mol for the highest x.     

High temperature tensile properties of 316LN stainless steel investigated using automated ball indentation technique

Type 316LN stainless steel (SS) is the principal structural material for the components of sodium cooled fast reactors operating under elevated temperature conditions. In order to assess the degradation in strength of service exposed components using a small specimen testing technique such as automated ball indentation (ABI), it is necessary to carry out prior detailed ABI studies on the virgin material. In this investigation, the tensile behaviour of as-received 316LN SS were investigated at several temperatures in the range 298–973 K using ABI technique. The load-depth of indentation data measured from ABI tests was analyzed using semi-empirical relationships to obtain the tensile properties. The yield stress and the flow curves were determined by correlating ABI results with corresponding uniaxial tensile test results. Trend curve for tensile strength with temperature, as estimated from ABI tests, exhibited a plateau region in the temperature around 823 K, similar to uniaxial tensile tests. The variations of strength coefficient, strain hardening exponent, yield ratio, hardness and uniform ductility with temperature were evaluated from ABI tests. The ABI technique was found to estimate the influence of temperature on tensile properties sensitively.     

On the numerical solution of Fredholm integral equations utilizing the local radial basis function method

The current investigation describes a computational technique to solve one- and two-dimensional Fredholm integral equations of the second kind. The method estimates the solution using the discrete collocation method by combining locally supported radial basis functions (RBFs) constructed on a small set of nodes instead of all points over the analysed domain. In this work, we employ the Gauss–Legendre integration rule on the influence domains of shape functions to approximate the local integrals appearing in the method. In comparison with the globally supported RBFs for solving integral equations, the proposed method is stable and uses much less computer memory. The scheme does not require any cell structures, so it is meshless. We also obtain the error analysis of the proposed method and demonstrate that the convergence rate of the approach is high. Illustrative examples clearly show the reliability and efficiency of the new method.     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。     

Improved ball milling method for the synthesis of nanocrystalline TiFe compound ready to absorb hydrogen

In this study, we propose a method to produce nanocrystalline TiFe powder by high-energy ball milling, in order to avoid the common sticking problem of the material to the milling tools, assuring a material prompt to absorb hydrogen as well. The method consists of making a preliminary milling operation with the elemental powders (50:50 stoichiometric ratio) to form a strong adhered layer of the milled material on the surfaces of the vial and balls. The main milling operation is then performed with a new powder charge (same composition as before), but now adding a process control agent (stearic acid). Various processing times - 2, 6, 10 and 20 h - were used in the milling experiments. Nanocrystalline TiFe was synthesized in this way with low oxygen contamination, full yields for milling times of 6 h or over, requiring no heat treatments for the first hydrogen absorption. Hydrogen storage capacity of 1.0 wt% at room temperature under 20 bar was attained by the sample milled for 6 h. Kinetic data from samples milled for 2 h and 6 h agreed with Jander model for the rate limiting step of the hydriding reaction, which is based on diffusion with constant interface area.     

A two-stage vision-based method for measuring the key parameters of ball screws

Ball screws are crucial for improving the reliability and interchangeability of transmission mechanical systems; however, existing contact measurement methods that utilise stylus contact are not efficient, which precludes their use for rapid in-situ geometry evaluation. This paper presents a vision-based two-stage method for rapid measurement of key parameters (raceway arc radii and centre distance) of ball screws. The edge contour information is extracted from the acquired image using the dual-tree complex wavelet transform and non-maximal suppression. In the matching stage, a shape-matching algorithm is used for detecting approximate geometrical centres of raceway arcs. The refinement stage, on the other hand, is implemented for acquiring precise dimensional results. Furthermore, the method of averaging multiple measurements is performed to suppress random noise. A comparative experiment is presented to validate the robustness of the proposed method. Based on experimental results, the calculated mean absolute errors in the measurement of the two raceway arc radii and the centre distance are found to be 0.0082 mm, 0.0079 mm and 0.0055 mm, respectively. This study therefore paves the way for key parameter measurement without removing ball screws.     

Microstructures and mechanical properties of B4C-TiB2-SiC composites fabricated by ball milling and hot pressing

B₄C-TiB₂-SiC composites were fabricated via hot pressing using ball milled B₄C, TiB₂, and SiC powder mixtures as the starting materials. The impact of ball milling on the densification behaviors, mechanical properties, and microstructures of the ceramic composites were investigated. The results showed that the refinement of the powder mixtures and the removal of the oxide impurities played an important role in the improvement of densification and properties. Moreover, the formation of the liquid phases during the sintering was deemed beneficial for densification. The typical values of relative density, hardness, bending strength, and fracture toughness of the composites reached 99.20%, 32.84?GPa, 858?MPa and 8.21?MPa?m^1/2, respectively. Crack deflection, crack bridging, crack branching, and microcracking were considered to be the potential toughening mechanisms in the composites. Furthermore, numerous nano-sized intergranular/intragranular phases and twin structures were observed in the B₄C-TiB₂-SiC composite.