Experimental and numerical assessment of grain boundary energies in polycrystalline uranium dioxide

Uranium dioxide ceramics are widely used as nuclear fuels. Thus, it is important to understand the role of the grain boundaries (GBs) which decisively govern the properties of these polycrystalline materials and subsequently determine their performances. Here, we report a coupled numerical - experimental approach enabling to assess GB energies. Firstly, GB formation energies (γ_gb) were computed for 34 symmetric tilt GBs in UO₂ with molecular dynamics simulations at 1700 K. The surface energies (γ_S) relative to the respective planes of these GBs were calculated as well. The Herring relation was then used to assess the dihedral angles Ψ of the corresponding GB grooves. Secondly, a UO₂ ceramic sample was annealed at 1673 K to obtain GB grooves. The CSL GBs of interest were identified by EBSD and their Ψ angles determined by AFM. Computed and measured Ψ values were found to be very close.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Notch radius effect on fracture toughness of ceramics pertinent to grain size

Unlike fracture toughness, the notch fracture toughness of a ceramic is not a constant; rather, it increases with the notch-root radius ρ in a notched specimen. In this study, by analyzing the fracture measurements of eight different notched ceramics with an average grain size G of 3–40 μm, a simple model describing the relation between the notch fracture toughness and fracture toughness is proposed as a function of the relative notch-root radius ρ/G. The normal distribution is incorporated to consider the inevitable scatter in measurements where fracture mechanisms and errors are present. The results demonstrate that the model can effectively predict the quasi-brittle fracture variation trend for ceramics, including the upper and lower bounds, with 96% reliability, from a normal distribution; thus, it can address virtually all of the experimental data. We also determined that the notch fracture toughness approximates the fracture toughness if ρ ≤ G.     

Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network

In rotary complex machines, collapse of a component may inexplicably occur usually accompanied by a noise or a disturbance emanating from other sources. Rolling bearings constitute a vital part in many rotational machines and the vibration generated by a faulty bearing easily affects the neighboring components. Continuous monitoring, fault diagnosis and predictive maintenance, is a crucial task to reduce the degree of damage and stopping time for a rotating machine. Analysis of fault-related vibration signal is a usual method for accurate diagnosis. Among the resonant demodulation techniques, a well-known resolution often used for fault diagnosis is envelope analysis. But, usually this method may not be adequate enough to indicate satisfactory results. It may require some auxiliary additional techniques. This study suggests some methods to extract features using envelope analysis accompanied by Hilbert Transform and Fast Fourier Transform. The proposed artificial neural network (ANN) based fault estimation algorithm was verified with experimental tests and promising results. Every test was initiated with a reference ANN architecture to avoid inappropriate classification during the evaluation of fitness value. Later, ANN model was modified using a genetic algorithm providing, an optimal skillful fast-reacting network architecture with improved classification results.     

Variable-dimensional vector modulation for perceptual-based DWT blind audio watermarking with adjustable payload capacity

A variable-dimensional vector modulation (VDVM) scheme is introduced to maximize the efficiency of the norm-space DWT-based blind audio watermarking technique. This flexible scheme allows the watermarking algorithm to reach a balance between robustness and capacity, while the imperceptivity is always ensured. The imperfection of applying quantization index modulation in the open-loop case has been rectified. The effectiveness of the proposed scheme is proven using the perceptual evaluation of audio quality (PEAQ) and bit error rates of recovered watermarks under various signal processing attacks. Experimental results show that the proposed VDVM scheme is comparable to other recently developed methods in robustness and imperceptivity even at a capacity as high as 301.46 bps. Such a capacity can be further doubled by halving the dimension of the involved DWT vector, while the robustness is still maintained at a satisfactory level.     

A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm

Targeting the non-linear dynamic characteristics of roller bearing faulty signals, a fault feature extraction method based on hierarchical entropy (HE) is proposed in this paper. SampEns of 8 hierarchical decomposition nodes (e.g. HE at scale 4) are calculated to serve as fault feature vectors, which takes into account not only the low frequency components but also high frequency components of the bearing vibration signals. HE can extract more faulty information than multi-scale entropy (MSE) which considers only the low frequency components. After extracting HE as feature vectors, a multi-class support vector machine (SVM) is trained to achieve a prediction model by using particle swarm optimization (PSO) to seek the optimal parameters of SVM, and then ten different bearing conditions are identified through the obtained SVM model. The experimental results indicate that HE can depict the characteristics of the bearing vibration signal more accurately and more completely than MSE, and the proposed approach based on HE can identify various bearing conditions effectively and accurately and is superior to that based on MSE.     

转速对电磁离心铸造高碳高速钢组织和性能的影响

采用自制的电磁离心铸造机,在磁场强度0.1 T下,对不同转速下电磁离心铸造高碳高速钢的铸态组织和热处理后的组织与性能进行实验研究。结果表明:随着电磁离心铸造机转速的增加,高碳高速钢的铸态组织中共晶碳化物变得越来越细小,沿晶界分布的粗大的碳化物(VC)逐渐变成点状和尖角状。各种转速下,电磁离心铸造高碳高速钢热处理后的硬度、冲击韧度和耐磨性能比普通铸造的高碳高速钢都有明显提高。     

Fabrication of c-axis oriented hydroxyapatite ceramics in a rotating high magnetic field using photopolymerization

C-axis oriented hydroxyapatite (HAp) was prepared by colloidal processing using a photopolymerization reaction in a rotating magnetic field with a UV-curable binder as the solvent. This technique achieved a short processing time of 150 s in a magnetic field as a result of rapid solidification induced by the photopolymerization. The slight difference in refractive index between the HAp material and UV curable resin allowed the fabrication of 1.6 mm thick green compacts. The oriented structure in the compact was maintained from the surface down to a depth of 700 μm, but was randomized at depths of 700 μm and beyond. The orientation degree was retained after sintering at 1250 °C, and the relative density of the compact was approximately 97 %. This technique can be effectively utilized for the manufacture of high-performance biomaterials.     

随机网格结构固结磨料磨盘平面磨削性能研究

针对超精密磨削加工过程对工件材料去除效率、表面质量、亚表面损伤等指标的复合需求,提出一种基于泰勒多边形设计的随机网格结构固结磨料磨盘（textured-fixed abrasive plate, T-FAP）,并以光固化树脂作为结合剂基体材料混合微米级氧化铝磨料制备磨盘,使用MATLAB图像分析和磨抛轨迹仿真方法研究磨盘磨削过程中表面磨损时变图案特征对其加工性能的影响,并通过铝制工件的平面磨削实验对磨盘磨削过程中的材料去除率及工件表面粗糙度进行分析。实验结果表明:相比传统固结磨料磨盘,采用随机网格结构磨盘加工的工件表面粗糙度为0.84 μm,材料去除率为3.21 μm/min,能够在保证材料去除率的同时获得较高的表面精度。