“Lattice Strain Matching”-Enabled Nanocomposite Design to Harness the Exceptional Mechanical Properties of Nanomaterials in Bulk Forms

Nanosized materials are known to have the ability to withstand ultralarge elastic strains (4–10%) and to have ultrahigh strengths approaching their theoretical limits. However, it is a long-standing challenge to harnessing their exceptional intrinsic mechanical properties in bulk forms. This is commonly known as “the valley of death” in nanocomposite design. In 2013, a breakthrough was made to overcome this challenge by using a martensitic phase transforming matrix to create a composite in which ultralarge elastic lattice strains up to 6.7% are achieved in Nb nanoribbons embedded in it. This breakthrough was enabled by a novel concept of phase transformation assisted lattice strain matching between the uniform ultralarge elastic strains (4–10%) of nanomaterials and the uniform crystallographic lattice distortion strains (4–10%) of the martensitic phase transformation of the matrix. This novel concept has opened new opportunities for developing materials of exceptional mechanical properties or enhanced functional properties that are not possible before. The work in progress in this research over the past six years is reported.     

数值模拟在电解槽集气烟道改造中的应用

针对国内一企业现有电解槽集气烟道存在积灰严重、烟道阻力大、净化能耗高、集气效率低等问题,以该集气烟道为研究对象,利用数值模拟计算软件Workbench对集气烟道改造前后烟道内气流流动情况和集气量进行了仿真计算,得出了改造前后烟道内气流流动分布及集气量结果。结果表明,改造后,在相同的集气负压下,烟道集气量有所增加,各烟道集气口集气量分布均匀。最后以数值模拟计算为依据对集气烟道进行改造,实践证明,改造后集气效率明显提高,改造效果良好。     

预时效对Al-5.2Mg-0.45Cu-2.0Zn合金室温稳定性的影响

本文详细研究了预时效对Al-5.2Mg-0.45Cu-2.0Zn合金时效析出行为的影响。预时效不仅提高了合金的室温稳定性,避免了合金烤漆软化,同时提高了合金的烤漆时效响应速度。合金经T4处理后,再峰时效处理后的组织包括粗大的T-Mg₃₂(AlZn)₄₉相以及针状的S-Al₂MgCu相。然而合金经T4P处理后,再峰时效处理的组织只含有细小而高密度的T-Mg₃₂(AlZn)₄₉相而不包括S-Al₂MgCu相。自然时效后不稳定的原子团簇在180℃时效后会回溶到基体中, 从而抑制了合金的时效析出强化。而预时效后生成的稳定的原子团簇会成为180℃时效的形核点,显著提高了合金的时效响应速度。     

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.     

Influence of hexagonal to orthorhombic phase transformation on diffusion-controlled dendrite evolution in directionally solidified Sn–Ni peritectic alloy

The hexagonal to orthorhombic (HO) transformation from β-Ni₃Sn₂ (hexagonal) phase to α’-Ni₃Sn₂ (orthorhombic) phase was confirmed in directionally solidified Sn–Ni peritectic alloys. It is shown that the remelting/resolidification process which is caused by both the temperature gradient zone melting (TGZM) and Gibbs?Thomson (G?T) effects can take place on secondary dendrites. Besides, the intersection angle between the primary dendrite stem and secondary branch (θ) is found to increase from π/3 to π/2 as the solidification proceeds. This is the morphological feature of the HO transformation, which can change the diffusion distance of the remelting/ resolidification process. Thus, a diffusion-based analytical model is established to describe this process through the specific surface area (S_V) of dendrites. The theoretical prediction demonstrates that the remelting/resolidification process is restricted when the HO transformation occurs during peritectic solidification. In addition, the slope of the prediction curves is changed, indicating the variation of the local remelting/resolidification rates.     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

Difference Detection Between Two Images for Image Monitoring

In manufacturing industries, images are commonly used for quality control purposes. In such applications, if the quality of the products is good, then their images should be all similar to the image of a good-quality product. Therefore, comparison of images is a fundamental task in image-based quality control. This problem, however, is complicated in the sense that (1) observed images often contain noise, and (2) the related images need to be geometrically matched up first because images of different products could be geometrically mismatched because the relative positions between a camera and different products are often not exactly the same. The first issue requires a statistical method that can remove noise, and the second issue is related to the so-called image registration problem in the image processing literature. In this article, we propose effective methods for detecting difference between two images of products, and our proposed methods can accommodate both noise and geometric mismatch mentioned above. Theoretical results and numerical examples show that they can work effectively in applications.     

Microstructural characteristics of as-forged and β-air-cooled Zr–2.5Nb alloy

Multiple characterization and analysis techniques including electron backscatter diffraction (EBSD), electron channeling contrast (ECC) imaging, transmission electron microscopy (TEM) and microhardness test were jointly employed to investigate microstructural characteristics such as local composition, morphology, grain boundary characteristics and interphase orientation relationship of a forged Zr–2.5Nb alloy before and after β-air-cooling. Results show that the as-forged specimen is composed of equiaxed and lamellar α grains and continuous net-like β-Zr films. After the β-air-cooling, the microstructure of the specimen is featured by basket-weave Widmanstätten structure, in which the inter-α-plate second phases are nanoscale β-Zr. Analyses for crystallographic orientations reveal that the Burgers relationship has been strictly followed during the β→α cooling. Compared to the as-forged specimen, the hardness of the β-air-cooled specimen is higher, which could be attributed to the decreased structural sizes of both α and β phases, and the increased fraction of high angle boundaries as well.     

基于十字形阵列和2D-MUSIC算法的Lamb波检测

采用由压电传感器组成的十字形阵列进行Lamb波信号的激励和接收，提出一种二维多重信号分类（2D-MUSIC）方法对铝板中的缺陷进行定位检测。选用合适的频率激励产生单一模态信号，可避免多模态的影响，降低Lamb波的频散；利用2D-MUSIC算法对接收信号进行分段处理，并结合反射信号和二维导向矢量对铝板中的缺陷进行定位。结果表明，提出的2D-MUSIC算法对铝板中的缺陷定位比传统MUSIC算法更精准。