螺伞齿轮轴纵向贯穿性开裂原因分析

通过断口的宏观及微观形态与金相组织的分析,查明由于锻件本身存在的缺陷,导致螺伞齿轮轴在淬火过程中产生裂纹,随后裂纹沿径向及轴向扩展,直至纵向贯穿性开裂。     

Atomically Thin Defect‐Rich Fe–Mn–O Hybrid Nanosheets as High Efficient Electrocatalyst for Water Oxidation

Engineering non‐noble metal–based electrocatalysts with superior water oxidation performance is highly desirable for the production of renewable chemical fuels. Here, an atomically thin low‐crystallinity Fe–Mn–O hybrid nanosheet grown on carbon cloth (Fe–Mn–O NS/CC) is successfully synthetized as an efficient oxygen evolution reaction (OER) catalyst. The synthesis strategy involves a facile reflux reaction and subsequent low‐temperature calcination process, and the morphology and composition of hybrid nanosheets can be tailored conveniently. The defect‐rich Fe–Mn–O ultrathin nanosheet with uniform element distribution enables exposure of more catalytic active sites; moreover, the atomic‐scale synergistic action of Mn and Fe oxide contributes to an enhanced intrinsic catalytic activity. Therefore, the optimized Fe–Mn–O hybrid nanosheets, with lateral sizes of about 100–600 nm and ≈1.4 nm in thickness, enable a low onset potential of 1.46 V, low overpotential of 273 mV for current density of 10 mA cm^?2, a small Tafel slope of 63.9 mV dec^?1, and superior durability, which are superior to that of individual MnO₂ and FeOOH electrode, and even outperforming most reported MnO₂‐based electrocatalysts.     

Defect Restoration of Low‐Temperature Sol‐Gel‐Derived ZnO via Sulfur Doping for Advancing Polymeric Schottky Photodiodes

This study shows that the deep‐level defect states in sol‐gel‐derived ZnO can be efficiently restored by facile sulfur doping chemistry, wherein the +2 charged oxygen vacancies are filled with the S^2? ions brought by thiocyanate. By fabricating a solution‐processed polymeric Schottky diode with ITO/ZnO as the cathode, the synergetic effects of such defect‐restored ZnO electron selective layers are demonstrated. The decreased chemical defects and thus reduced mid‐gap states enable to not only enlarge the effective built‐in potential, which can expand the width of the depletion region, but also increase the Schottky energy barrier, which can reduce undesired dark‐current injection. As a result, the demonstrated simple‐structure blue‐selective polymeric Schottky photodiode renders near‐ideal diode operation with an ideality factor of 1.18, a noise equivalent power of 1.25 × 10^?14 W Hz^?1/2, and a high peak detectivity of 2.4 × 10¹³ Jones. In addition, the chemical robustness of sulfur‐doped ZnO enables exceptional device stability against air exposure as well as device‐to‐device reproducibility. Therefore, this work opens the possibility of utilizing low‐temperature sol‐gel‐derived ZnO in realizing high‐performance, stable, and reliable organic photodiodes that could be employed in the design of practical image sensors.     

预防大型锻件缺陷的方法

分析了锻件生产过程中，在锻造、热处理以及炼钢中经常产生缺陷的原因，并提出了预防措施。     

高熵合金拉伸测试不确定性和力学性能表征

摘要：高熵合金的拉伸力学性能测试不确定性,使现行传统的拉伸(压缩)试验技术,不适合于高熵合金的力学性能测量。金属拉伸力学性能测试不确定性,是拉伸试验弹性变形阶段,张应力改变了被测金属原子水平上的微观结构引起的。在此弹性变形微观理论基础上,提出用“力学性能 拉伸应变速率”曲线,表征高熵合金的原始力学性能,服役力学性能,以及加工变形力学性能。     

数据驱动的热轧带钢边部线状缺陷智能预报模型

 边部线状缺陷是热轧带钢易发缺陷,不仅严重影响成材率,还可能对热轧下游工序生产过程造成影响。边部线状缺陷的影响因素复杂多变,建立精确的机理预报模型十分困难。为此,首先分析边部线状缺陷的主要影响因素;然后以智能方法为基础,分别建立了基于逻辑回归与神经网络的边部线状缺陷智能预报模型,并分析了2个模型的精度与泛化能力;最后,以神经网络智能预报模型为基础,对加热工艺参数进行优化,使缺陷发生率与封闭率均大幅降低。研究结果对提高热轧带钢表面质量具有实践意义,可推广应用于同类轧线。     

低碳钢夹杂物产生原因及控制

 CSP线100 t转炉生产SPHD冷轧夹杂物降等比例比较高,经分析夹杂降等主要原因是钢水夹杂物去除不彻底。通过优化转炉脱氧工艺减少夹杂产生,提高转炉终点温度减少LF处理过程中钢水中铝的氧化;精炼炉采用钢包渣循环渣利用快速造白渣,控制钢包渣w((CaO))/ w((Al₂O₃))为1.4～1.8,碱度为18～25,喂钙线前白渣时间大于8 min, LF炉出站镇静大于15 min促进夹杂的去除;开浇前中间包用氩气吹扫2 min、加硅钙粉防止开浇塞棒上涨,确保浇铸过程中恒拉速、钢包换包时中间包钢水大于20 t减少液位波动。最终基板夹杂降等率由1.8%降低至0.2%。     

Synthesis of mechanisms for single and hybrid tasks using differential evolution

The optimal dimensional synthesis for planar mechanisms using differential evolution (DE) is demonstrated. Four examples are included: in the first case, the synthesis of a mechanism for hybrid-tasks, considering path generation, function generation, and motion generation, is carried out. The second and third cases pertain to path generation, with and without prescribed timing. Finally, the synthesis of an Ackerman mechanism is reported. Order defect problem is solved by manipulating individuals instead of penalizing or discretizing the search space for the parameters. A technique that consists in applying a transformation in order to satisfy the Grashof and crank conditions to generate an initial elitist population is introduced. As a result, the evolutionary algorithm increases its efficiency.     

基于ButterWorth滤波和EM算法的电子布疵点检测

为了精确定位和分割电子布生产过程中产生的疵点,提出一种基于ButterWorth滤波和EM算法的电子布疵点检测方法。因电子布的背景纹理对疵点检测存在影响,首先采用ButterWorth滤波弱化图像背景纹理信息;再应用高斯混合模型对预处理后的图像进行表征,通过EM算法迭代模型的最优解,对像素进行标记;最后,根据标记结果分离背景与疵点区域,定位并分割疵点。实验结果表明,该方法既能准确定位疵点位置,又能有效保留疵点的细节信息,并且能够检测多种类型的疵点,在疵点检测方面具有一定的参考价值。     

DP-Share: Privacy-Preserving Software Defect Prediction Model Sharing Through Differential Privacy

In current software defect prediction (SDP) research, most previous empirical studies only use datasets provided by PROMISE repository and this may cause a threat to the external validity of previous empirical results. Instead of SDP dataset sharing, SDP model sharing is a potential solution to alleviate this problem and can encourage researchers in the research community and practitioners in the industrial community to share more models. However, directly sharing models may result in privacy disclosure, such as model inversion attack. To the best of our knowledge, we are the first to apply differential privacy (DP) to privacy-preserving SDP model sharing and then propose a novel method DP-Share, since DP mechanisms can prevent this attack when the privacy budget is carefully selected. In particular, DP-Share first performs data preprocessing for the dataset, such as over-sampling for minority instances (i.e., defective modules) and conducting discretization for continuous features to optimize privacy budget allocation. Then, it uses a novel sampling strategy to create a set of training sets. Finally it constructs decision trees based on these training sets and these decision trees can form a random forest (i.e., model). The last phase of DP-Share uses Laplace and exponential mechanisms to satisfy the requirements of DP. In our empirical studies, we choose nine experimental subjects from real software projects. Then, we use AUC (area under ROC curve) as the performance measure and holdout as our model validation technique. After privacy and utility analysis, we find that DP-Share can achieve better performance than a baseline method DF-Enhance in most cases when using the same privacy budget. Moreover, we also provide guidelines to effectively use our proposed method. Our work attempts to fill the research gap in terms of differential privacy for SDP, which can encourage researchers and practitioners to share more SDP models and then effectively advance the state of the art of SDP.