SiC_P/Al基复合材料在等径角挤扭变形中的界面原子扩散行为

为了研究金属基复合材料在剧烈塑性变形(SPD)过程中增强颗粒与金属基体的界面连接机制,通过等径角挤扭(ECAP-T)工艺在较低温度下制备块状10wt%SiCP/Al基复合材料,并对经过1、2和4道次ECAP-T变形的SiC颗粒与纯Al之间的界面反应以及元素扩散进行了研究。通过TEM和XPS研究了界面和元素扩散,结果表明:即使在较低的外界制备温度下,Al和SiC颗粒表面的SiO2层也能够发生反应,形成主要由Al2O3组成的界面层。相比理论计算值,ECAP-T变形可以将Al的扩散系数提高约1016倍,增强扩散的原因主要是ECAP-T变形促使界面温度升高,且在铝基体内产生空位、位错和晶界等高密度晶格缺陷。     

基于图像处理的碳纤维增强树脂基复合材料固化压力-缺陷-力学性能建模与评估

利用热压罐成型工艺制备了不同固化压力条件下的碳纤维增强树脂基复合材料层合板,分析了超声相控阵C扫描图像与微观缺陷的对应关系,研究了固化压力、孔隙缺陷及力学性能之间的关联规律。结果表明：利用超声C扫描图像差异能够表征孔隙等缺陷含量,在本实验条件下,固化压力由0 MPa提高到0.6 MPa,复合材料孔隙率降低96.7%,拉伸强度（TS）和层间剪切强度（ILSS）分别提高56.1%和68.8%。在此基础上,对不同固化压力条件下制备的复合材料层合板的超声相控阵C扫描图像进行图像处理并定义成型质量指数,实现了基于C扫描图像对孔隙缺陷的定量表征。最后,通过对孔隙缺陷检测、力学性能测试及图像定量化评价结果进行数学拟合,建立了基于图像处理的固化压力-缺陷-力学性能之间的数学关联模型（CPDMP模型）,并给出了成型质量指数阈值为81%,及可接受的孔隙率应不高于1.1%,相应的固化压力应不低于0.35 MPa。     

Free vibration analysis of FGM plates with internal defects using extended isogeometric hybrid collocation–Galerkin method

Abstract

Modelling of plates with internal defects or cut outs is often an issue in the traditional isogeometric methods and hence extended isogeometric methods are developed. In order to reduce the computational cost involved in extended isogeometric methods based on weak form, a new extended isogeometric hybrid collocation–Galerkin method is proposed in the present paper. The natural frequencies of a homogeneous and functionally graded material plate with internal defects of varying gradient index, sizes and shapes are obtained using the proposed method. The obtained results are compared and found to be in good agreement with the reference results.     

基于机器视觉的插座品质检测方法

针对目前人工成本上升,人眼易出现视觉疲劳导致检测效率低下等问题,本文提出了一种基于机器视觉的插座品质检测方法.主要研究内容如下:1)研究了基于模板匹配的插座机器视觉定位方法.2)研究了基于灰度均值的螺丝缺陷机器视觉检测方法.3)研究了基于灰度标准偏差的插孔缺陷机器视觉检测方法.根据以上检测方法,编写了检测软件,在生产线上进行测试,对插座螺丝和插孔缺陷的识别率达到100%,平均每帧图像处理仅耗时100ms,表明该方法具有准确率高、速度快的优点.     

管道内腐蚀缺陷超声在线检测机器人设计

针对管道内腐蚀缺陷超声在线检测数据量大、灵敏度低的难题,采用超声反射单脉冲距离检测法,设计制造了一种新型长输管道缺陷检测机器人.设计了满足检测精度的探头阵列、机器人结构以及检测数据处理系统;进行了标准模拟缺陷检测实验,校准了阵列探头的性能.实验表明,所设计机器人检测数据量小,其检测灵敏度可达到检测10 mm×10 mm×1 mm(长×宽×深)的缺陷.     

Soldering defect detection in automatic optical inspection

This paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both localization and classifications tasks were considered. For the localization part, in contrast to the existing methods that are highly specified for particular PCBs, we used a generic deep learning method which can be easily ported to different configurations of PCBs and soldering technologies and also gives real-time speed and high accuracy. For the classification part, an active learning method was proposed to reduce the labeling workload when a large labeled training database is not easily available because it requires domain-specified knowledge. The experiments show that the localization method is fast and accurate. In addition, high accuracy with only minimal user input was achieved in the classification framework on two different datasets. The results also demonstrated that our method outperforms three other active learning benchmarks.     

跨项目软件缺陷预测方法研究综述

软件缺陷预测是提高软件测试效率、保证软件可靠性的重要途径,已经成为目前实证软件工程领域的研究热点。在软件工程中,软件的开发过程或技术平台可能随时变化,特别是遇到新项目启动或旧项目重新开发时,基于目标项目数据的传统软件缺陷预测方法无法满足实践需求。基于迁移学习技术采用其他项目中已经标注的软件数据实现跨项目的缺陷预测,可以有效解决传统方法的不足,引起了国内外研究者的极大关注,并取得了一系列的研究成果。首先总结了跨项目软件缺陷预测中的关键问题。然后根据迁移学习的技术特点将现有方法分为基于软件属性特征迁移和软件模块实例迁移两大类,并分析比较了常见方法的特点和不足。最后探讨了跨项目软件缺陷预测未来的发展方向。     

Photoluminescene study acceptor defects in lightly doped n type GaSb single crystals

Lightly Te-doped GaSb samples grown by the liquid encapsulated Czochralski(LEC) method have been studied by Hall measurements and low-temperature PL spectroscopy. The results suggest that acceptor-related antisite is the dominant defect in n-type GaSb with low Te-doping concentration. As the Te concentration increases, gallium vacancy related defects become the main acceptor. A new band of around 665 meV is observed in the GaSb sample with the lowest Te-doping concentration. The variation of the acceptor defects and their influence on the electronic and optical property on the n-GaSb single crystal are discussed based on the results.     

一种多传感器云融合技术的亚面表缺陷深度检测算法研究

针对未知深度的亚表面缺陷检测,各种传感器产生的特征信息难以直接应用的难题,提出了一种多传感器云融合技术的亚表面缺陷深度检测算法.首先,本文简要介绍涡流检测与超声检测两种经典无损检测模式;然后,在已知缺陷深度的样本集上建立云滴数学模型获取单一测量模式下缺陷深度的隶属度分布函数;最后,提出了一种D-S+PCR信息融合算法将不同传感器获取的隶属度分布转化为概率分布进行数据融合,以高斯拟合的方式实现亚表面缺陷深度的检测估计.实验结果显示:对比传统Bayes变换的D-S证据融合检测技术与单传感检测技术,本文提出的检测算法对亚表面缺陷深度检测具有较高的识别准确率.     

Anisotropic diffusion based denoising on X-radiography images to detect weld defects

This paper proposes a machine vision scheme for denoising, feature space gradient preserving, and detecting weld defects in noisy weld X-radiography images; particularly, for the images that are in low-contrast and contain noises. The detection of small weld defects present on noisy image is extremely difficult in non-destructive testing through machine vision. The presence of high gradient magnitude and the low intensity in the feature space of a noisy image are the main characteristics of weld defects. These characteristics can be considered to refine and obtain noise-free images for detection of weld defects. This study proposes a modified anisotropic diffusion model, which considers a local probability value of gray-level and an adaptive threshold parameter in diffusion coefficient function to adjust the implication of low edge gradient of the feature space from the noisy image. Furthermore, an entropy based stopping criterion has been introduced to terminate the diffusion process. This proposed model is compared with the existing models, and its performance is evaluated through Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Entropy (E) and Mean Structural Similarity (MSSIM) measures. Experimental results confirm the reliability of the proposed model.