车用复合材料板簧性能的有限元分析

为了初步验证所设计复合材料板簧的结构性能,利用有限元分析方法对复合材料板簧结构的静载力学和动态疲劳进行了研究。计算得到空载工况加载过程中的载荷—位移曲线,板簧刚度为373 N/mm。当两端施加载荷为64.7 kN时,板簧达到最大压缩应力。结合复合材料的S—N曲线对板簧寿命进行了估算,循环次数最低处位于板簧下表面靠近卷耳部分,循环次数为33万次。对比复合材料板簧的设计刚度和疲劳寿命,与有限元分析处理方法计算所得结果较为接近,该板簧结构基本符合使用要求。     

我国钻井装备现状及其相关配套技术

结合目前国外钻机现状以及国际石油钻井市场对钻机的配套要求,分析指出我国目前陆地钻机存在的不足与差距,对我国钻机及其配套技术的发展提出建议。     

A practical reliability assessment approach and its application for pile-stabilized slopes using FORM and support vector machine

Bulletin of Engineering Geology and the Environment - Many uncertainties exist in pile-stabilized slopes which make their design substantially complicated. In this paper, a first-order reliability...     

500 kV 开关弹簧机构支架螺栓断裂的原因分析及处理

HPL500B2型弹簧机构支架(BA)螺栓断裂可能导致开关分合闸失败,对其原因进行分析,发现BA螺栓由于预紧固力矩过大,接近其扭断力矩,使得作用在BA螺栓上的力偏高,BA螺栓安全系数仅为12%,安全系数较低。列出BA螺栓更换步骤及注意事项,并详细介绍分闸、合闸弹簧调整步骤,为类似事件的处理提供参考。     

高瓦斯突出矿井底抽巷一巷多用技术研究与实践

胡底煤业为高瓦斯高突出高矿压的三高矿井。对高瓦斯高突出的治理，该矿采用“地面网络预抽、区域模块降量、底抽穿层保掘、高位顺层保采”的四级模式，施工底抽巷，实现“底抽穿层消突、区域模块降量、异巷探测增效、多巷通风治患、循环排矸减压”等“一巷多用”。该4种方法已在1303（上）、1305（上）工作面应用并推广，取得了良好的效果，大大提高了掘进效率且满足抽放、探放水、通风需要；回填矸石初步设计现已经完善，待下一步掘进将实现填矸作用。     

浅谈压延铜箔光面色差的成因

针对压延铜箔在表面处理过程中光面易出现色差的问题,从设备、工艺、人员操作等方面进行成因探讨,并给出了应对措施。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.