应力时效对P110套管螺纹参数的影响

在Φ339.7mmP110高强套管研制开发中发现,螺纹加工完成后因应力时效引起管体端头直径的变化,导致螺纹参数随之变化。通过对调质处理并矫直的套管几何尺寸及螺纹参数的测量试验,测试出随时间推移钢管几何尺寸、螺纹参数的变化量,分析其变化趋势及程度。通过应力时效对钢管形体作用的机理分析,提出确保套管螺纹参数稳定的控制措施。     

周期式冷轧管机的发展

叙述了周期式冷轧管机国内外开发、研制的历史和现状。介绍了典型型号的周期式冷轧管机的技术参数,结构特点及目前应用情况。     

Influencing factors on elastic-plastic deformation of multi-layered surfaces under sliding contact

Stress distribution in the gradient multi-layered surface under a sliding contact was investigated using finite element method(FEM). The main structure parameters of layered surface discussed are total layer thickness,layer number and elastic modulus ratio of layer to the substrate. A model of multi-layered surface contact with rough slider was studied. The effect of the surface structure parameters on the elastic-plastic deformation was analyzed.     

A new high accuracy method for two-dimensional biharmonic equation with nonlinear third derivative terms: application to Navier–Stokes equations of motion

In this paper, we propose a new compact fourth-order accurate method for solving the two-dimensional fourth-order elliptic boundary value problem with third-order nonlinear derivative terms. We use only 9-point single computational cell in the scheme. The proposed method is then employed to solve Navier–Stokes equations of motion in terms of streamfunction–velocity formulation, and the lid-driven square cavity problem. We describe the derivation of the method in details and also discuss how our streamfunction–velocity formulation is able to handle boundary conditions in terms of normal derivatives. Numerical results show that the proposed method enables us to obtain oscillation-free high accuracy solution.     

应用MES技术优化分组加工工艺过程

分组加工模式的应用是特色工艺研究和精细化加工的结合,目前被应用在了很多企业中。为了解决计划制订到生产调度指挥这一过程中的问题,企业采用了MES技术来优化这一模式下的工艺过程。分析了MES技术的应用。     

宽带钢厚度模型的研究与优化

介绍莱钢宽带钢厚度模型的优化,以改善板形,提高控制精度.     

虚拟人运动中受扰的平衡保持算法

为了更加真实且实时地模拟运动中虚拟人恢复平衡的反应动作,提出一种针对受到外界作用力扰动的虚拟人平衡保持算法.首先通过虚拟人质心位移和速度来判断平衡性;然后借助生物力学的研究成果设计了具有人体特性的虚拟人运动受扰后的平衡保持方法,并用动力学进行模拟,驱动虚拟人完成平衡恢复.实验结果表明:该算法计算效率高,符合人体的生物力学特性,并且具有良好的交互性与较好的视觉效果,适用于虚拟人动画合成.     

Android IoT Lifelog System and Its Application to Motion Inference

In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users’ daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users’ daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-class ML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively.     

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified.