积分中值屈服准则解析厚板轧制椭圆速度场

为解决非线性Mises比塑性功率积分困难以及由此导致的轧制功率解析式难以获得的问题,本文通过建立并利用线性比塑性功率表达式对提出的椭圆速度场进行能量分析,得到了轧制力能参数的解析解.文中通过对变角度屈服函数求积分中值,构建了一个新的屈服准则,它是主应力分量的线性组合,在π平面上的轨迹是逼近Mises圆的等边非等角的十二边形,其基于Lode参数表达式的理论结果也与实验数据吻合较好.同时,根据厚板轧制时金属流动速度从入口到出口逐渐增大的特点,提出了水平速度分量满足椭圆方程的速度场,该速度场满足运动许可条件.通过相应的轧制能量分析,获得了基于线性屈服准则的内部变形功率以及基于应变矢量内积法上的摩擦功率与剪切功率.在此之上,通过泛函的极值变分导出了轧制力矩、轧制力以及应力状态系数的解析解,并与现场实测数据进行了对比,结果表明利用本文提出的屈服准则与速度场所建立的轧制力矩与轧制力模型与实测值吻合较好,其中轧制力误差小于5.3%,轧制力矩误差在6%左右.     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.     

基于康复训练中力信号的人体参与度建模

设计一种基于人机接触力的人体主动参与程度评估模型,用于康复机器人应用中的主动康复训练。通过人体的肌电信号得到肌肉活跃度,确定肌电信号与人体参与度的关系。分析这一过程中人机接触力的变化,将腿部力信号平均值以及腰部力信号方差作为模型输入,对基于肌电信号的参与度模型进行拟合,得到基于力信号的参与度模型,使用肌电信号参与度模型对力信号参与度模型进行验证,证明了该模型的有效性。该方法避免了肌电信号采集时干扰较大且准备工作繁琐等缺点,具有良好的实用性。     

微带型Wilkinson功分器设计与实现

小型低功耗器件是射频电路设计的研究热点,而微带技术具有小型化低功耗的优点,为此在介绍微带型Wilkin-son功分器工作原理的基础上,使用基于矩量法的ADS软件设计、仿真和优化计算相关数据参数,并制作了一个微带功分器实例,最后对加工的样品进行实测,获得与仿真值吻合较好的预期结果。     

First-order modeling and stability analysis of illusory contours

In visual cognition, illusions help elucidate certain intriguing latent perceptual functions of the human vision system, and their proper mathematical modeling and computational simulation are therefore deeply beneficial to both biological and computer vision. Inspired by existent prior works, the current paper proposes a first-order energy-based model for analyzing and simulating illusory contours. The lower complexity of the proposed model facilitates rigorous mathematical analysis on the detailed geometric structures of illusory contours. After being asymptotically approximated by classical active contours, the proposed model is then robustly computed using the celebrated level-set method of Osher and Sethian [S. Osher, J.A. Sethian, Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations, J. Comput. Phys., 79 (12) (1988) 12–49] with a natural supervising scheme. Potential cognitive implications of the mathematical results are addressed, and generic computational examples are demonstrated and discussed.     

Effect of fluoropolymer antidripping agent on the microstructure and properties of polyesters

Poly(ethylene terephthalate) (PET) is of excellent mechanical properties and melt processability and is widely used as raw material for textile fibers. However, the flame retardant properties of PET were rather poor, and both reactive and additive phosphorus- and halogen-containing compounds have been employed to enhance the reaction-to-fire properties while the meltdripping behaviour during burning hasn’t been handled properly with the flame retardants. In this work, fluoropolymer was blended with both pure PET (pPET) and reactive phosphorus-containing flame retarded copolyester (fPET), and the flame retardance and char formation and mechanical properties of the resulted pPET and fPET blends were investigated. The tensile strengths of modified pPET samples were worsen whereas those of modified fPET samples were improved at low concentrations. The initial thermal degradation in nitrogen was accelerated remarkably for the two polyesters with fluoropolymer. The oxygen indices of the all modified samples were reduced while char formation and meltdripping suppression were encouraged. The apparent melt viscosity and elasticity for the two polyesters were gained much with antidripping agent. Therefore, fluoropolymer improved char formation of the two polyesters based on the gaseous phase mechanism while the partial suppression of meltdripping behavior and the decrease of mechanical properties mainly originated from the increase of melt viscosity via fibrillation for pPET.     

有机硅丙烯酸酯增深剂的制备及应用

由乙烯基三乙氧基硅烷与KH-550、丙烯酸酯类单体在阴/非离子表面活性剂存在下，通过乳液聚合制得带蓝光的半透明有机硅丙烯酸酯乳液．该乳液粘度小，有机硅质量分数15％，总固体质量分数30％，该增深剂既可单独使用，也可与氨基硅油乳液复配，经蓝色16电力纺、黑色03双绉等真丝产品增深，其增深度可达15％，但亲水性有所下降．     

基于微位移定位系统MEMS梳齿位移传感器的研究

MEMS硫齿位移传感器是一种基于MEMS技术电容理论的传感器,具有体积小、重量轻、性能稳定、功耗低和易于集成等特点.首先探讨了梳齿传感器的工作原理,随后介绍了使用梳齿传感器有效检测微动工作台位移的方法,并进行了结构失效和梳齿电极不平行失效的可靠性分析.通过采用这种传感器可以达到提高微位移定位平台系统的集成度、精度、分辨...     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.