Analytical and experimental analysis of deep drawing process for bimetal elements

The paper presents the concepts of solving of deep drawing process for bimetal elements of sheet materials. The inertia forces, plastic hardening behaviour of the deformed material and dynamic stress are utilized in the problems of plasticity. The experimental results and general conclusion are presented at the end of the paper.     

金属板材成形中拉深损伤模拟的两种求解方法

讨论用数值模拟预测板材成形中发生拉深损伤破坏的两种不同的求解方法。第一种是采用完全耦合的弹塑性破坯模型的动力显式数值方法。基于状态参量的不可逆热力学过程 ,完全耦合的本构方程考虑各向同性强化和拉深。这些已引入有限元软件ABAQUS/E用于对金属成形的模拟。在数值技术方面 ,隐式积分方法被用来对本构方程的局部时间积分 ,动力显式技术用于求解总体平衡方程。第二种方案是采用简化的损伤模型的方法 ,被称为逆法 (InveseApprch)。I.A被限定在考虑各向同性强化和损伤的塑性全量理论基础上的简单的本构关系。做比例加载和临界损伤达到以后的损伤饱和假定 ,可以得到损伤演化方程的封闭解。这两种损伤模型在编程中的实施采用两种方式 :计算中考虑或不考虑损伤作用 ,也就是耦合的或非耦合的计算。文中给出一些算例来说明每种损伤模型的优点     

简支深梁用和函数法的级数解答

引用和函数法求解弹性力学平面问题, 对简支深梁受三角形荷载作用时的应力进行了分析,同时给出了该问题的付立叶级数解答,该方法不仅将求解弹性力学的偏微分方程组解耦成非耦联的几个偏微分方程,而且其结果与传统的Airy应力函数得到的解答完全一致, 证明了该方法的正确性和适用性,该方法拓展了将经典的弹性力学解析法用于解决工程实际问题的空间。     

基于图像分析和深度学习的复合绝缘子憎水性分级

为了更加方便快捷地检测大量复合绝缘子憎水性等级,提出一种基于图像分析和深度学习的复合绝缘子憎水性分级方法。首先为提高图像对比度,对复合绝缘子憎水性图像进行灰度化和图像增强处理;其次利用图像分析技术和U-Net网络提取水珠轮廓,得到水珠轮廓图像;接着引入深度卷积神经网络,将这些水珠轮廓图作为神经网络的输入,以相应的憎水性等级作为输出向量,训练网络得到分级模型;最后将分级模型用于憎水性分级,得到分级结果。实验结果表明:该方法的分级结果已达到实际应用要求,水珠轮廓提取的精度达到了92.96%,分级准确率达到了90.2%,预测一幅图像的憎水性等级平均耗时0.1 s。     

基于长短期记忆网络的电网故障区域定位与故障传播路径推理

为了在发生故障后维持电力系统的安全稳定,有必要实现对故障区域的快速定位并确定故障冲击的传播路径,提出基于长短期记忆网络(LSTM)的故障区域定位和故障传播路径推理方法.首先,利用LSTM建立2个故障诊断模型分别实现在线检测故障时刻和确定故障区域;然后,通过计算故障点附近线路的端口供给能量确定故障冲击的传播路径;最后,以8机36节点电网为例进行算例验证,结果表明所提模型可在发生故障后短时间内检测到故障,给出故障区域和故障冲击传播的路径,且对噪声有较强的鲁棒性.     

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L⁻¹) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2–2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.     

基于深度强化学习的光储充电站储能系统优化运行

优化储能充放电策略有利于提升光储充电站运行经济性,但是现有模型驱动的随机优化方法无法全面考虑储能系统的复杂运行特性以及光伏发电功率、电动汽车充电负荷的不确定性.因此,提出一种基于深度强化学习的光储充电站储能系统全寿命周期优化运行方法.首先对储能运行效率模型和容量衰减模型进行精细化建模.然后考虑电动汽车充电需求、光伏出力和电价的不确定性,在满足电动汽车充电需求和光伏消纳的条件下,以光储充电站收益最大化为目标,建立了基于强化学习的储能优化运行问题.考虑到储能充放电决策动作的连续性,采用双延迟深度确定性策略梯度算法进行求解.采用实际历史数据对模型进行训练,根据当前时段状态对储能充放电策略进行实时优化.最后,对所提方法及模型进行测试,并将所提出的方法与传统模型驱动方法进行对比,结果验证了所提方法及模型的有效性.     

考虑样本类别不平衡的电网故障事件智能识别方法

电网中不同设备的故障概率存在差异,影响智能诊断技术的准确性.为解决此问题,提出了一种基于代价敏感学习和模型自适应选择融合的电网故障事件智能识别方法.首先,利用Word2vec模型将预处理后的电网告警信息向量化,并搭建2个双向长短期记忆网络作为基础分类器;然后,设计代价敏感损失函数,将交叉熵损失函数与代价敏感损失函数分别应用于2个分类器中;最后,提出一种模型自适应选择融合法,融合上述分类器,得到故障事件识别结果.实际数据测试表明,所提方法能够有效降低故障事件识别中样本类别不平衡的影响.     

MICROSTRUCTURES AND TEXTURES IN COLD-ROLLED ALUMINIUM STRIP

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Effect of deep cryogenic treatment on mechanical behavior of a Cu-Cr-Zr alloy for electrodes of spot welding

The effects of deep cryogenic treatment on mechanical behavior of a Cu-Cr-Zr alloy for electrodes of spot welding were investigated employing Brinell-hardness testing unit, abrasion examination machine, electronic almighty testing machine and X-ray stress analyzer. Tensile fracture surfaces of the alloy were characterized by scanning electronic microscope （SEM） with energy dispersive X-ray spectroscopy （EDS）. The results showed that, after deep cryogenic treatment, σb and σ0.2 increased 23 MPa and 21 MPa respectively, the wear rate of the alloy exhibited the trend of decrease with the decreasing temperature and increasing time of deep cryogenic treatment, and the surface residual stress of the alloy was partially eliminated by deep cryogenic treatment.