金属增材制造技术研究热点聚类分析

为深入分析金属增材制造技术分类和发展状况,采集中国期刊全文数据库(CNKI)收录的核心期刊上的768篇科技文献,借助文献分析可视化软件CiteSpace,对关键词聚类进行了全景式描绘,构建金属增材制造技术知识图谱,揭示该技术研究分类以及演化趋势。     

改进的多层感知机在客户流失预测中的应用

针对在传统的客户流失预测数据预处理中，使用one-hot编码处理离散属性导致数据维度增加及数据过于稀疏的问题，提出了两种基于多层感知机的改进后的客户流失预测模型。其主要思想是分别使用堆叠自编码器和实体嵌入两种方法对多层感知机进行改进，通过将离散属性的高维编码数据向低维空间映射，有效地减少了one-hot编码产生的稀疏数据，增加了离散属性值之间的关联度。在对两份公开的数据集进行交叉验证后的实验结果表明，改进后的模型既有效地提高了预测的准确度，又维持了传统多层感知机模型在并行化计算方面的优势。     

低活化马氏体钢热变形行为及机理型本构建模研究

利用Gleeble-3500热模拟试验机研究了低活化马氏体钢在变形温度为850～950 ℃、应变速率为0.001～1 s^-1条件下的热变形行为。建立了流变应力本构方程，并评估了该方程的预测能力。绘制了低活化马氏体钢在不同应变下的热加工图。结果表明：在较高的应变速率条件下，该材料主要发生动态回复，在较高变形温度和较低应变速率下具有明显的动态再结晶特征；本构方程的预测结果与实验结果符合良好；变形温度870～930 ℃、应变速率0.001～0.01 s^-1和变形温度920～950 ℃、应变速率0.3～1 s^-1分别是真应变为0.4和0.6下最优的热加工区域。     

管状带式输送机在线纠扭托辊的研究与应用

管状带式输送机是一种新型环保的散状物料连续输送设备,在运行过程中受各种因素影响,头、尾过渡段经常会跑偏,管状段经常会扭转,常规停机调偏纠扭方法已不能胜任复杂路线长距离管带机稳定运行的要求。为适应市场发展趋势,设计人员分析了管带机扭转原因,结合纠扭基本原理及常规纠扭方法,新开发出一种在线(不停机)纠扭托辊并投入项目应用。     

Propagation and termination of an internal crack in continuously cast austenitic stainless steel analyzed by ∑ values and the Schmid factor

The formation mechanism of an internal crack was clarified from the viewpoint of the crystallography and thermal expansion. An inverse pole figure map obtained by EBSD pattern showed that the crack propagated along the grain boundaries having high ∑ values within the columnar zone. After the crack initiation, these positions were considered to undergo cracking followed by propagation toward the equiaxed side. Near the termination position, the grains ahead of crack propagation had a Schmid factor higher than 0.45 consuming elastic strain energy. Thermal expansion measurements showed that the grain with (0 0 1) orientation had the largest expansion while that with (0 1 1) the smallest. The grain boundaries neighboring the combination of (0 0 1) and (0 1 1) grains had the largest thermal stress. Therefore, thermal stress contributed to the initiation of cracking. It was thus proposed to enlarge the equiaxed zone to prevent cracking by discontinuing the crack propagation.     

小尺度交通信号灯的检测与状态识别

交通信号灯识别包括检测和状态识别，在智能交通系统中发挥重要作用。基于YOLOv3算法提出了一种交通信号灯检测与状态识别模型。针对交通信号灯相较于交通场景中其他目标具有尺度小的特性进行了算法的设计：降低骨干网络的下采样倍率以增加小尺度目标的特征描述能力；通过增大特征图的尺度来改进多尺度特征融合；引入广义交并比作为检测任务的损失函数来改进目标边界框的回归效果。同时，根据交通信号灯本身的特性，使用颜色和形状约束的方法对信号灯进行状态识别和类别验证。最后在公开的Bosch交通信号灯数据集上和实际的城区道路进行了实验验证。实验结果表明，所提出的算法能够提升交通信号灯识别的精度和召回率，识别准确率可以达到90%左右。     

Densely connected convolutional network block based autoencoder for panorama map compression

As a novel virtual reality (VR) format, panorama maps are attracting increasing attention, while the compression of panorama images is still a concern. In this paper, a densely connected convolutional network block (dense block) based autoencoder is proposed to compress panorama maps. In the proposed autoencoder, dense blocks are specially designed to reuse feature maps and reduce redundancy of features. Meanwhile, a loss function, which imports a position-dependent weight item for each pixel, is proposed to train and adjust network parameters, in order to make the autoencoder fit to properties of panorama maps. Based on the proposed autoencoder and the weighted loss function, a greedy block-wise training scheme is also designed to avoid gradient vanishing problem and speed up training. During training process, the autoencoder is divided into several sub-nets. After each sub-net is trained separately, the whole network is fine-tuned to achieve the best performance. Experimental results demonstrate that the proposed autoencoder, compared with JPEG, saves up to 79.69 % bit rates, and obtains 7.27dB gain in BD-WS-PSNR or 0.0789 gain in BD-WS-SSIM. The proposed autoencoder also outperforms JPEG 2000, HEVC and VVC in both BD-WS-PSNR and BD-WS-SSIM. Meanwhile, subjective results show that the proposed autoencoder can recover details of panorama images, and reconstruct maps with high visual quality.     

30CrNi3MoV钢的热变形行为及热加工图

采用Gleeble-3500热模拟试验机对30CrNi3MoV钢进行单向热压缩试验,研究了其在变形温度950~1150 ℃、应变速率0.01~10 s^-1的热变形行为,构建了应变补偿型流变应力本构方程,并绘制出该钢的热加工图。结果表明,30CrNi3MoV钢真应力-真应变曲线有3种不同特征:高温小应变速率时,表现为典型的动态再结晶过程;低温小应变速率时,曲线为动态回复特征;应变速率较大时,应力随应变的增大而增大,无明显的峰值应力。采用5次多项式拟合构建的应变耦合流变应力本构方程具有高的精确度,采用该方程获得的预测值与试验值的平均相对误差为3.2%,相关性系数R值为0.993。从热加工图中得到试验钢最佳的热加工工艺参数范围是:变形温度为1020~1150 ℃、应变速率为0.03~0.35 s^-1。     

A 360-Degree Panoramic Image Inpainting Network Using a Cube Map

Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it as a training network. Since the cube map format is used, the correlation of the six sides of the cube map should be considered. Therefore, all faces of the cube map are used as input for the whole discriminative network, and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image. The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.