High-temperature deformation behaviour and microstructural evolution of low carbon steel based on processing map

The high-temperature deformation behaviours of low carbon steel QD08 were investigated by hot compression tests over temperatures from 1000 to 1200°C and strain rates from 0.1 to 10 s^?1. The processing map was obtained by superimposition of the power dissipation and the instability maps and the regions having the lowest strain rate sensitivity added for more clarification of low and high workability regions. The results show that the security domain mainly of hot deformation with a higher powder dissipation factor and maintain a smooth variation, by the metallographic observations, the grain refinement by DRX under the stable deformation conditions. On the basis of processing map and microstructure evolution, the optimal deformation processing parameters are the hot deformation temperature range from 1070 to 1100°C, and strain rates range from 5 to 10 s^?1.     

IDENTIFYING REGIONS OF INTEREST IN SPECTRA FOR CLASSIFICATION PURPOSES

Distinguishing between separate classes of time-series data can often be simplified by using frequency-domain methods. Different states of a process can often result in different shapes and amplitudes in a spectral representation of the time series. The interpretation of the spectrum can be achieved in this case by identifying frequency regions which have a high discriminative power between the different classes, the so-called regions of interest (ROI). The discriminative power of two sequences is high if statistical or geometric parameters differ significantly between the classes. In this paper, a new approach for identifying these ROI, which makes use of image processing techniques is given. This new algorithm was developed in a research project with the aim of monitoring solenoid valves by analysing their mechanical vibration during switching on or off. Failure detection as developed in this project can be used for condition-based maintenance. However, it is anticipated that this new method can be generalised to many other similar types of classification problems.     

Interfacial characteristics of titanium coated micro-powder diamond abrasive tools fabricated by electroforming-brazing composite process

In this study, abrasive tools were fabricated by an electroforming-brazing composite process. The abrasive tools were prepared using micro-powder diamond (MPD) grits with and without titanium (Ti) coatings. The interface characteristics of the MPD diamond grits and filler alloy were investigated. The results show that the diamond grits are uniformly dispersed on the surface of the steel substrate without visible agglomeration, and the NiCr filler alloy has good wetting toward diamond grits. Compared to the uncoated diamond grits, the abrasive grains with a Ti coating have an ideal grain distribution and brazed joints, and no aggregation of Si element observed at its edge. There is a more stable carbide TiC that formed on its surface in addition to the chromium carbide. Furthermore, the coated diamond grits are not graphitized and show a better residual stress state. In the abrasive tools with Ti-coated diamond, numerous intermetallic compounds (IMCs) are formed in the brazing layer, but the formation of theγ-Ni-based solid solution is relatively slight. The IMCs increase the microhardness of the filler alloy and help improve the wear resistance of the solder layer.     

Effect of CuCe alloy addition on the microstructure and mechanical performance of brazed diamonds with NiCr alloy

This study was aimed to reduce unwanted thermal damage to diamond grains during brazing. To achieve this goal, the diamonds were brazed using NiCr alloy with the addition of CuCe alloy (i.e. NiCr composite solder). Surface morphology and interfacial microstructure of the brazed samples using NiCr composite solder was characterized. The thermally induced residual stresses of brazed diamond grains were examined by Raman spectroscopy. The static pressure strength and impact toughness of the brazed diamond grains were examined. Constitutional phases of NiCr composite solder after brazing were detected. The results show that the thermal residual stresses values of the brazed diamond grains using NiCr composite solder, in which 2 wt% and 5 wt% CuCe alloy were used respectively, were decreased by 6.4% and 9.7% respectively, meanwhile, the static pressure strength values increased by 4.9% and 13.4% respectively as well as impact toughness values increased by 4.0% and 9.2% respectively, compared with that of the brazed diamond grains using NiCr alloy. Chromium carbides (Cr₃C₂ and Cr₇C₃) were obtained at the bonding interface between diamonds and composite solder. The constitutional phases containing Ce₂Ni₇, which could be beneficial to reduce the thermal damage, were formed in the solder alloy after brazing.     

Cutting edge damage in grinding of cemented carbides micro end mills

Micro mills are widely applied in the micro manufacturing and mainly fabricated using the grinding method. Cutting edges have significant influences on the performance of micro mills such as the micro mill life and machining quality. In this paper, the cutting edge damage mechanisms in the grinding of cemented carbides micro mills are investigated. The micro end mills grinding experiments are carried out and the cutting edge maximum edge damage width and surface roughness of the end teeth flank are measured. The results show that the micro fractures and micro cracks are generated in the cutting edge following micro pits in the grinding surface. The grain size and composition of cemented carbides have significant impacts on the damage of the cutting edge. The maximum edge damage width increases with the increase of Co binder content and WC grain sizes. However, a better flank quality with less micro pits is obtained as the reduction of Co binder content and grain size of WC.     

基于自归一化神经网络的电弧故障检测方法

电弧故障是电气火灾的重要原因。低压线路发生串联电弧故障时,回路电流波形的时域特征与正常工作状态类似,采用传统的特征提取方法无法完整表达时域信号的全部数据特征,限制了电弧故障的特征表达能力,导致检测结果的误报率和漏报率较高。针对此问题,提出基于自归一化卷积神经网络的电弧故障检测方法。该方法将采集到的不同种类负载的电流时间序列按照半周期截取,然后进行归一化处理,将灰度矩阵变换生成电弧故障及正常工作的二维图像;利用卷积神经网络提取电弧故障的灰度变换特征;通过全连接层拟合计算下采样信息实现电弧故障卷积特征的识别。验证表明,所提方法对电弧故障的识别率达到99.67%,优于传统卷积神经网络,具有良好的泛化性能。     

基于非对称卷积神经网络的电弧故障检测系统

串联电弧故障是引发电气火灾的重要原因,对其有效检测能确保线路的正常运行和电气设备的可靠工作。 根据低压串 联电弧故障的检测难点,提出了基于非对称卷积神经网络的识别模型,用于适应性地提取串联电弧故障信息。 针对串联电弧故 障种类多、信息隐蔽等问题,首先利用格拉姆角差场时域数据处理方法,将负载模拟的时域信号经过极坐标变换、三角变换后映 射到二维矩阵中,以增加故障数据点的空间占有率和数据关联信息。 之后,为了不增加时间开销,同时改善模型的识别效能,使 用自适应非对称卷积、多通道离散注意力机制改进残差神经网络,作为低压线路中的串联电弧故障模型。 最后,利用容器封装 已训练好的故障识别模型,实现故障信息的快速分析。 验证表明,所提方法对串联电弧故障的识别率达到 99. 95%,具有良好的 识别效果。