辊速差对连铸连轧7075铝板显微组织、织构及力学性能的影响

研究辊速差对连铸连轧7075铝板显微组织、织构及力学性能的影响。采用3种不同上辊/下辊转速比(ω/ω0,ω为上辊转速,ω0为下辊转速)1:1、1:1.2及1:1.4进行多次试验。结果显示,在最大辊速差条件下(ω/ω0=1:1.4),7075铝板在轧制方向的屈服强度和极限抗拉强度分别提高41.5%和21.9%。此外,当辊速比ω/ω0为1:1.4时,成品轧制板的平均晶粒尺寸减小36%,横剖面平均硬度增加约9.2%。织构研究结果显示,辊速差越大,成品各向同性及硬度越大。然而,采用不同辊速度的连铸连轧会导致变形板伸长率降低约6%。     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

基于滚动摩擦作用下玉米颗粒在模拟筒仓卸料中的力链分析（网络首发、推荐阅读）

针对颗粒滚动摩擦作用对筒仓中玉米颗粒的力链空间分布进行研究，通过EDEM离散元软件建立筒仓模型与仿真玉米颗粒模型进行卸粮仿真模拟，并与筒仓卸料实验作流态对比，验证模型与仿真结果的准确性。通过对模拟仓进行切片观察和数据处理，对比分析了不同摩擦情况下力链的细观参数随时间演化规律。模拟结果表明：颗粒间摩擦系数越大，卸粮完成的最终时间越长；颗粒间滚动摩擦系数越小，颗粒由整体流转变为管状流的时间越早。对于有漏斗的筒仓来说，减小颗粒间摩擦会改变整体流和管状流之间的极限，从而增加产生管状流的面积。标准滚动摩擦系数下玉米颗粒在卸料过程中会出现起拱-塌陷效应；减小滚动摩擦，玉米颗粒卸料较稳定，未出现起拱的应力突增、以及拱塌陷的应力衰减；增大颗粒间滚动摩擦不但会增加拱效应，且出现成拱高度距离漏斗口更高。     

离线式带钢双面翻转检查站方案设计及实践

针对某酸轧机组检查站穿带困难、检查效率低、产生废品多、故障率高的问题,开发了一种离线式带钢双面检查站。该检查站新增了带钢翻转装置,将在入口分切剪处完成定尺剪切的带钢试样由两侧压板夹紧,中间可升降皮带机落下,翻转装置将带钢试样张紧,并翻转90°或者180°,实现带钢的双面检查。皮带机两侧布置有可升降、可横移的活动操作平台,检测人员站在平台上对带钢进行打磨检查,可同时对带钢上下表面进行检测。由于缩短了带钢的传输距离,有效减少了其他擦划伤的影响,提高了带钢表面质量检测准确性。     

Effect of spheroidized microstructure on the impact toughness and electrochemical performance of a high-carbon steel

The influence of cementite spheroidization on the impact toughness and electrochemical properties of a high-carbon steel has been thoroughly investigated in this study. Heavy warm rolling, followed by 2 h of annealing, has resulted in near-complete spheroidization, leading to a microstructure consisting of nano-cementite globules dispersed in the ultrafine-grained ferritic matrix. The Charpy impact test exhibited superior impact toughness with increased spheroidization. It is validated by the presence of abundant dimples in the fractographs of spheroidized specimens, in contrast to the as-received one that experienced a brittle failure due to its lamellar pearlitic structure. Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) carried out in a 3.5% NaCl solution revealed that the corrosion resistance of the alloy gets improved with the increase in the degree of spheroidization. This is attributed to the lower susceptibility of the spheroidized specimen to microgalvanic corrosion owing to the minimum area of contact between nano-spheroidized cementite and ferrite, as elucidated with the help of EIS results aided by equivalent electrical circuit model.     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

基于改进耦合增强随机共振的滚动轴承故障诊断

针对强背景噪声下经典随机共振方法对滚动轴承故障特征提取效果差的问题,提出了一种基于改进耦合增强随机共振的滚动轴承故障诊断方法。首先,利用一个定参双稳系统和一个变参双稳系统构成耦合随机共振系统,外部输入直接作用于定参双稳系统;然后,通过调节变参双稳系统参数和耦合系数实现耦合系统的随机共振控制,并借助遗传算法实现控制参数的自适应选取。实验和工程应用验证了所提方法的有效性和优越性。     

An improved method for the hot strip mill production scheduling problem

In most research on the hot strip mill production scheduling problem (HSMPSP) arising in the steel industry, it is accepted that a schedule with lower penalty caused by jumps of width, hardness, and gauge will result in lower roller wear, so it is regarded as a better schedule. However, based on the analysis of production processes, it is realised that rolling each coil also cause roller wear. In order to assessing the roller wear associated with production scheduling more precisely, it is necessary to consider it as another factor besides those jumps, especially when complicated constraints are involved. In this paper, an improved method is proposed to quantify the expected wear of the rollers done by those jumps and rolling processes. Then the HSMPSP whose objective is to maximise the total length of all scheduled coils is formulated as a team orienteering problem with time windows and additional production constraints. A heuristic method combining an improved Ant Colony Extended algorithm with local search procedures dedicated to HSMPSP is developed. Finally, computational results on instances generated based on production data from an integrated steel mill in China indicate that the proposed algorithm is a promising solution specific to HSMPSP.