A Multilevel Design Method of Large-scale Machine System Oriented Network Environment

The design of large-scale machine system is a very complex problem.These design problems usually have a lot of design variables and constraints so that they are difficult to be solved rapidly and efficiently by using conventional methods.In this paper,a new multilevel design method oriented network environment is proposed,which maps the design problem of large-scale machine system into a hypergraph with degree of linking strength (DLS) between vertices.By decomposition of hypergraph,this method can divide the complex design problem into some small and simple subproblems that can be solved concurrently in a network.

Series Design of Large-Scale NC Machine Tool

Product system design is a mature concept in western developed countries. It has been applied in war industry during the last century. However,up until now,functional combination is still the main method for product system de-sign in China. Therefore,in terms of a concept of product generation and product interaction we are in a weak position compared with the requirements of global markets. Today,the idea of serial product design has attracted much attention in the design field and the definition of product generation as well as its parameters has already become the standard in serial product designs. Although the design of a large-scale NC machine tool is complicated,it can be further optimized by the precise exercise of object design by placing the concept of platform establishment firmly into serial product de-sign. The essence of a serial product design has been demonstrated by the design process of a large-scale NC machine tool.

Sintering and Machinability of Monazite-Type CePO_4 Ceramics

The sintering and machinability of monazite-type CePO_4 ceramics were investigated. Relative density ≥98% and apparent porosity <2% were achieved when the monazite-type CePO_4 were sintered at 1500 ℃/1 h in air,and the maximal bending strength value (184 MPa) was achieved at this temperature. CePO_4 ceramics has a multilayer structure and an exciting 'ductility',so it can be drilled and cut with WC cutter with a small machining damage.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

Sintering and Microstructure of LaPO_4 Ceramic

To provide preliminary information for design of rare earth phosphate-contained machinable ceramic, sintering and microstructure of LaPO_4 were investigated. The results show that LaPO_4 can be sintered independently without other components from 1580 to 1620 ℃, and its grains are ellipsoidal or orbicular in surface but multilayer in the inside. The fracture of LaPO_4 ceramic presents transgranular along the larger grains and along-granular for the smaller grains. It is supposed that multi-layer structural LaPO_4 may contribute to machinabilities for those LaPO_4-contained ceramic duo to its low cleavage energy, which provides a easy path for cracks propagate of material removing, also leads crack deflections, branching and blunting helping to prevent macroscopic fractures from propagation beyond the local machining area.

PHLOGOPITE GLASS CERAMIC BY POWDER SINTERING PROCESS

Phlogopite glass ceramics can be made by powder sintering technology. This paper now studies the factors which affect properties of the sintered phlogopite glass ceramic by X-ray diffraction in qualitative and quantitative way, and discusses the method improved the machinable properties of phlogopite glass ceramic. (Author abstract)

Energy Saving Technology for Lowering Air Leakage of Sintering Pallets and Dust Collectors in Sinter Plant

The hot-wire type anemometers were used for measuring the velocity of effective air flowing through sinter bed in this study.Meanwhile,microphones were installed beside the pathway and close to the outer sidewall of travelling pallets for monitoring sound pressure generated by an abnormal air leakage.For identifying the passing pallet,a thermal-resistant type RFID technology was adopted.Based on the measured data via anemometers,the air leakage rate of sintering machine was calculated with the mass balance method,and pallets with the abnormal leakage can be detected and ranked in the severity of leakage from the measured sound pressure with the relevant criteria.In addition,for examining the leakage situation,this study set up a capillary type of differential pressure gauge to double cone valve(DCV)below the electrostatic precipitator(EP)in sintering plant for collecting the larger dust.The criteria of determining leaked DCV and the patterns for replacing the DCV were proposed to develop a detecting and predicting system on the air leakage into dust collectors of sinter machine.It offered field staff a basis of maintaining or renewing DCV via a warning reminding and reducing air leakage to increase EP efficiency for avoiding the dust emission from the stack.These technologies had been implemented in the sintering plants of China Steel Corporation,and they can effectively reduce the air leakage rate by5%at least and further decrease the electricity consumption of the suction fan and coke rate,increase the production for the sintering machine.

机制砂高韧性混凝土加固普通烧结砖墙抗震性能研究

通过对未加固砖墙、钢筋网水泥砂浆单面加固砖墙以及机制砂高韧性混凝土(MSHDC)单、双面加固砖墙进行低周往复试验,分析了试件的破坏形态、滞回性能、耗能机理。结果表明,MSHDC加固面层可以有效改善墙体破坏形态,相较于未加固和钢筋网水泥砂浆加固试件的突然脆性破坏,MSHDC面层加固试件的裂缝开展缓慢,墙体的最终损伤较小。MSHDC加固面层可以有效提高墙体抗震性能,相较于未加固试件,钢筋网水泥砂浆单面加固试件、MSHDC单面加固试件和MSHDC双面加固试件的峰值荷载分别提高227%、95%、368%,峰值位移分别提高118%、30%、156%,耗能分别提高362%、139%、434%。

Simulation and Prediction of Alkalinity in Sintering Process Based on Grey Least Squares Support Vector Machine

The prediction of the alkalinity is difficult during the sintering process. Whether or not the level of the alkalinity of sintering process is successful is directly related to the quality of sinter. There is no very good method for predicting the alkalinity by now owing to the high complexity, high nonlinearity, strong coupling, high time delay, and etc. Therefore, a new technique, the grey squares support machine, was introduced. The grey support vector machine model of the alkalinity enabled the development of new equation and algorithm to predict the alkalinity. During modelling, the fluctuation of data sequence was weakened by the grey theory and the support vector machine was capable of processing nonlinear adaptable information, and the grey support vector machine has a combination of those advantages. The results revealed that the alkalinity of sinter could be accurately predicted using this model by reference to small sample and information. The experimental results showed that the grey support vector machine model was effective and practical owing to the advantages of high precision, less samples required, and simple calculation.

An Overview of Stochastic Quasi-Newton Methods for Large-Scale Machine Learning

Numerous intriguing optimization problems arise as a result of the advancement of machine learning.The stochastic first-ordermethod is the predominant choicefor those problems due to its high efficiency.However,the negative effects of noisy gradient estimates and high nonlinearity of the loss function result in a slow convergence rate.Second-order algorithms have their typical advantages in dealing with highly nonlinear and ill-conditioning problems.This paper provides a review on recent developments in stochastic variants of quasi-Newton methods,which construct the Hessian approximations using only gradient information.We concentrate on BFGS-based methods in stochastic settings and highlight the algorithmic improvements that enable the algorithm to work in various scenarios.Future research on stochastic quasi-Newton methods should focus on enhancing its applicability,lowering the computational and storage costs,and improving the convergence rate.

Joining and machining of(ZrB2-SiC)and(Cf-SiC)based composites

Filler materials of(ZrB_2-SiC-B_4C-YAG) composite were developed for gas tungsten arc welding(GTAW) of the ZrB_2-SiC and Cf-SiC based composites to themselves and to each other. Reaction with filler material,porosity and cracks were not observed at weld interfaces of all the joints. Penetration of filler material in to voids and pores existing in the Cf-SiC composites was observed. Average shear strength of 25.7 MPa was achieved for joints of Cf-SiC composites. By incorporation of Cf-SiC(CVD) ground short fibre reinforcement the(ZrB_2-SiC-B_4C-YAG) composite was machinable with tungsten carbide tool. The joint and machined composites were resistance to oxidation and thermal shock when exposed to the oxy-propane flame at 2300℃ for 300s. The combination of(ZrB_2-SiC-B_4C-YAG) and Cf-SiC based composites can be used for making parts like thermal protection system or nozzles for high temperature applications.

A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning

We performed large-scale molecular simulation to screen and identify metal-organic framework materials for gaseous iodine capture,as part of our ongoing effort in addressing management and handling issues of various radionuclides in the grand scheme of spent nuclear fuel reprocessing.Starting from the computation-ready experimental(CoRE)metal-organic frameworks(MOFs)database,grand canonical Monte Carlo simulation was employed to predict the iodine uptake values of the MOFs.A ranking list of MOFs based on their iodine uptake capabilities was generated,with the Top 10 candidates identified and their respective adsorption sites visualized.Subsequently,machine learning was used to establish structure-property relationships to correlate MOFs’various structural and chemical features with their corresponding performances in iodine capture,yielding interpretable common features and design rules for viable MOF adsorbents.The research strategy and framework of the present study could aid the development of high-performing MOF adsorbents for capture and recovery of radioactive iodine,and moreover,other volatile environmentally hazardous species.

Intelligent Forecasting of Sintered Ore’s Chemical Components Based on SVM

Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore.In order to control complicated chemical components in the manufacturing process of sintered ore,some key techniques for intelligent forecasting of the chemical components of sintered ore are studied in this paper.A new intelligent forecasting system based on SVM is proposed and realized.The results show that the accuracy of predictive value of every component is more than 90%.The application of our system in related companies is for more than one year and has shown satisfactory results.

基于机器视觉的烧结矿FeO含量在线感知

烧结矿FeO的含量是烧结生产的一项综合性指标,影响它的因素较多而且各因素间呈现一种非线性关系,导致对FeO含量的预测难度较大。针对烧结矿FeO含量难以预测的问题,本文提出一种基于机器视觉技术实现烧结矿FeO含量在线感知的系统。该系统通过在烧结台车机尾安装红外热成像设备来获取烧结断面的热成像图片信息并对图片信息特征进行提取和分析,提取的机尾断面图像特征作为Darknet-19算法的输入参数,建立基于改进的Darknet-19算法的烧结矿FeO含量预测模型,实现对烧结矿FeO含量的实时预测。现场实际使用表明,烧结矿FeO含量预测模型的预测值与实际值偏差±0.5时,准确率在82.5%,对稳定和优化烧结生产过程控制有积极作用。

基于CSO-LSSVM模型的选择性激光烧结成型工艺参数优化

成型收缩是影响选择性激光烧结技术(selective laser sintering,SLS)制件精度的关键因素,而工艺参数对材料烧结情况和收缩变形程度有着明显影响,因此选择合理的参数组合对减小精度误差和改善成型性能质量有着重要意义。为降低SLS成型件工艺参数优化试验成本,文章开发了一种名为CSO-LSSVM成型精度预测模型用于工艺参数的预测。该模型的设计思路是:首先,通过Sine映射、非线性切换因子和针孔成像反向学习等3种改进策略全方面协调增强了蛇优化器(snake optimizer,SO)的收敛精度和寻优速度,接着,将改进后的蛇优化器(chaotic multi-strategy enhanced snake optimizer,CSO)与最小二乘支持向量机(least square support vector machine,LSSVM)结合,整定关键核函数参数,提高模型预测精度和泛化能力。为验证CSO-LSSVM模型的有效性和优越性,利用Matlab软件在真实数据集基础上将其与LSSVM、BP(back propagation)神经网络以及极限学习机(extreme learning machine,ELM)模型进行对比。结果表明:文中所提方法具有更高的预测精度,其误差评价指标均方根误差、平均绝对百分比误差和平均绝对误差分别为0.5462、9.4877、0.4017。该模型可为SLS成型加工提供最优工艺参数,有效指导加工。

黏结剂喷射打印技术研究现状与发展趋势

黏结剂喷射(binder jetting,BJ)是一种将液态黏结剂喷射到粉末材料层上,选择性黏结粉末成形,随后进行致密化处理的增材制造技术。近年来,BJ技术因其高效率、低成本、适用材料范围广而受到广泛关注和研究。在BJ打印过程中,粉末特性、黏结剂及其与粉床的相互作用、打印参数等因素对生坯质量和性能有至关重要的影响。此外,烧结过程是影响最终部件质量的关键因素之一。本文总结了BJ打印的影响因素,提出可借助机器学习辅助坯体质量和烧结收缩预测,实现控形控性。目前,BJ技术正在推向汽车、医疗器械等行业。未来,BJ技术大规模应用的关键在于提高生坯质量和精度、增强黏结剂与坯体的结合强度、优化后处理工艺等方面。

烧结机台车缺陷部件在线智能检测系统设计与实现

烧结机台车是制取烧结矿的关键设备,为避免由台车上的箅条缺失、车轮锁紧螺母缺失与车轮脱落引发的生产事故,提高烧结生产效率。本文根据台车实际运行状况,在硬件层面制定了故障检测方案,构建了基于YOLOv7与DeepStream的烧结机台车缺陷部件检测系统。系统选取YOLOv7网络模型在缺陷部件数据集上训练,将YOLOv7模型训练所得权重文件部署在DeepStream6.1平台进行加速推理,并采用Kafka消息组件推送推断结果。试验结果表明,YOLOv7对所有类别检测的平均准确率为0.991,可用于缺陷部件的目标检测。系统实时监测烧结机台车运行情况,对Kafka消息进行解析,实现故障判定规则,通过实时的故障判定、存储、显示及预警实现烧结机台车缺陷部件智能化检测,为烧结机台车缺陷部件检修提供了一种新的解决方案。

烧结机头灰浸出提取高纯度氯化钾的研究

为了实现烧结机头灰中的氯化钾高附加值利用,以某钢铁企业的烧结机头电除尘灰和高盐碱性洗脱水为原料,采用预处理-高盐废水协同水洗脱钾-真空抽滤-除杂与脱色-固液分离-分盐结晶的工艺路线,设计了BoxBehnken试验,建立浸出温度、液固比和搅拌速率3个因素与烧结机头灰钾脱除率之间的数学模型,采用响应曲面(Box-Behnken)法对烧结机头灰钾脱除率工艺进行优化,并通过氯化钠和氯化钾在不同温度下的溶解度大小进行分盐结晶。实验结果表明:响应曲面分析得出3个因素对烧结机头灰中钾的脱除率的影响程度由大到小依次为浸出温度、液固质量比、搅拌速率;浸出过程最佳的工艺条件是浸出温度为51.45℃、液固质量比为2.85∶1、搅拌速率为674 r/min,30 min时钾的脱除率为93.89%;含钾滤液经过除杂脱色处理后,利用NaCl-KCl-H_(2)O水盐体系相图,控制蒸发条件进行分盐结晶,使闪发终点控制在氯化钾单固相结晶区内,可得到纯度为92%氯化钾产品。

钢铁含铊高盐固废/废水协同水洗技术研究

钢铁烧结工序会产生大量烧结机头灰和脱硫废水,盐含量高,且含有剧毒重金属铊,其消纳问题成为制约钢铁行业可持续发展的关键问题之一。提出含铊高盐固废与高盐废水协同处置的思路。结果表明,当利用1/4浓度活性炭法脱硫废水用于洗灰时,洗灰水中铊含量低于5 mg/L,且钙、镁的溶出量也有所降低。重点研究了活性炭法脱硫废水中各组分对烧结机头灰水洗过程铊溶出行为的影响,发现水洗过程SO_(3)^(2-)的存在及适宜的pH对铊的溶出有明显抑制作用。