船板钢CCT曲线的测定与分析

利用Gleeble3500热模拟试验机对船板用钢进行模拟试验,采集温度-膨胀量曲线,并结合金相法、硬度法绘制出试验材料的CCT曲线。分析CCT图和试验钢显微组织照片,得出不同冷却速度下该钢的组织转变情况。随着冷却速度的逐渐增大,试验钢的组织由多边形铁素体、准多边形铁素体逐步向粒状贝氏体、贝氏体铁素体的转变,同时显微硬度也随冷速的升高呈明显的上升趋势。     

硼微合金化中碳冷镦钢10B33的连续冷却转变行为

采用膨胀法结合金相-硬度分析法测定了10B33钢的连续冷却转变（CCT）曲线,研究了连续冷却过程中冷却速率对10B33钢组织和硬度的影响。结果表明：10B33钢在较宽的冷却速率范围内（0.1～20 ℃/s）可获得铁素体和珠光体组织,但随着冷速的增加,珠光体含量逐渐增加,且珠光体形貌由粗片状逐渐演变为层间距小的屈氏体。当冷速达到30 ℃/s时,转变为马氏体、部分屈氏体和极少量铁素体。整个冷速范围未发现明显贝氏体。随着冷却速率的增加,硬度逐渐提高。通过CCT曲线分析,制定了10B33钢的优化冷却工艺,得到了利于冷镦变形的理想铁素体和珠光体组织。     

冷却速度对E690海洋平台用钢组织与性能的影响

采用热膨胀法在Gleeble-3800热模拟机上测定了E690海洋平台用钢的相变临界点。同时测定了过冷奥氏体在不同冷却速度下连续转变时的膨胀曲线,绘制了其静态CCT曲线。结合金相-显微硬度法,分析了不同冷却速度对E690海洋平台用钢组织性能的影响。结果表明：冷却速度为0.05到0.5 ℃/s时,冷却转变的产物为粒状贝氏体;当冷速增加,达到0.1~3 ℃/s后,板条状贝氏体明显增多;冷却速度超过5 ℃/s后,冷却产物为板条贝氏体和板条马氏体。     

松辽盆地红岗北地区扶余油层储层单砂体分布模式

针对松辽盆地红岗北地区扶余油层岩性油藏储层单砂体分布特征认识不清和岩性圈闭预测困难等问题,利用8 口井的岩心资料、226 个岩心样品分析测试结果和386 口井的测井资料等,研究了储层单砂体成因类型、接触关系和分布模式。结果表明,扶余油层的储层单砂体类型主要为分流河道、水下分流河道和河口坝砂体。平面上,多支分流河道或水下分流河道砂体及其控制的河口坝砂体呈密集的窄带状分布特征;垂向上,储层单砂体分布受控于沉积物供给量与垂向沉积演化特征,呈现“南北分异”和“垂向分段”特征。平面多支、垂向多层的分流河道砂体的相互叠置,造成了研究区扶余油层储层砂体分布复杂多变的特征。扶余油层不同分流河道或水下分流河道砂体之间的接触关系可分为叠置式、切叠式、迁移式和孤立式等4 种类型。分流河道或水下分流河道砂体与溢岸薄层砂体或河口坝砂体之间多为突变接触关系;多期溢岸薄层砂体或河口坝砂体常呈“堆砌”叠置关系。以上认识可为红岗北地区扶余油层岩性圈闭预测和油水分布规律研究提供一定的依据。     

分析参数相关和时序特征的飞行动作识别方法

利用飞行训练过程中记录的飞行参数数据,可以识别飞行员操作的飞行动作类型。飞行参数数据的各参数子序列具有时序性、相关性、各参数量纲不同、数据量大的特点,所以该方法考虑利用主成分分析提取其参数相关度统计特征,使用欧氏距离判别分析进行粗分类;然后提取相关度较大参数数据的时序趋势变化特征,利用动态时间弯曲距离匹配细分类,进一步识别动作区段归属的飞行动作类型。该方法能够在保证识别率的前提下,提高飞行动作识别的效率。     

一种误差可控的地图边界线的等距线计算方法

在边境地区无线电台站审查、协调需要较高精度地理数据的背景下,提出了一种基于距离场的地图边界线的等距线计算方法。该方法使用规则栅格点构建距离场,通过对误差来源的分析,得到了用栅格点间距表示的误差上界。实际计算时,只需设定一个允许的最大误差值,即可自动完成对指定边界线的计算,得到多边形表示的所需精度的等距线。实验表明基于距离场的方法是可行的并且具有可控的精度,计算的等距线已应用到边境台站的技术审查中。     

基于主曲线方法的水轮机特性曲线的数值拟合

主曲线是用一维光滑曲线对数据集合进行信息抽取的一种方法,是第一主成分的非线性推广.通过分析传统回归方法在水轮机特性曲线数值拟合中存在的问题,提出了利用主曲线作为数学工具对水轮机特性曲线进行数值拟合.理论分析与实践表明,利用主曲线方法进行水轮机特性曲线的数值拟合,不仅拟合精度可以得到保证,而且对各种曲线可以用以投影指标为自变量的统一的数学表达式进行描述.该方法为水轮机特性曲线的数值拟合提供了一个新途径.     

Hot-working characteristics of C and V HSLA steels

The 0.045% V HSLA and the C steel were deformed in torsion over the range 850–1150°C and 0.1–30 s^?1. Flow stress, temperature and strain rate were related by the combination of the Arrhenius function with either the exponential or sinh functions. Based on peak stress from the sinh equation and a linear peak strain dependence, an equation is proposed for calculating flow curves. The constants in the exponential law were evaluated at strain intervals of 0.05 up to ε = 0.5, so that flow curves and mean pass stresses could be estimated for any rolling condition. Calculated and measured roll separating forces were compared for a nine-pass schedule. Ferrite grain size increased linearly with rising T at constant ε and ε. In multistage, isothermal tests after pass strains of 0.3, the fractional softening for delay times of 1, 10 and 30 s were determined. In mill simulations of nine passes with T declining from 1270 to 750°C, the pass flow curves and the finishing grain sizes were determined. The behaviours of the steels are contrasted and also compared with those of other HSLA steels.     

Learning curve modelling of work assignment in mass customized assembly lines

In customized markets, a large variety of product models is demanded by customers. That requires fast set-up of production resources to comply with specifications of the next model to be produced. Such compliance, however, may cause considerable production and quality losses owing to workers’ poor performance during the initial production runs of a new model. Therefore, modelling workers’ learning upon exposure to each product model may help production managers to define the best assignment scheme for models and workers, such that losses in the initial stages of production are minimized. The current paper presents a method that uses learning curves to guide the best assignment of product models to teams of workers. Product models are first clustered into families based on their similarities, aiming at a smaller data collection to generate the learning curves. Allocation of product families to teams is then carried out, based on the analysis of their corresponding learning curves. Two courses of action are proposed for this, depending on whether the production batch will lead to longer or shorter production runs. The proposed methodology is illustrated in a case study from the shoe manufacturing industry.