Bulging in incremental sheet forming of cold bonded multi-layered Cu clad sheet: Influence of forming conditions and bending

Bulge is a defect that causes geometrical inaccuracy and premature failure in the innovative incremental sheet forming (ISF) process. This study has two-fold objectives: (1) knowing the bulging behavior of a Cu clad tri-layered steel sheet as a function of forming conditions, and (2) analyzing the bending effect on bulging in an attempt to identify the associated mechanism. A series of ISF tests and bending analysis are performed to realize these objectives. From the cause-effect analysis, it is found that bulge formation in the layered sheet is sensitive to forming conditions in a way that bulging can be minimized utilizing annealed material and performing ISF with larger tool diameter and step size. The bending under tension analysis reveals that the formation of bulge is an outgrowth of bending moment that the forming tool applies on the sheet during ISF. Furthermore, the magnitude of bending moment depending upon the forming conditions varies from 0.046 to 10.24 N·m/m and causes a corresponding change in the mean bulge height from 0.07 to 0.91 mm. The bending moment governs bulging in layered sheet through a linear law. These findings lead to a conclusion that the bulge defect can be overcome by controlling the bending moment and the formula proposed can be helpful in this regards.     

基于密集神经网络的灰度图像着色算法

针对在灰度图像着色领域中，传统算法信息提取率不高、着色效果不理想的问题，提出了基于密集神经网络的灰度图像着色算法，以实现改善着色效果，让人眼更好地观察图片信息的目的。利用密集神经网络的信息提取高效性，构建并训练了一个端到端的深度学习模型，对图像中的各类信息及特征进行提取。训练网络时与原图像进行对比，以逐渐减小网络输出结果的信息、分类等各类型的损失。训练完成后，只需向网络输入一张灰度图片，即可生成一张颜色饱满、鲜明逼真的彩色图片。实验结果表明，引入密集网络后，可有效改善着色过程中的漏色、细节信息损失、对比度低等问题，所提算法着色效果较基于VGG网络及U-Net、双流网络结构、残差网络（ResNet）等性能优异的先进着色算法而言取得了显著的改进。     

基于交会图和多元统计法的神经网络技术在火山岩识别中的应用

针对火山岩储层的特殊性(复杂性、离散性和随机性),应用BP神经网络技术对火山岩测井解释中岩性识别问题进行了研究。该方法的技术关键是样本集和初始权重的建立,以及模型的优选。本文提出了一种基于交会图和多元统计法的学习样本生成方法,即根据取心岩样的地球化学和岩石学研究,用交会图技术建立样本集,采用聚类分析和距离判别法确定初始权重。将研究方法应用在松辽盆地杏山地区火山岩岩性识别问题中,取得了很好的效果,岩性解释符合率高于90%。文中通过四种岩性识别处理模式的对比研究,表明赋权重处理模式为最优处理模型。在神经网络模型预测过程中,需充分利用已有的地质经验和测井曲线信息建立典型可靠的样本文件,同时考虑神经网络方法中各种因素的影响,优选模型和计算参数才能使预测结果符合实际情况。     

地统计克立格插值法在工程土方计算中的应用

土方计算是工程中经常碰到的问题。尝试利用地统计学的克立格插值法来计算土方，将其应用于余姚市泗门镇土地整理工程中进行实测试验。介绍了其基本的原理和公式，并与利用数字高程模型计算得到的土方工程量进行了比较，发现地统计插值法可较精确地确定设计标高和土方量，证明该方法的可行性和优越性。     

大坝监测自动化中的人工比测研究

阐述了人工比测的概念、方式和数据分析方法,对人工比测及数据分析中应注意的问题也进行了深入研究。     

A suitable computational strategy for the parametric analysis of problems with multiple contact

The aim of the present work is to develop an application of the LArge Time INcrement (LATIN) approach for the parametric analysis of static problems with multiple contacts. The methodology adopted was originally introduced to solve viscoplastic and large‐transformation problems. Here, the applications concern elastic, quasi‐static structural assemblies with local non‐linearities such as unilateral contact with friction. Our approach is based on a decomposition of the assembly into substructures and interfaces. The interfaces play the vital role of enabling the local non‐linearities, such as contact and friction, to be modelled easily and accurately. The problem on each substructure is solved by the finite element method and an iterative scheme based on the LATIN method is used for the global resolution. More specifically, the objective is to calculate a large number of design configurations. Each design configuration corresponds to a set of values of all the variable parameters (friction coefficients, prestress) which are introduced into the mechanical analysis. A full computation is needed for each set of parameters. Here we propose, as an alternative to carrying out these full computations, to use the capability of the LATIN method to re‐use the solution to a given problem (for one set of parameters) in order to solve similar problems (for the other sets of parameters). Copyright © 2003 John Wiley & Sons, Ltd.     

均匀设计法分析制备过程对钴钼硫化物催化剂机械强度的影响

建立了CoMoP/Al2O3加氢精制催化剂机械强度及其可靠性在制备过程中的数学模型,采用均匀设计考察了浸渍、干燥、煅烧、硫化4个制备过程中,浸渍时间、干燥温度、干燥时间、煅烧温度、煅烧时间、硫化温度和硫化升温速率7个实验因子对催化剂强度均值、Weibull模量的影响,同时考察了这些因素对颗粒密度的影响。方差分析表明所有模型都是充足的。实验结果表明,硫化过程是影响催化剂强度均值的主要过程。在实验范围内提高硫化温度、降低硫化时的升温速率有利于提高催化剂的强度。影响催化剂Weibull模量的因素主要为各个制备过程的交互效应。要提高催化剂强度的可靠性就必须全面考虑催化剂制备的各个过程,特别是各个制备过程的交互效应。     

应用SPC技术对机械零件加工过程的管理和控制

介绍了SPC(统计过程控制)技术原理，以某传动轴外径φ45 0.50^0.90 mm为控制对象，提出应用SPC技术，对机械零件加工过程进行管理和控制，从而达到过程失控预警、过程不断改进、过程能力不断提高的目的，继而保证产品质量的稳定和不断提高。