基于ADAMS的变摇臂比配气机构仿真分析

本文在ADAMS中建立了变摇臂比配气机构的刚体模型与刚柔耦合模型,并做了仿真分析比较,验证了刚柔耦合模型在变摇臂比配气机构运动分析中的可行性,为变摇臂比配气机构的设计提供了一种简捷可行的方法。     

Iterative circle fitting based on circular attracting factor

An intuitive method for circle fitting is proposed. Assuming an approximate circle(CA,n) for the fitting of some scattered points, it can be imagined that every point would apply a force to CA,n, which all together form an overall effect that "draws" CA,n towards best fitting to the group of points. The basic element of the force is called circular attracting factor(CAF) which is defined as a real scalar in a radial direction of CA,n. An iterative algorithm based on this idea is proposed, and the convergence and accuracy are analyzed. The algorithm converges uniformly which is proved by the analysis of Lyapunov function, and the accuracy of the algorithm is in accord with that of geometric least squares of circle fitting. The algorithm is adopted to circle detection in grayscale images, in which the transferring to binary images is not required, and thus the algorithm is less sensitive to lightening and background noise. The main point for the adaption is the calculation of CAF which is extended in radial directions of CA,n for the whole image. All pixels would apply forces to CA,n, and the overall effect of forces would be equivalent to a force from the centroid of pixels to CA,n. The forces from would-be edge pixels would overweigh that from noisy pixels, so the following approximate circle would be of better fitting. To reduce the amount of calculation, pixels are only used in an annular area including the boundary of CA,n just in between for the calculation of CAF. Examples are given, showing the process of circle fitting of scattered points around a circle from an initial assuming circle, comparing the fitting results for scattered points from some related literature, applying the method proposed for circular edge detection in grayscale images with noise, and/or with only partial arc of a circle, and for circle detection in BGA inspection.     

热超声倒装键合机视觉定位系统的设计与实现

通过对芯片热超声倒装键合工艺过程的研究,结合国内外热超声倒装键合设备发展的现状,研制了一台用于芯片的热超声倒装键合机。     

一种基于神经网络的基板引脚图像识别方法

基板引脚图像识别是自动引线键合的关键技术之一。文章针对引线键合过程中基板引脚图像的特点,提出了一种基于神经网络的识别方法:首先从图像中提取了由5个特征值组成的识别特征向量;然后用神经网络设计了分类器,并进行了有效的训练;最后,经过训练的神经网络分类器可对引脚图像进行有效的识别。研究结果表明:该方法具有简单、快速、有效的特点,在少量的训练情况下可以达到很好的识别效果。     

压力约束模式下热超声倒装键合的试验

在采用压力约束模式夹持倒装芯片的条件下，实现了热超声倒装键合。通过观测键合过程中的输入超声功率、工具末端和芯片的振幅变化情况，研究了压力约束模式下不同键合参数对键合过程和键合强度的影响规律。试验结果表明：在这种约束模式下，键合力和功率对键合强度具有重要的影响；键合力对金凸点的变形是决定性的；键合强度与输入的超声能量总量有关，过小和过大的超声能量都不能形成好的键合强度，高强度的键合并不需要大的超声功率输入，键合过程中能量主要耗散于工具／芯片间相对运动导致的摩擦做功，并造成了芯片和工具的磨损。     

我国性能化防火设计的实施方法

为加快我国性能化防火设计规范化发展与应用进程，根据我国建筑消防设计审核的实际流程，提出了我国性能化防火设计的基本流程和基本步骤，并对性能化防火设计人员的能力要求提出了建议。     

热超声倒装键合工具和芯片振动的频率特征

采用激光多普勒振动测量系统,监测了热超声倒装键合过程工具末端和芯片的振动速度。由工具和芯片的振动速度有效值曲线,发现了“速度分离”现象,即键合启动数毫秒后,芯片的振动速度突然下降而工具的振动速度继续增大;“速度分离”表明金凸点／焊盘界面已初步形成键合强度。研究了工具和芯片振动速度信号的频率特征,发现芯片振动信号的3倍频成分的产生时刻就是“速度分离”发生的时刻,小键合力有利于“速度分离”发生,也就是有利于初始键合强度的形成,提出了以3倍频产生时刻为临界点的变键合力加载思路。     

神经网络在D350风机故障预测中的应用

风机的故障预测关系到风机的安全运行,准确的预测可以提前知道故障发生的时间,确定预防性维护的措施与时间,保障企业的生产安全,减少经济损失,长期以来,人们对风机故障的研究仅限于对风机故障原因的差别和诊断,而对风机故障预测的研究却甚少。     

基于AMESim的液压动力转向系统特性研究

用AMESim建立了某车型液压动力转向系统的仿真模型,仿真结果表明,此液压动力转向系统的固有频率为20.75Hz,而发动机怠速激振频率为21Hz,有可能出现液压动力转向系统与发动机怠速共振。通过对液压动力转向系统进行优化从而避免了共振的发生,提升了整车的NVH性能。