基于DSP的等精度测速方法

传统的测速方法由于受测量方式手和实现手段的限制,使得测速精度随速度而变化。本文以一个实际应用为例,详细介绍等精度测速的原理及用ＤＳＰ实现的方法和技巧。     

微小尺寸精度图像处理检测系统的研究

本文提出图像处理与分析的新方法对微小零件的尺寸及各种误差进行测量,阐明了采用此方法的检测系统的工作原理、硬件构成、软件流程及系统标定的意义和方法,并论证了检测系统在实际检测中具有高效、准确、实用的特点.     

加工中心精度可靠性分析

五轴车铣复合加工中心是军工、航天、汽车、医疗机械等机械制造领域中重要的高档数控机床。通过分析五轴车铣复合加工中心的结构,基于D-H方法,建立了运动学方程。基于广义的工作空间,结合精度可靠性,提出了精度工作空间的概念,给出了精度工作空间的定义,并基于蒙特卡洛方法分析了车铣复合加工中心的精度工作空间。不同运动参数对加工中心精度可靠性的影响是不同的,为了分析各运动参数对精度可靠性的影响程度,建立了机构精度可靠性数学模型,应用解析法进行了精度可靠性灵敏度分析,得出运动可靠度对各运动参数误差均值和方差的灵敏度。     

数控车床精度分析及提高精度保持性措施

对数控车床的精度进行了分析,找出了数控车床误差产生的原因,从设计、制造和装配各个环节分别阐述了提高数控车床精度、精度保持性应采取的措施。     

数控车床精度分析及提高精度保持性措施

对数控车床的精度进行了分析,找出了数控车床误差产生的原因,从设计、制造和装配各个环节分别阐述了提高数控车床精度、精度保持性应采取的措施.     

6-SPS并联机器人精度综合算法

通过对 6 SPS型并联机器人位置输入输出方程微分 ,建立机器人误差模型。在此基础上 ,运用误差独立作用原理和原始误差等效作用原则 ,对并联机器人进行精度综合。该方法将并联机器人精度综合这一原本为多目标多变量的非线性最优化组合问题转化为线性问题 ,因而简单可行 ,具有一定的实用价值。     

柔顺铰链的精度特性分析

以椭圆型铰链为基础,推导了单轴柔性铰链的几何中心偏移和转角计算公式,提出了将柔性铰链几何中心转角与偏移之比为精度系数这一概念,为定量地比较或设计柔性铰链提供了依据。通过椭圆型铰链结构参数变化算例,系统地比较了柔性铰链精度系数与其结构参数的关系,结果表明圆型铰链具有相对运动精度高的特性。     

环形移动装置的精度分析与优化研究

针对环形焊缝自动焊接装置中移动装置存在的低精度、高成本等问题,将精度可靠性的评价方法应用到移动装置的精度分析与优化中。基于多体系统理论建立了移动装置的误差模型,采用蒙特卡洛法对移动装置的运动误差进行了精度分析。以精度分析中误差敏感度的分析结果为基础,建立了以移动装置制造成本最低为优化目标、以满足精度可靠性设计要求为性能约束的移动装置精度分配优化模型,利用多岛遗传算法与蒙特卡洛法相结合对移动装置的误差源进行了精度分配优化设计。研究结果表明,移动装置经过优化后,精度可靠性达到设计要求,且与经验设计相比,制造成本降低19.1%,为环形移动装置的加工制造提供了依据。     

粒子图像测速(PIV)技术的发展

在流场显示测量技术中，粒子图像测速(PIV)技术占有相当重要的地位，本文重点介绍了PIV的测试原理及应用要求，并以TSI公司开发的PIV系统为例对DPIV系统的构成作了概括，同时指出了PIV技术的未来发展方向。     

The development of an automated PIV image processing software—SmartPIV

Since the popularity of digital particle image velocimetry technique (DPIV), many PIV image processing algorithms have been proposed. Amongst them, fast Fourier transform (FFT) Cross Correlation, Discrete Window Offset Cross Correlation, Iterative Multigrid Cross Correlation, Iterative Image Deformation Cross Correlation and cross correlation based particle tracking methods are widely used algorithms and have been extensively studied by researchers. All of these algorithms have their advantages and disadvantages in terms of computational load and measurement accuracy. To choose a suitable algorithm, researchers not only need to understand the complex principles of these algorithms, but also need to find out their applicable flow conditions. This could greatly increase work load for PIV users who focus more on flow structure itself instead of PIV algorithms. It is therefore necessary to develop a method which can choose PIV algorithms wisely according to the input PIV images. This paper firstly reviews the development of PIV algorithm with mainly focus on analysing advantages and disadvantages of six widely used algorithms. By using both synthetic and real PIV images, comparative studies are then carried out among these algorithms. The tests give a rate for the performance of the algorithms and provide a parameter to automatically separate pattern match and particle tracking algorithms. Based on qualitative and quantitative analysis, an automated PIV image processing method—SmartPIV is proposed and tested by both synthetic and real PIV images. For all the three test cases, the SmartPIV successfully picks the most suitable algorithm and gives very promising results.     

二值化粒子成象流速测量系统研究

本文提出了数字ＰＩＶ技术中采取二值化数值采样思想，并对二值化粒子成象流速测量系统的实现及其程控光源、二值化采样、图象处理各部分工作原理作了介绍。论述了在ＣＣＤ工作原理基础上提高系统测速上限的方法。论文还介绍了利用该系统对转盘速度径向分布的测量结果及对方管内圆柱扰流流场的测量结果，并给出了流场速度矢量分布图。     

Enhancing particle image tracking performance with a sequential Monte Carlo method: The bootstrap filter

One of the common problems of current pattern match and particle image tracking algorithms is the deployment of constant velocity assumption for particle motion between two frames, which would result in serious errors when high velocity gradient flows are measured. To address this issue, a new particle image tracking method—bootstrap filter tracking is proposed. In this new method, a simple nonlinear dynamic model which takes particle acceleration into account is employed and a sequential Monte Carlo method—bootstrap filter is used in conjunction with pattern match algorithm to strengthen the particle image tracking performance. By using the nonlinear system model and bootstrap filter, particle location at next time step can be predicted accurately and the new method is able to measure high velocity gradient flows with better performance than the traditional particle image tracking algorithms. This new method is validated by using numerically generated particle images. Its accuracy in terms of particle image density, out-of-plane displacement and displacement gradient is compared with the Kalman filter tracking (Takehara et al., 2000 [34]) and the Super-PIV (Keane et al., 1995 [30]) methods. The three algorithms are also compared by using a set of real turbulent jet images. The test results demonstrate that the bootstrap filter tracking method is superior than the Kalman filter tracking and the Super-PIV methods for measuring low density, high velocity gradient flows.     

电驱动微流体粒子图像测试系统

为了研究微米量级管道机械特性对小雷诺数流体运动传质机理的影响,建立了电驱动微流体粒子图像测试系统,系统包括倒置荧光显微镜、致冷图像传感器、微流体驱动高压电源及微流场位移矢量识别算法.通过实验测试及理论分析给出了系统的关键技术参数;通过测量示踪粒子表面电荷特性,以消除粒子电泳运动对电驱动流体PIV方法产生的测量偏差;测量了水力直径为30μm微管道中电渗流场,测量结果与理论分析的塞状流型一致;分析了布朗运动、粒子电泳运动以及相关算法误差,对粒子图像测速方法不确定度的影响.     

粒子追踪测速(PTV)技术及其在多相流测试中的应用

粒子追踪测速即PTV方法是近年来受到较多关注的一种多相流全场测试技术。本文通过文献综述总结了该方法的最新进展,涵盖了其试验装置构成、多相流图像数据的处理、粒子匹配算法的分类及特点等专题内容,着重对粒子匹配进行了讨论。最后结合工程应用实例分析了使用该方法时应注意的问题。     

Particle Image Velocimetry Measurement of the Flow Field in the Play of the Drilling Pump Valve

The failure of a drilling pump is always due to the break of the drilling pump valve, which is one of the most important but also the weakest parts of the drilling pump. Over the decades, the degradation of drilling pump valves has been investigated extensively and various failure mechanisms have been proposed. However, no experimental test on the fluid has been successfully performed to support some of these mechanisms. In this paper, tests of the flow within the valve play are carried out to investigate the factors resulting in the failure of the valve. In the tests, particle image velocimetry(PIV) technology is employed to measure the flow field distribution of the valve play in the model. From these tests, the distributions of velocity and vorticity of fluid in various valves with different valve angles and different valve lifts are obtained, from which the features of flow fields are derived and generalized. Subsequently, a general rule of the influence of valve angles and valve lifts on the flow velocity is concluded according to chart analyses of maximal velocities and mean velocities. Finally, an analysis is made on the possibility of valve failure caused by erosion and abrasion in a working valve, with the application of the failure mechanisms of drilling pump valves. PIV measurement improves the study on the failure of the drilling pump valve, and the results show good agreement with previous computational fluid dynamics(CFD) simulations.     

Measurement of particle/bubble motion and turbulence around it by hybrid PIV

Characteristics of bubble flow are influenced by bubble motion, liquid flow and interactions between bubbles, and between a bubble and liquid phase. Thus because behavior of a single bubble and liquid around it is regarded as one of the basic elements characterizing bubble flow, the single bubble motion in stagnant water was investigated experimentally by using flow visualization and image processing methods. The bubble motion is influenced by several factors, that is, bubble size, density difference between gas and liquid, bubble shape and deformation in motion. In order to separate the effect of each factor, some solid particles with different size, shape and/or density were also measured and the characteristic of each factor was discussed. Two-dimensional water velocity field and the motion of a rising particle/bubble in the water were simultaneously measured by PIV (Particle Image Velocimetry) and PTV (Particle Tracking Velocimetry), respectively (Hybrid PIV). The experimental results showed that the large density difference between a particle and water caused high relative velocity and induced zigzag motion of the particle. Furthermore, the turbulence intensity of a bubble was about twice in the case of the spherical solid particle of similar diameter.     

A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow

PIV (Particle Image Velocimetry) technique for flow field measurement has achieved popular self-identify through over ten years development, and its application range is becoming wider and wider. PIV post-processing techniques have a great influence on the success of particle-fluid two-phase flow field measurement and thus become a hot and difficult topic. In the present study, a Phase Respective Identification Algorithm (PRIA) is introduced to separate low-density solid particles or bubbles and high-density tracer particles from the PIV image of particle-fluid two-phase flow. PTV (Particle Tracking Velocimetry) technique is employed to calculate the velocity fields of low-density solid particles or bubbles. For the velocity fields of high-density solid particles or bubble phase and continuous phase traced by high-density smaller particles, based on the thought of wavelet transform and multi-resolution analysis and the theory of cross-correlation of image, a delaminated processing algorithm (MCCWM) is presented to conquer the limitation of conventional Fourier transform. The algorithm is firstly testified on synthetic two-phase flows, such as uniform steady flow, shearing flow and rotating flow, and the computational results from the simulated particle images are in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual bubble-liquid two-phase flow and jet flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images of particle-fluid two-phase flow.     

影响数字摄影测量系统精度因素的分析

对影响数字摄影测量系统精度的因素进行了分析,并提出了提高测量精度的方法.系统测量实验的结果证明了所提出方法的可行性和有效性.