坐标测量机测量端面距离的不确定度评定

以坐标测量机端面距离测量为例,全面分析测量全过程中影响测量结果不确定度的主要因素,建立坐标测量机测量端面距离的不确定度数学模型;利用蒙特卡罗数值模拟方法进行仿真,得到被测参数的测量不确定度,给出了完整的测量结果,提升了坐标测量机的应用价值。将蒙特卡罗模拟方法评定结果与测量不确定度表示指南给出方法评定的结果进行比较,可以看出应用蒙特卡罗模拟方法对于评定复杂模型测量不确定度更方便、高效。     

单目视觉测量系统不确定度的分析

基于光学测头特征点成像的单目视觉坐标测量系统是一种新兴的高精度、大尺寸、三维整体、在线测试系统,在现代工业生产中具有重要应用价值。为评价这种单目视觉测量系统的测量精度,对基于测量不确定度的评定方法进行研究。在对影响单目视觉坐标测量系统测量精度的主要因素进行深入分析研究的基础上,建立测量不确定度评定表达式,实现对测量系统测量精度的评定;通过测量试验对评定方法加以验证,试验证明测量系统的测量精度与分析结果相吻合。     

动态汽车衡测量结果的不确定度评定

本文主要介绍了在动态汽车衡的试验过程中不确定度来源,并以动态试验为例重点介绍了计算各分量的标准不确定度、合成标准不确定度以及扩展不确定度的方法.     

测量不确定度评定在三坐标测量机中的应用

介绍了三坐标测量机的工作原理,并结合测量不确定度评定在测量工作中的应用,分析影响其测量结果的因素,并对测量结果的不确定度进行了评定。     

测量不确定度评定与表示

主要介绍了测量不确定度、标准不确定度的评定方法,合成标准不确定度和扩展不确定度的评定.     

测量不确定度评定与分析

测量不确定度是在当代国际贸易和技术交流活动中,各国用于表征测量结果的质量优劣的一种先进方法,章简要描述了测量不确定度的评定与分析方法,并举例说明其应用分析过程。     

基于GPS的测量不确定度评定方法分析

本文阐述了新一代GPS不确定度理论的形成、发展,以及在实现几何产品规范设计与计量认证统一中的重要作用;基于测量不确定度贡献因素的分析,着重研究了测量不确定度的评定方法、模型及应用技术,为实现测量不确定度评定管理的规范统一奠定必要的技术基础。     

阵列式皮带秤检定测量结果不确定度评定研究

本文重点分析了阵列式皮带秤检定测量过程中的误差来源,结合应用实例基于相关数学模型对检定测量不确定度的主要来源和因素进行了系统分析,给出了实物校验过程中对造成测量不确定度的主要因素,并依此开展了计算合成不确定度和扩展不确定度的研究,进而对阵列式皮带秤检定测量结果开展了测量不确定度评定,获得了其计量精度等级.     

台砰的测量不确定度评定

本文主要介绍了不确定度的意义和来源,并以台秤为例介绍了如何计算各标准不确定度、合成不确定度以及扩展不确定度.     

测量不确定度评定研究现状及进展

介绍了测量不确定度的起源,以及国内外的发展过程,对目前国际上存在的五种测量不确定度评定模型的特点进行了对比。综述了由ISO等七个国际组织联合发布的GUM中测量不确定度评定方法的研究现状和进展。另外介绍了不同于GUM的测量不确定度评定方法和标准不确定度的灰色评定、模糊评定、信息熵评定、贝叶斯评定以及基于神经网络的间接评定等非统计评定方法。     

三坐标机测量齿轮齿廓的不确定度评价

介绍了坐标测量中几种常用的不确定度评价方法.指出传统的三坐标测量机的测量不确定度评价方法大都不适用于评价坐标测量中面向对象的测量不确定度,并对使用蒙特卡洛方法评价测量不确定度进行了研究.首先,根据三坐标测量机详细标定文件及补偿策略说明建立测量模型.然后,将测量中的采样点通过测量模型生成大量测量结果,并以此评价测量不确定度.在齿廓评价实验中,评定齿廓误差的测量不确定度为0.96 μm时,多次评价结果之间的最大差值不超过0.03 μm,具有可靠的理论依据和较稳定的评定结果.文章指出,目前商用三坐标测量机大都不能为特定的测量对象提供测量不确定度报告,使用蒙特卡洛方法有希望改变此现状.     

Evaluation of coordinate measurement uncertainty with use of virtual machine model based on Monte Carlo method

Advances in modern manufacturing techniques implies more efficient production but also new tasks for coordinate metrology. The main of them is evaluation of accuracy of the measurement, because according to technological requirements, results of measurements are useful only when they are given with their accuracy. Currently used methods for uncertainty assessment are difficult and require knowledge and measuring experience. It is therefore important to implement correct and validated methods that will also be easy to implement and will not require broad metrological knowledge from the personnel. Presented here simulation method, based on Monte Carlo method is one of them. The article presents the conception, implementation and validation of this method.     

Experimental evaluation of software estimates of task specific measurement uncertainty for CMMs

Estimation of the uncertainty of Coordinate Measuring Machine (CMM) measurements for real, imperfect parts is a very complex undertaking. Not only are there many contributors to the uncertainty that may interact in a non-linear fashion, making it difficult to mathematically determine an uncertainty estimate, but it is also difficult to predict the long-term variation of these parameters. Our work seeks to provide experimental validation of the uncertainties predicted by two different commercial software packages that purport to predict the task-specific measuring uncertainty of CMM measurement results. The validation procedure uses repeated measurements of calibrated artifacts to experimentally determine measurement uncertainties. These measurements can then be simulated in the commercial software packages. The comparison will allow the software to be tested to see if it appropriately accounts for the influences of the operator, environment, and part placement. This paper reports the results of actual part measurements and the predicted uncertainty provided by commercial simulation packages. Differences between experimental and simulated uncertainties are highlighted, and their causes examined.     

Generalized expressions of second and third order for the evaluation of standard measurement uncertainty

The Guide to the Expression of Uncertainty in Measurement (GUM) requires the use of a first-order Taylor series expansion for propagating uncertainties. However, when the measurement function is strongly non-linear the use of this linear approximation may be inadequate and therefore higher order terms from the Taylor series cannot be neglected. The present paper aims to derive generalized expressions of second and third order for the evaluation of the estimate of a measurand and its associated standard uncertainty. A case study is given to illustrate an application of the proposed methods and the results obtained with the GUM method are compared to the corresponding ones when applying the method proposed in GUM Supplement 1.     

磁光阱异面空间角度测量及测量不确定度评定

随着空间几何量精密测量技术在装备制造工业的制造与装配过程中应用越来越广泛,角度计量正在从平面角度向空间角度发展。针对磁光阱的异面空间角度进行测量,使用平行平晶作为标准平面引出待测面法向量,并将其空间角度有效的划分成水平投影角和竖直投影角两个平面角度进行测量,保证了空间角度的可溯源性,得到磁光阱各待测面法向量间的空间角度偏差最大值为0.286 0 mrad;然后通过蒙特卡洛法评定该方法的测量不确定度为0.095 9 mrad;最后与三坐标测量机法比对结果中偏差最大值为0.184 2 mrad,在考虑待测面平面度指标为±0.145 4 mrad时,二者的一致性良好。目前该方法已在NIM5铯原子喷泉钟物理真空子系统的研制中进行了应用,证明了该方法能够满足测量的准确度要求。     

10~(-6)量级精密离心机输出加速度测量模型及不确定度评定

为了给高精度惯性仪表校准试验提供高精准的加速度输入值,研究了精密离心机输出加速度的建模、测量及不确定度评定方法。建立了适用于10-6量级高精度精密离心机的加速度测量模型及不确定度传递模型。基于本课题组提出的高精度测量方法,完成了10-6量级精密离心机的静动态半径、静动态俯仰失准角等重要分量的高精度测量。分析、归纳了测量不确定度源,分别基于建立的加速度测量不确定度传递模型和蒙特卡洛方法完成了该精密离心机输出加速度的测量不确定度评定。最后,讨论和总结了高精度精密离心机输出加速度建模和精度评定的相关问题。结果表明:该精密离心机对1g~100g输出加速度的相对标准不确定度均小于3×10-6,其精度与目前国际上公开的最高精度离心机处于同一数量级;建立的测量模型及测量不确定度评定方法可以为相关精度等级的精密离心机研制和评价提供参考。     

Adaptive Monte Carlo and GUM methods for the evaluation of measurement uncertainty of cylindricity error

Measurement uncertainty is one of the most important concepts in geometrical product specification (GPS). The “Guide to the expression of uncertainty in measurement (GUM)” is the internationally accepted master document for the evaluation of uncertainty. The GUM method (GUMM) requires the use of a first-order Taylor series expansion for propagating uncertainties. However, when the mathematical model of measurand is strongly non-linear the use of this linear approximation may be inadequate. Supplement 1 to GUM (GUM S1) has recently been proposed based on the basis of probability density functions (PDFs) using the Monte Carlo method (MCM). In order to solve the problem that the number of Monte Carlo trials needs to be selected priori, adaptive Monte Carlo method (AMCM) described in GUM S1 is recommended to control over the quality of the numerical results provided by MCM.The measurement and evaluation of cylindricity errors are essential to ensure proper assembly and good performance. In this paper, the mathematical model of cylindricity error based on the minimum zone condition is established and a quasi particle swarm optimization algorithm (QPSO) is proposed for searching the cylindricity error. Because the model is non-linear, it is necessary to verify whether GUMM is valid for the evaluation of measurement uncertainty of cylindricity error. Then, AMCM and GUMM are developed to estimate the uncertainty. The procedure of AMCM scheme and the validation of GUMM using AMCM are given in detail. Practical example is illustrated and the result shows that GUMM is not completely valid for high-precision evaluation of the measurement uncertainty of cylindricity error if only the first-order terms in the Taylor series approximation are taken into account. Compared with conventional methods, not only the proposed QPSO method can search the minimum zone cylindricity error precisely and rapidly, but also the Monte Carlo simulation is adaptive and AMCM can provide control variables (i.e. expected value, standard uncertainty and lower and higher coverage interval endpoints) with an expected numerical tolerance. The methods can be extended to the evaluation of measurement uncertainty of other form errors such as roundness and sphericity errors.     

Comparison of GUM and Monte Carlo methods for evaluating measurement uncertainty of perspiration measurement systems

Measurement uncertainty is an important parameter to express measurement results including means and reliability. The uncertainty analysis of the biomedical measurement system needs to be established. A perspiration measurement system composed of several sensors was developed. We aim to estimate the measurement uncertainty of this system with several uncertainty sources, including airflow rate, air density, and inlet and outlet absolute humidity. Measurement uncertainty was evaluated and compared by the Guide to the expression of the uncertainty in measurement (GUM) method and Monte Carlo simulation. The standard uncertainty for the perspiration measurement system was 6.81 × 10⁻⁶ kg/s and the uncertainty percentage <10%. The major source of the uncertainty was airflow rate, and inlet and outlet absolute humidity. The Monte Carlo simulation could be executed easily with available spreadsheet software programs of the Microsoft Excel. GUM and Monte Carlo simulation did not differ in measurement uncertainty with precision to two decimal places. However, the sensitivity coefficient derived by GUM provided useful information to improve measurement performance, which was not evaluated with the Monte Carlo simulation method.     

Data driven uncertainty evaluation for complex engineered system design

Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail. The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carried out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.     

Use of coordinate measuring machine to measure angles by geometric characterization of perpendicular planes. Estimating uncertainty

The purpose of this paper is to estimate the uncertainty of angle measurements using a Coordinate Measuring Machine (CMM), through the geometrical characterization of two concurrent sides of a steel angle gauge block with four perpendicular sides.For the calculation of the uncertainty associated with the measurement and investigating the errors of the CMM associated with orientation and length in the work volume, two models: linear statistical model behavior of CMM and the Mitutoyo model behavior are used and compared. After having established two behavioral models for the CMM we have determined the values of the angles and their uncertainty by using Monte Carlo Method. The results show that the proposed methods are suitable to investigate CMM hardware performance and determine the contribution of machine variables to measurement uncertainty. We can affirm that the statistical model behavior is more immediate and less laborious in terms of calculation and implementation time than the Mitutoyo model.