Assessing measurement uncertainty in CMM measurements: comparison of different approaches

Abstract

Abstract. Manufactured parts are affected by size and form errors, which need to be assessed against geometrical and dimensional tolerances in order to meet the functional requirements they have been conceived for. The compliance assessment of workpieces with specifications depends on measurements and is unavoidably affected by several uncertainty contributions. According to the geometrical product specifications and verification (GPS), measurement uncertainty consists of method and implementation uncertainty. Literature proposes different approaches for the evaluation of implementation uncertainty, however a standardized method has not been yet achieved. This paper makes an analysis of the various elements to be considered when choosing a specific approach: in the implementation uncertainty of circular features. The most common manufacturing signatures affecting circular profiles were considered, together with the number of points necessary for a reliable estimation of implementation uncertainty.

A Random Factorial Design of Experiments Study on the Influence of Key Factors and Their Interactions on the Measurement Uncertainty: A Case Study Using the ZEISS CenterMax

“…In the paper of [1], the authors mention that, according to the ISO12181-1, roundness tolerance is defined as the zone between two concentric circles which enclose the circular profile. Besides, it limits the roundness deviations, which is the distance between two concentric circles touching and enclosing the extracted circle (i.e., measured points) at the minimum radial distance from each other.…”

“…In manufacturing, parts and pieces are often affected by form and size errors that have to be assessed against geometrical and dimensional tolerances in order to prove their conformance with given specifications [1]. In this regard, when referring to the measurement result of a physical quantity, it becomes necessary to offer valuable measurable proof of the quality of the result in a way that people using it afterward can assess its reliability.…”

Simulation Algorithm of Sample Strategy for CMM Based on Neural Network Approach

“…In another paper, Qimi, Mesay et al [6] have estimated the frequency of systematic errors by use of Fourier analyses. Other authors have investigated the measuring uncertainty due to the sample size based on approximation of aperiodic deterministic profile with Fourier series [7], [8].…”

Robust Estimation of Optimal Sample Size for CMM Measurements with Statistical Tolerance Limits

“…The influence of sample size on the measurement result has been widely discussed, and several different approaches has been used to estimate the contribution to the measurement uncertainty. Approaches such as statistical methods [2,3] (for normal distribution), fuzzy logic [4], genetic algorithm [5], extended zone model optimization [6], adaptive sample strategy with use of Kriging models [7] and analytical methods with implementation uncertainty simulations [8,9] have been suggested. However, a standard guide or criterion for sample strategy with CMM GD&T inspection has not been established yet.…”