Coordinate Measuring Machine Measurements Research Articles

Laser-Tracing Multi-Station Measurements in a Non-Uniform-Temperature Field

Due to the increasing requirements for the improvement of the accuracy of large coordinate-measuring machines (CMMs), the laser-tracing multi-station measurement technology, as one of the advanced precision measurement technologies, is worth studying in depth in terms of its practical application for the compensation of errors in large CMMs. Since it is difficult to maintain a constant temperature of about 20 °C in the actual workshop under the influence of solar radiation and convective heat transfer, there is a gradient in the spatial temperature distribution, and the overall temperature changes with the influence of external factors with synchronous hysteresis, it is difficult for the actual calibration environment to meet the standard environmental requirements. Therefore, the influence of temperature and other environmental factors on the accuracy of laser ranging and large-scale CMM calibration should not be ignored. In this paper, on the basis of analyzing the temperature distribution and change rule of large CMM measurement space under different working conditions, the radial basis function (RBF) neural network algorithm was used to build a non-uniform-temperature field model, and based on this model and the measurement principle of the laser-tracking instrument, the method of laser tracking and interferometric ranging accuracy enhancement was put forward under a non-uniform-temperature field. Finally, based on the multi-station technique of laser tracing, an accurate solution for the volumetric error of large CMMs under the condition of non −20 °C ambient temperature was realized. Simulation results proved that compared with the traditional temperature-compensation method, the proposed method improved the measurement accuracy of the volumetric error of a large-scale CMM using laser-tracing multi-station technology in a non-uniform-temperature field by 33.5%. This study provides a new approach for improving the accuracy of laser-tracer multi-station measurement systems.

Particle swarm algorithm-based identification method of optimal measurement area of coordinate measuring machine.

A particle swarm algorithm-based identification method for the optimal measurement area of large coordinate measuring machines (CMMs) is proposed in this study to realize the intelligent identification of measurement objects and optimize the measurement position and measurement space using laser tracer multi-station technology. The volumetric error distribution of the planned measurement points in the CMM measurement space is obtained using laser tracer multi-station measurement technology. The volumetric error of the specified step distance measurement points is obtained using the inverse distance weighting (IDW) interpolation algorithm. The quasi-rigid body model of the CMM is solved using the LASSO algorithm to obtain the geometric error of the measurement points in a specified step. A model of individual geometric errors is fitted with least squares. An error optimization model for the measurement points in the CMM space is established. The particle swarm optimization algorithm is employed to optimize the model, and the optimal measurement area of the CMM airspace is determined. The experimental results indicate that, when the measurement space is optimized based on the volume of the object being measured, with dimensions of (35 × 35 × 35) mm3, the optimal measurement area for the CMM, as identified by the particle swarm algorithm, lies within the range of 150mm < X < 500mm, 350mm < Y < 700mm, and -430mm < Z < -220mm. In particular, the optimal measurement area is defined as 280mm < X < 315mm, 540mm < Y < 575mm, and -400mm < Z < -365mm. Comparative experiments utilizing a high-precision standard sphere with a diameter of 19.0049mm and a sphericity of 50nm demonstrate that the identified optimal measurement area is consistent with the results obtained through the particle swarm algorithm, thereby validating the correctness of the method proposed in this study.

A COMPARATIVE STUDY ON PRECISION METROLOGY SYSTEMS FOR ADDITIVE MANUFACTURING

This paper presents a comparative study on precision metrology systems such as Coordinate Measuring Machine (CMM), 3-Dimensional Scanning (3DS) and Computed Tomography (CT) for polymer additive manufacturing. A special test sample was designed and manufactured by Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS) AM systems. The manufactured parts were then measured by three different precision metrology systems and the results were compared in terms of different measurement and AM methods. Uncertainty analyses were conducted based on the results of CMM measurements. The benchmark highlighted the difference between part characteristics manufactured by FDM and SLS, where FDM part represented higher surface roughness and more deviation to the nominal design. Furthermore, expanded uncertainties computed for the FDM manufactured part were almost three times of the uncertainties computed for the SLS manufactured part. It was also demonstrated that one of the major contributors to the expanded uncertainty occurred because of rougher surface of FDM manufactured part. Similar tendency of part to nominal deviations were observable in all metrology systems including CMM, CT and 3DS. Findings of the study revealed the need of standardized measurement for inspection and control of AM parts.

Metrology Benchmarking of 3D Scanning Sensors Using a Ceramic GD&T-Based Artefact.

The use of non-contact scanning equipment in metrology and in dimensional and geometric inspection applications is increasing due to its ease of use, the speed and density of scans, and the current costs. In fact, these technologies are becoming increasingly dominant in the industrial environment, thus moving from reverse engineering applications to metrological applications. However, this planned transfer requires actions to ensure the achievable accuracy by providing traceability of measurements. In the present study, a comparison between the devices is carried out and a specific standard artefact is designed, equipped with multiple ceramic optically friendly entities, and allowing a wide variety of geometric dimensioning and tolerancing (GD&T). Four different 3D scanning sensors are used in the experimentation. Three of them are based on laser triangulation, and the fourth is a structured blue light sensor (fringe pattern projection). The standard artefact is calibrated with a high accuracy, using a coordinate measuring machine (CMM) and probing sensors. With this CMM, reference values of multiple predefined GD&T are obtained. The evaluation methodology maximises the accuracy of each device in measuring the dimensions of the artefact due to the good dimensional (milling and turning), surface (control of machining variables), and the dimensional and spatial distribution characteristics. The procedure also includes the same treatment of the captured point clouds (trimming, filtering, and best-fit algorithm, etc.) in each of the four 3D scanning sensors considered. From this process, very reliable measurements of the maximum achievable accuracy of each device (deviations from the CMM measurements) are finally obtained, and a multi-characteristic comparison between the four sensors is performed, also with high reliability.

Analysis of the Planar Point Identification Accuracy in CMM Measurements.

The paper presents the results of the investigations on the direction-dependent accuracy of the point identification during contact probe measurements with a coordinate measuring machine (CMM). Considering the contact point identified by an orthogonal to the surface probe movement, the transformation of coordinates was made in order to calculate the displacement of the measured point. As a result, the positioning accuracy was estimated in three axes. The experiments demonstrated a strong dependence of the displacement on the declination angle. Moreover, it was found that the directional surface texture which provided different roughness in perpendicular directions, had an impact on the positioning accuracy.

High precision measurement of blade profile curve using iterative normal vector approximation

The precision measurement of blades has always been a complex problem in free-form surface inspection. For contact inspection of blades by coordinate measuring machine (CMM), the probe radius compensation has a critical influence on the measurement accuracy. In this study, an iterative approach based on the normal vector approximation is proposed to measure the blade profile curve, which aims to reduce the measurement error caused by the conventional method relying on a computer aided design model for compensation. The proposed method combines the local reconstruction of blade surface and CMM measurement software. It first takes the measured results of conventional method as a base, then the resulting corrected points of several sectional profiles are fitted to a B-spline surface to provide an alternative nominal model for CMM software, leading to the next same measurement task. Thus, the measurement-fitting process iterates, in which the current nominal model in CMM software is updated by the reconstructed model of foregoing iteration, until a preset condition is reached. Consequently, the proposed method is implemented on a four-axis CMM, and two blade samples are designed to verify the approach’s effectiveness. Experiments show that our approach is superior to the conventional one in blade profile measurement accuracy.

MACHINE DATA-BASED PREDICTION OF BLISK BLADE GEOMETRY CHARACTERISTICS

The increasing availability of data recording solutions in the field of machining in combination with major developments in Machine Learning and Artificial Intelligence enable new approaches towards optimization in the industrial environment. In the aviation industry, critical components must fulfil extremely high quality standards. This requires a stable and error-free manufacturing process, as well as an extensive geometrical compliance, what is until now verified by long-lasting coordinate measuring machine (CMM) inspection. This publication shows how machine data analysis can contribute to reduce CMM measurement effort and thus decrease component cycle time. For this purpose, production machine data from an aircraft engine Inconel compressor blisk blade 5-axis milling operation was recorded and analysed by subsequent application of machine learning algorithms to predict the geometric measurement characteristics.

Uncertainty assessment for measurement and simulation in selective laser melting: a case study of an aerospace part

&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;In this work, the additive manufacturing process selective laser melting is analysed with the aim of realising a complex piece for aerospace applications. In particular, the effect of the manufacturing process and of the following thermal treatments on the dimensions of the workpiece is evaluated. The study is based on a hybrid approach including a simulation of the whole manufacturing process by advanced software packages and the dimensional measurements of the realised pieces taken by a coordinate measuring machine (CMM). The integrated use of simulation and measurements is carried out with the aim of validating the simulation results and of identifying the operational limits of both approaches; this analysis is based on metrological evaluation of the results of both the simulation and the tests, taking into account the uncertainty of the data. In addition, the main causes of uncertainty for the simulation activity and the experimental data have been identified, and the effects of some of them have also been experimentally evaluated. Based on the experimental validation, the simulation seems to predict the absolute displacement of the supports of the piece in a satisfactory way, while it is unable, in the actual configuration, to assess the conformity of the surface to its very tight shape tolerances. Conformity assessment of the surface should be carried out by CMM measurement. Integrated use of simulation and experimental results is expected to strongly improve the accuracy of simulation results for the effective and accurate design and control of the additive manufacturing process, including dimensional control and thermal treatments to mitigate induced thermal stresses.&lt;/span&gt;&lt;/p&gt;

Path Planning and Setup Orientation for Automated Dimensional Inspection Using Coordinate Measuring Machines

Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity. A reasonable inspection plan, which can reduce inspection costs and achieve high customer satisfaction, is therefore very important in the production industry. Considerations such as preparations for part inspection, measuring machines, and their setups as well as the measurement path are described in an inspection plan which is subsequently translated into part inspection machine language. Therefore, the measurement of any component using a coordinate measuring machine (CMM) is the final step preceded by several other procedures, such as the preparation of the part setup and the generation of the probe path. Effective measurement of components using CMM can only be done if the preceding steps are properly optimized to automate the whole inspection process. This paper has proposed a method based on artificial intelligence techniques, namely, artificial neural network (ANN) and genetic algorithm (GA), for fine-tuning output from the different steps to achieve an efficient inspection plan. A case study to check and validate the suggested approach for producing effective inspection plans for CMMs is presented. A decrease of nearly 50% was observed in the travel path of the probe, whereas the CMM measurement time was reduced by almost 25% during the actual component measurement. The proposed method yielded the optimum part setup and the most appropriate measuring sequence for the part considered.

Investigation of the effect of temperature and other significant factors on systematic error and measurement uncertainty in CMM measurements by applying design of experiments

The design of experiments was used in this paper in order to determine the significance of particular factors for the parameters that indicate the quality of measurement results obtained by means of a coordinate measuring machine (CMM). Special emphasis was placed on the interaction of temperature and other factors. In accordance with the design of experiments, the diameter and roundness of a ring gauge were measured at the reference temperature and at the temperature of 26 °C. The ISO 15530-3:2011 standard for the assessment of measurement uncertainty of CMM measurements distinguishes systematic error from measurement uncertainty. Thus, separate analyses were done on how temperature, in the first place, but also workpiece position on the CMM table, coordinate system, stylus tip diameter, sampling size and interaction of temperature with other factors affect systematic error and measurement uncertainty. The experimental results showed an increase in systematic error at the higher temperature when the diameter was measured, whereas with roundness, systematic error was even smaller at the higher temperature. As for measurement uncertainty, measurements made at the higher temperature have a significantly greater uncertainty when the diameter is measured, unlike with roundness, where the effect of temperature is almost negligible.

Estimation of Wear Depth as an Approach to Predict Tool Service Life in Cold Forming Process

Wear phenomenon is considered as a predominant parameter in the forming processes causing the shorten tool life which in turn increases the production costs. In this study, wear depth of tool components for multi-stages of metal shell of spark plug (MSSP) manufacturing was analyzed using Archard's model with FE simulation software. The 3D geometry models were built using SolidWorks software then the models files were exported to Simufact forming software to do the settings of preprocess and FE analysis. The cylinder compression test and ring compression test were executed to get the flow stress and frictional factor respectively. Product parts dimensions, forging loads, effective stress, relative sliding velocity and contact pressure were solved and discussed. Subsequently to verify the analysis acceptance, the actual product parts measurements which obtained by coordinate measuring machine (CMM), were compared with the simulation results. It was found that the deviations of actual MSSP geometry dimensions less than (4%). Also verification is performed to forging loads at each stage which gives a good agreement between actual and FE simulation results. Finally, the wear depth of tool components were calculated for each stage using the FE software. Based on the results of wear depth from simulation and tolerance rages of actual product parts, tool service lives were predicted to find the productivities for each tool component. The results of FE simulation were compared with the CMM measurements for known productivities tool components from actual production line, which gives a good accuracy and acceptable agreement

Hip simulator testing of the taper-trunnion junction and bearing surfaces of contemporary metal-on-cross-linked-polyethylene hip prostheses.

Adverse reaction to metal debris released from the taper-trunnion junction of modular metal-on-polyethylene (MoP) total hip replacements (THRs) is an issue of contemporary concern. Therefore, a hip simulator was used to investigate material loss, if any, at both the articulating and taper-trunnion surfaces of five 32-mm metal-on-cross-linked-polyethylene THRs for 5 million cycles (Mc) with a sixth joint serving as a dynamically loaded soak control. Commercially available cobalt-chromium-molybdenum femoral heads articulating against cross-linked polyethylene (XLPE) acetabular liners were mounted on 12/14 titanium (Ti6Al4V) trunnions. Weight loss (mg) was measured gravimetrically and converted into volume loss (mm3 ) for heads, liners, and trunnions at regular intervals. Additionally, posttest volumetric wear measurements of the femoral tapers were obtained using a coordinate measuring machine (CMM). The surface roughness (Sa) of femoral tapers was measured posttest. After 5 Mc, the mean volumetric wear rate for XLPE liners was 2.74 ± 0.74 mm3 /Mc. The CMM measurements confirmed material loss from the femoral taper with the mean volumetric wear rate of 0.045 ± 0.024 mm3 /Mc. The Sa on the worn area of the femoral taper showed a significant increase (p < 0.001) compared with the unworn area. No other long-term hip simulator tests have investigated wear from the taper-trunnion junction of contemporary MoP THRs. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 108B:156-166, 2020.

Reducing the latency between machining and measurement using FEA to predict thermal transient effects on CMM measurement

Coordinate Measuring Machines (CMM) are widely used to validate the dimensional conformance of machined components. Since time frames are critical in ensuring cost effective production, it is important to know when a machined component is ready for dimensional inspection. A common approach is to delay the inspection until the component has reached thermal stabilization such as 20 °C as standard, however, this latency incurs an uncertainty of getting either under or overestimated which can result in a considerable waste of time and resources lowering cost effectiveness. Conversely, premature measurement could also increase the measurement uncertainty if significant temperature gradients exist. This paper presents an approach to predict the required latency at which the machined component is within critical thermal boundary conditions and ready for dimensional checks. The approach is comprised of experimental tests required to obtain key boundary conditions including convective and Thermal Contact Conductance (TCC) values. FEA was then used to simulate and validate the latency based on anticipated workpiece conditions. The method was validated on an industrial part, namely a Venturi vessel, in two typical scenarios i.e. placed on the (1) Chuck and (2) CMM to predict the latency required for thermal stabilization using a pre-selected part tolerance level of 1.5 µm. The validation revealed 8.3 min for (1) and 7.6 min for (2) which is a considerable reduction of latency in comparison to waiting for longer periods. This technique was also compared with a typical CMM measurement regime of using a single temperature sensor for compensation. A simple comparison yielded an over or under compensation of 7 µm depending on the position of temperature sensor on the vessel surface. The validations have allowed the formation of a platform for the FEA software to be effectively used to predict the required latency to improve the management of CMM machine usage while ensuring low uncertainty and a cost effective solution.

Simulated variogram-based error inspection of manufactured parts

Industrial parts are routinely affected by dimensional and geometric errors originated in the course of manufacturing processes. These errors, whose pattern is typically related to a specific machining or forming process, are controlled in terms of dimensional and geometrical tolerances (such as e.g. straightness, roundness, flatness, profile) that require verification. In the last two decades, the Kriging model has been put forward for predicting errors of surfaces, whose tolerances are verified using coordinate measuring machines (CMMs), commonly used for 3D measurement on account of both accuracy and flexibility. Kriging is stochastic linear interpolation technique based on an evaluation of how the variance between responses at different points depends on the distance between the two locations. This can be expressed as a variogram which shows how the average difference between values at points changes; it is a function of the distance and of the corresponding direction of any pair of points depicting their correlation extent. The use of the variogram for identifying the correlation structure is recommended by the geostatisticians even if the stochastic process is not stationary. In this paper we resort to empirical variograms to detect possible manufacturing signatures, i.e. systematic pattern that characterises all the features manufactured with a particular production process, and systematic errors of the CMM measurement process. We simulate planar surfaces affected by different and typical manufacturing signatures and possible errors of a measurement process. The behaviour of the variogram gives evidence of spatial correlations, enlightening possible non isotropy. More specifically, we recommend the variograms in different directions of the axis because they draw attention to trend components with a specified direction.

Thermal test piece for 5-axis machine tools

The accuracy of 5-axis machine tools is a key factor to modern manufacturing of multi-axes machined workpieces. Up to 75% of errors on manufactured workpieces are caused by thermally induced errors of machine tools. In this paper a thermal test piece for evaluating thermal errors of machine tools equipped with rotary tables is introduced. In comparison to geometric test pieces, which detect geometrical errors of machine tools only, the developed thermal test piece visualizes thermally induced errors in X-, Y- and Z-direction as well as two additional angular errors at the tool center point (TCP) of 5-axis machine tools over time. Further, the thermal material expansion error of the test piece on centric clamped machined workpieces can be evaluated with the new thermal test piece. The presented thermal test piece is machined during a corresponding eight hour test cycle and tested on two 5-axis milling machines with different axis configurations. The thermal test piece can either be evaluated with a coordinate measuring machine (CMM) or manually with handheld measurement devices such as dial gauge, micrometer and straightedge. The thermal behavior of the two machine tools is also investigated, following the ISO 230-3:2007 regarding thermal errors of machine tools, under no-load or finishing conditions using the R-Test measurement setup. The resulting workpiece errors evaluated by using the thermal test piece are compared to the machine tool measurements. A good correspondence between the R-Test measurements and the thermal workpiece errors can be shown. CMM and handheld measurements are both capable to detect the thermal machine tool errors.

A Clinical Comparative Study of 3‐Dimensional Accuracy between Digital and Conventional Implant Impression Techniques

To evaluate the accuracy of a digital implant impression technique using a TRIOS 3Shape intraoral scanner (IOS) compared to conventional implant impression techniques (pick-up and transfer) in clinical situations. Thirty-six patients who had two implants (Implantium, internal connection) ranging in diameter between 3.8 and 4.8 mm in posterior regions participated in this study after signing a consent form. Thirty-six reference models (RM) were fabricated by attaching two impression copings intraorally, splinted with autopolymerizing acrylic resin, verified by sectioning through the middle of the index, and rejoined again with freshly mixed autopolymerizing acrylic resin pattern (Pattern Resin) with the brush bead method. After that, the splinted assemblies were attached to implant analogs (DANSE) and impressed with type III dental stone (Gypsum Microstone) in standard plastic die lock trays. Thirty-six working casts were fabricated for each conventional impression technique (i.e., pick-up and transfer). Thirty-six digital impressions were made with a TRIOS 3Shape IOS. Eight of the digitally scanned files were damaged; 28 digital scan files were retrieved to STL format. A coordinate-measuring machine (CMM) was used to record linear displacement measurements (x, y, and z-coordinates), interimplant distances, and angular displacements for the RMs and conventionally fabricated working casts. CATIA 3D evaluation software was used to assess the digital STL files for the same variables as the CMM measurements. CMM measurements made on the RMs and conventionally fabricated working casts were compared with 3D software measurements made on the digitally scanned files. Data were statistically analyzed using the generalized estimating equation (GEE) with an exchangeable correlation matrix and linear method, followed by the Bonferroni method for pairwise comparisons (α = 0.05). The results showed significant differences between the pick-up and digital groups in all of the measured variables (p < 0.001). Concerning the transfer and digital groups, the results were statistically significant in angular displacement (p < 0.001), distance measurements (p = 0.01), and linear displacement (p = 0.03); however, between the pick-up and transfer groups, there was no statistical significance in all of the measured variables (interimplant distance deviation, linear displacement, and angular displacement deviations). According to the results of this study, the digital implant impression technique had the least accuracy. Based on the study outcomes, distance and angulation errors associated with the intraoral digital implant impressions were too large to fabricate well-fitting restorations for partially edentulous patients. The pick-up implant impression technique was the most accurate, and the transfer technique revealed comparable accuracy to it.

An investigation of Outlier Detection Procedures for CMM Measurement Data

The paper analyses methods for outlier detection in dimensional measurement. The cross sections of an internal cylinder were inspected by CMM (coordinate measuring machine), and received data sets were employed for further investigation. The efficiency of Rosner’s and Grubbs’ methods for excluding outliers from the measuring data had been estimated. The method of Rosner had been defined as the most effective for this case study. The simulation results were confirmed by experimental verification.

A Gaussian Process Data Modelling and Maximum Likelihood Data Fusion Method for Multi-Sensor CMM Measurement of Freeform Surfaces

Nowadays, the use of freeform surfaces in various functional applications has become more widespread. Multi-sensor coordinate measuring machines (CMMs) are becoming popular and are produced by many CMM manufacturers since their measurement ability can be significantly improved with the help of different kinds of sensors. Moreover, the measurement accuracy after data fusion for multiple sensors can be improved. However, the improvement is affected by many issues in practice, especially when the measurement results have bias and there exists uncertainty regarding the data modelling method. This paper proposes a generic data modelling and data fusion method for the measurement of freeform surfaces using multi-sensor CMMs and attempts to study the factors which affect the fusion result. Based on the data modelling method for the original measurement datasets and the statistical Bayesian inference data fusion method, this paper presents a Gaussian process data modelling and maximum likelihood data fusion method for supporting multi-sensor CMM measurement of freeform surfaces. The datasets from different sensors are firstly modelled with the Gaussian process to obtain the mean surfaces and covariance surfaces, which represent the underlying surfaces and associated measurement uncertainties. Hence, the mean surfaces and the covariance surfaces are fused together with the maximum likelihood principle so as to obtain the statistically best estimated underlying surface and associated measurement uncertainty. With this fusion method, the overall measurement uncertainty after fusion is smaller than each of the single-sensor measurements. The capability of the proposed method is demonstrated through a series of simulations and real measurements of freeform surfaces on a multi-sensor CMM. The accuracy of the Gaussian process data modelling and the influence of the form error and measurement noise are also discussed and demonstrated in a series of experiments. The limitations and some special cases are also discussed, which should be carefully considered in practice.

Measurement uncertainty on the circular features in coordinate measurement system based on the error ellipse and Monte Carlo methods

The uncertainty determination of the geometrical feature measurement for coordinate measuring machines (CMMs) is an essential part in the reliable quality control process. However, the most commonly-used methods for uncertainty assessment are difficult and require not only a large number of repeated measurements but also rich operation experience. Based on the error ellipse theory and the Monte Carlo simulation method, an uncertainty evaluation method for CMM measurements is presented. For circular features, the uncertainty evaluation model was established and extended into the use of an application of two holes’ central distance measurement through Monte Carlo Simulation. The verification experiment of the new method was conducted and results were compared with the traditional ones and they fit reasonably well, which proved the validity of the proposed method.

Evaluating hip implant wear measurements by CMM technique

Wear of the bearing components is a major factor affecting the performance of hip implants. Wear analysis is thus one of the most important processes for understanding of how wear occurs and its location and development, with an aim to improve the success rate and longevity of total hip replacements. This study presents wear measurement and analysis of polyethylene acetabular components using a coordinate measuring machine (CMM). An uncertainty analysis was performed to validate CMM wear measurements. Measurements were made with an increasing number of scanning points on retrieved ex vivo implants to investigate the effect of the number of scanning points on wear measurements. CMM wear measurements were compared to in vivo radiographic measurements (RSA) and volumetric wear estimates made using well-established analytical formulae. The number of scanning points has insignificant effect on wear quantity and measurement uncertainty. CMM linear wear measurements closely match those made using RSA, whereas theoretical volumetric formulae applied to CMM and RSA measurements both appear to overestimate wear volume. The findings further emphasize potential benefit of using CMM ex vivo and RSA in vivo to assess polyethylene wear after total hip replacement surgery. Further, it is shown that identifying key components of the measurement uncertainty are important to improve the accuracy and the reliability of CMM wear measurement.