Simplified model to estimate uncertainty in CMM

Arencibia, Rosenda Valdés; Souza, Cláudio Costa; Costa, H.L.; Piratelli-Filho, Antonio

doi:10.1007/s40430-014-0157-8

Cited by 13 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Weckenmann et al [4], operators and workpiece shapes should also be considered as the critical factors because they contribute significantly to the measurement uncertainty. A simplified mathematical model was developed by Arencibia et al [46] to compute measurement uncertainty relevant to the inspection of circularity and cylindricity deviations. ey recognized the probing error as having the greatest impact on the uncertainty values.…”

Section: Literature Surveymentioning

confidence: 99%

Prognosis and Multiobjective Optimization of the Sampling Plan for Cylindricity Evaluation

Mian

Umer

Abdulhameed

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The actualization of the befitting sampling strategy and the application of an appropriate evaluation algorithm have been elementary issues in the coordinate metrology. The decisions regarding their choice for a given geometrical feature customarily rely upon the user’s instinct or experience. As a consequence, the measurement results have to be accommodated between the accuracy and the inspection time. Certainly, a reliable and efficient sampling plan is imperative to accomplish a dependable inspection in minimal time, effort, and cost. This paper deals with the determination of an optimal sampling plan that minimizes the inspection cost, while still promising a measurement quality. A cylindrical-shaped component has been utilized in this work to achieve the desired objective. The inspection quality of the cylinder using a coordinate measuring machine (CMM) can be enhanced by controlling the three main parameters, which are used as input variables in the data file, namely, point distribution schemes, total number of points, and form evaluation algorithms. These factors affect the inspection output, in terms of cylindricity and measurement time, which are considered as target variables. The dataset, which comprises input and intended parameters, has been acquired through experimentation on the CMM machine. This work has utilized state-of-the-art machine learning algorithms to establish predictive models, which can predict the inspection output. The different algorithms have been examined and compared to seek for the most relevant machine learning regression method. The best performance has been observed using the support vector regression for cylindricity, with a mean absolute error of 0.000508 mm and a root-mean-squared error of 0.000885 mm. Likewise, the best prediction performance for measuring time has been demonstrated by the decision trees. Finally, the optimal parameters are estimated by employing the grey relational analysis (GRA) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS). It has been approved that the values obtained from GRA are comparable with those of the FTOPSIS. Moreover, the quality of the optimal results is bettered by incorporating the measurement uncertainty in the outcome.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Prognosis and Multiobjective Optimization of the Sampling Plan for Cylindricity Evaluation

Mian

Umer

Abdulhameed

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The expanded uncertainty in the 2D CMM measurement for straightness deviation is studied based on GUM [12]. Combined and expanded uncertainty for the experimental results is estimated.…”

Section: Aiii Straightness Measurement In 2dmentioning

confidence: 99%

“…Straightness deviation is also an important feature of alignment for rotating parts. Optimization in straightness form deviation in measurement using many algorithm techniques is a newly introduced methodology in dimensional metrology as it has been indicated in the GUM guidelines [9][10][11][12][13]. In GUM, propagation of uncertainty and the characterization of the output quantity by a Gaussian distribution are recommended [14].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Experimental And Theoretical Validation Method For Estimation Of Straightness Deviation And Associated Uncertainty In Cnc-Cmm Measurement

Ali

Khalafalla

Naeim

et al. 2016

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Abstract-Geometrical deviation in intelligent metrologyComparison with relevant report showed agreement with our result since we used a computationally efficient modified SMC technique and PSO algorithm. The results of the straightness deviations and associated expanded uncertainties for both 2D and 3D measurements have been discussed and compared. They were found to be suitable for the proposed validation method. This work confirms that the developed strategic alternative methodology can be achieved successfully. Systematic acquisition of CNC-CMM data is another contributing factor for improving the required accuracy in measurement. Moreover, the confidence in the proposed hybrid validation method for estimating the straightness deviation with associated uncertainty has been achieved.

show abstract

“…Arencibia adopted the GUM method in combination with the coordinate measuring machine to calculate the uncertainty of roundness or cylindricity errors. Considering the correlation of variables, the uncertainty of the measurand was calculated by the GUM method [12]. The use of calibrated parts in ISO/TC 15530-3 is also a method to calculate the uncertainty.…”

Section: Introductionmentioning

confidence: 99%

The Calculation of Roughness Uncertainty by Fitting B‐Spline Filter Assessment Middle Lines

Liu

Sang

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The roughness and uncertainty are important parameters of surface morphology. The least square middle line method is often used to estimate the roughness and its uncertainty. However, the roughness and its uncertainty obtained by the least square middle line method are inaccurate. This paper proposes a method to calculate exactly the roughness and its uncertainty by piecewise fitting the smooth B-spline filter assessment middle lines. A B-spline smoothing filter is selected to determine the assessment middle line of roughness. The B-spline filter can not only give the accurate roughness, but also obtain the smooth assessment middle line. The model of roughness uncertainty is proposed by piecewise fitting B-spline filter middle lines as the quadratic curves. The S-shaped test part is used to verify the model of roughness uncertainty.

show abstract

Simplified model to estimate uncertainty in CMM

Cited by 13 publications

References 19 publications

Prognosis and Multiobjective Optimization of the Sampling Plan for Cylindricity Evaluation

Prognosis and Multiobjective Optimization of the Sampling Plan for Cylindricity Evaluation

Experimental And Theoretical Validation Method For Estimation Of Straightness Deviation And Associated Uncertainty In Cnc-Cmm Measurement

The Calculation of Roughness Uncertainty by Fitting B‐Spline Filter Assessment Middle Lines

Contact Info

Product

Resources

About