A study on mutative scale straightness measurement based on uncertainty analysis

Cao, Yanlong; Li, Bo; Guan, Jiayan; Yang, Jiangxin; Gan, Chunbiao

doi:10.1016/j.measurement.2012.06.001

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…(x max , y max )is the farthest point and (x min , y min )is the nearest point. According to the above definition of straightness error, the literature [4] gives the analytical expression of the straightness uncertainty u δ as the combined uncertainty based on GUM.…”

Section: Error Ellipse Theory Integrated With Monte Carlo Methodsmentioning

confidence: 99%

“…As the uncertainty is influenced by various factors, including temperature, measurement method, data processing algorithm, sampling strategy, etc, some researchers started with the factors. Cao et al [4] described the composition of the uncertainty caused by the measuring process and analysed the uncertainty caused by sample size insufficiency and measuring process. Cui et al [5] also pointed out that, with more sample points, the uncertainties from different methods are a little smaller, and it is necessary to focus on the measurement strategies.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty evaluation of straightness in coordinate measuring machines based on error ellipse theory integrated with Monte Carlo method

Zhu

Yang

et al. 2019

Meas. Sci. Technol.

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According to the new generation geometrical product specification, it is necessary to provide measurement uncertainty together with measurement results in order to determine the reliability of results. The traditional methods used for the uncertainty evaluation of straightness are laborious and time-consuming owing to a large quantity of repeated measurements or a complicated computational process. Based on the error ellipse theory and the Monte Carlo method, a novel method for uncertainty evaluation is proposed. Through the error ellipse theory, in the measuring space of coordinate measuring machines, the positional uncertainty of sampling points can be more accurately considered to be represented by an ellipse. By integrating the Monte Carlo method, only with limited sets of real measured data in small experimental trials, the uncertainty propagation from a single sampling point to the whole straight line can be demonstrated clearly in the simulation without requiring large amounts of time and labour. The detailed procedures of uncertainty evaluation are given. The straightness uncertainty can then be obtained by statistical analysis of the simulation results. Real straightness measurement experiments were carried out and compared with the results from the proposed method. The difference was no more than 5%, which verified the validity of the method.

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Section: Error Ellipse Theory Integrated With Monte Carlo Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty evaluation of straightness in coordinate measuring machines based on error ellipse theory integrated with Monte Carlo method

Zhu

Yang

et al. 2019

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The sphericity error is the distance between the two concentric spheres. The general function of the sphericity profile as a quadratic surface [19] was given by…”

Section: Sphericity Errormentioning

confidence: 99%

Sampling strategy and error estimation for evaluation of quadratic form error using Cartesian coordinate data

Fei

Liu

et al. 2017

IET Science, Measurement & Technology

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“…Wen et al [26] presented a quasi-particle swarm optimization algorithm on the basis of the minimum zone principle to calculate the cylindricity error. Cao et al [27] studied the uncertainty composition of the measurement process and the factors that influence the uncertainty by the insufficient sample size. Hüser et al [28] calculated the uncertainty of material ratio parameters in light of a single profile by a statistical approach.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty assessment of machine tool squareness error identification using on-machine measurement

Tang,

Feng,

et al. 2023

Meas. Sci. Technol.

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Identification and compensation of geometric errors in the machine tool are widely performed to increase machining accuracy. Periodic verification of the geometric errors, as introduced in ISO 230-2, helps monitor the machine tool accuracy during continued manufacturing operation and detect accuracy degradation early on. While interferometry-based error identification techniques are commonly applied, on-machine measurement (OMM), with its increasing availability to machine tools, can be used to identify geometric errors as an alternative to traditional interferometry-based techniques. As geometric errors also contribute to OMM error, assessment of the uncertainty of error identification is essential to ensure the reliability of the identification. This work presents a method to evaluate the uncertainty in the OMM-based geometric error identification process by Monte Carlo simulation. The error ellipse model, which represents OMM errors with better accuracy, is utilized to improve the identification uncertainty. The squareness errors, as position-independent geometric errors that contribute to machining inaccuracy, are taken as an example to demonstrate the presented method. The influence of the artifact setup errors on the error identification is also investigated. A series of experiments are conducted to evaluate the uncertainty of the OMM-based identification method. The geometric error obtained with the presented method is found to deviate by less than 10% from that obtained with an interferometry-based commercial instrument. The uncertainty obtained from the proposed Monte Carlo simulation method matches well with the uncertainty results obtained by the Guide to the Expression of Uncertainty in Measurement method and repeated measurements.

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A study on mutative scale straightness measurement based on uncertainty analysis

Cited by 9 publications

References 30 publications

Uncertainty evaluation of straightness in coordinate measuring machines based on error ellipse theory integrated with Monte Carlo method

Uncertainty evaluation of straightness in coordinate measuring machines based on error ellipse theory integrated with Monte Carlo method

Sampling strategy and error estimation for evaluation of quadratic form error using Cartesian coordinate data

Uncertainty assessment of machine tool squareness error identification using on-machine measurement

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