Calibration and uncertainty estimation of non-contact coordinate measurement systems based on Kriging models

Lan, Fei; Dantan, Jean‐Yves; Baudouin, Christophe; Du, Shichang

doi:10.1016/j.precisioneng.2019.02.004

Cited by 14 publications

(3 citation statements)

References 49 publications

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“…This prediction capability was used to develop adaptive inspection methods for engineering problems and was successfully implemented in surface metrology to design adaptive sampling strategies for machined parts on CMMs (Ascione et al, 2013; Du and Fei, 2016). The Kriging model has also been used to identify the minimum number of checkpoints to reduce measurement errors (Dumas et al, 2013) and estimate the uncertainty to improve measurement accuracy (Fei et al, 2019). In the inspection of an unknown freeform surface, a few initial points are sampled manually (Zhang et al, 2021) or by other means (Chen and Peng, 2017; Lu et al, 2019; Sun et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

Gao,

Zheng,

et al. 2023

Transactions of the Institute of Measurement and Control

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Owing to the lack of prior knowledge, the accurate reconstruction of surfaces with high uncertainty is dependent on the reasonable real-time selection of the next best point (NBP) during the sampling process. In this study, a new informative criterion called the MaxCWVar weighting shape effect is proposed for NBP selection. The responses to the geometric features of the candidate locations are predicted by a back propagation neural network (BPNN), which is then used in combination with the jackknife method to estimate the candidate uncertainty. The blade cross-section sampling case is considered to validate the flexibility and effectiveness of the proposed method. A comparison with other adaptive sampling strategies shows that BPNN-based response prediction is well-suited for allocating sample points. In contrast to other NBP selection criteria, the sample point distribution recommended by the MaxCWVar criterion is preferable as it improves the reconstruction accuracy and modeling efficiency. This study promotes the exploration of metrological methods for the fast and intelligent reconstruction of complex surfaces with high uncertainty.

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Section: Introductionmentioning

confidence: 99%

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

Gao,

Zheng,

et al. 2023

Transactions of the Institute of Measurement and Control

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“…The use of spatial statistics based on Gaussian fields, in particular kriging, has been investigated [39], where a method to directly derive form error from the kriging model was presented, and the relationships with point-based sampling investigated. More recently, kriging models have been used to estimate uncertainty and calibrate non-contact CMSs [40].…”

Section: Introductionmentioning

confidence: 99%

Statistical point cloud model to investigate measurement uncertainty in coordinate metrology

Senin

Catalucci

Moretti

et al. 2021

Precision Engineering

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“…According to Fei L the measurement results from some advance machines nowadays commonly affected by uncertainty [6]. In International Vocabulary of Metrology (VIM), uncertainty is define according to the result of the measurement.…”

Section: Introductionmentioning

confidence: 99%

Surface Reversal Method Algorithm Development

Roslan¹,

Ismail²

2021

AETiC

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The reversal method introduced by previous research focuses in finding reference datum of topography data of cylinder end surface but somehow ignores the existence of error. This problem leads to the purpose in introducing an understanding of how this method can be improvised. Thus, in this paper, a primitive approach was done by introducing the algorithm of the surface reversal method and simulating the reversal method on a selected surface to test how it works. This paper used MATLAB software for the majority of data processing. It started by preparing two simulated surfaces which were surface A and surface B (180o rotation). Then, it continued with the preparation of surface by converting the surface data into an image file type which made surface A and surface B aligned in the same direction before conducting surface correlation and finally finding the best match point between both surfaces. These basic steps play an important role to ensure the whole procedure for the next future studies is accurate. The peak result at position (540 x 409) from surface correlation was able to ease the next procedure by having a proper position before doing surface subtraction. The algorithm developed through MATLAB has successfully demonstrated the primary steps of surface reversal method.

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Calibration and uncertainty estimation of non-contact coordinate measurement systems based on Kriging models

Cited by 14 publications

References 49 publications

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

Statistical point cloud model to investigate measurement uncertainty in coordinate metrology

Surface Reversal Method Algorithm Development

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