Review of the mathematical foundations of data fusion techniques in surface metrology

Wang, Jian; Leach, Richard; Jiang, Xiangqian

doi:10.1088/2051-672x/3/2/023001

Cited by 38 publications

(42 citation statements)

References 86 publications

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“…Some advanced fitting or fusion models for surface modelling or reconstruction are parametric models; but the model parameters cannot be calculated using the linear method described in equation (5). For example, the multilevel B-spline approximation [10] and the least-squares B-spline approximation [26] determine the model parameters using an iterative approximation method.…”

Section: Parametric and Non-parametric Weighted Fusionmentioning

confidence: 99%

“…Data fusion [5], which is usually a computationally-intensive process, is one of the essential processes in multi-sensor measurement. Data fusion combines data from several information sources into a common representational format, hence the metrological evaluation can benefit from all available sensor information and data [1].…”

Section: Introductionmentioning

confidence: 99%

“…Most fusion methods rely on advanced surface fitting techniques under a residual approximation (RA) framework [5]. For example, in the research work [8,9], Gaussian process (GP) RA fusion (GPRA), which approximates the residuals between two inputs using a GP model, has been demonstrated to be a successful solution to the fusion of heterogeneous data.…”

Section: Introductionmentioning

confidence: 99%

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Study of weighted fusion methods for the measurement of surface geometry

Wang

Pagani

Leach

et al. 2017

Precision Engineering

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights:  Four weighted fusion methods for surface measurement were classified and analysed.  The uncertainty propagation and the relationship with Kalman filter were analysed.  Advanced models compatible with different fusion methods were described.  Experiments verified the effectiveness of weighted fusion on accuracy improvement.  Weighted fusion complements residual approximation fusion in many fusion scenarios. AbstractFour types of weighted fusion methods, including pixel-level, least-squares, parametrical and non-parametrical, have been classified and theoretically analysed in this study. In particular, the uncertainty propagation of the weighted least-squares fusion was analysed and its relation to the Kalman filter was studied. In cooperation with different fitting models, these four weighted fusion methods can be applied to a range of measurement challenges.The experimental results of this study show that the four weighted fusion methods compose a computationally efficient and reliable system for multi-sensor measurement problems, especially for freeform surface measurement. A comparison of weighted fusion with residual approximation-based fusion has also been conducted by providing the input datasets with different noise levels and sample sizes. The results demonstrated that weighted fusion and residual approximation-based fusion are complementary approaches applicable to most fusion scenarios.

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Section: Parametric and Non-parametric Weighted Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study of weighted fusion methods for the measurement of surface geometry

Wang

Pagani

Leach

et al. 2017

Precision Engineering

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“…One difficulty lies in measuring the internal part surface. Computer Tomography (CT) now allows non-destructive 3D measurements of internal and external part geometry with uncertainties down to micrometers [7,8,9]. Data issued from tomography represent the different levels of X-ray attenuation of the part for each point of the measured volume.…”

Section: Am Product Examplesmentioning

confidence: 99%

Multi-scale surface characterization in additive manufacturing using CT

Quinsat

Lartigue

Brown³

et al. 2016

Lecture Notes in Mechanical Engineering

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In additive manufacturing, the part geometry, including its internal structure, can be optimized to answer functional requirements by optimizing process parameters. This can be performed by linking process parameters to the resulting manufactured geometry. This paper deals with an original method for surface geometry characterization of printed parts (using Fused Filament Fabrication FFF) based on 3D Computer Tomography (CT) measurements. From 3D measured data, surface extraction is performed, giving a set of skin voxels corresponding to the internal and external part surface. A multi-scale analysis method is proposed to analyse the relative internal area of the total surface obtained at different scales (from sub-voxel to super-voxel scales) with different process parameters. This analysis turns out to be relevant for filling strategy discrimination.

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“…Data fusion has been carried since the 60's in different fields, and can be primarily classified in three levels: decision, feature, and signal level [3]. Decision data fusion level is when a set of parameters describing the surface characteristics are extracted from the topographical data, like roughness or step height, from measurements taken from different sensors and at the same or different scales.…”

Section: Surface Metrology Data Fusionmentioning

confidence: 99%

Three-dimensional measurements with a novel technique combination of confocal and focus variation with a simultaneous scan

et al. 2016

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The most common optical measurement technologies used today for the three dimensional measurement of technical surfaces are Coherence Scanning Interferometry (CSI), Imaging Confocal Microscopy (IC), and Focus Variation (FV). Each one has its benefits and its drawbacks. FV will be the ideal technology for the measurement of those regions where the slopes are high and where the surface is very rough, while CSI and IC will provide better results for smoother and flatter surface regions. In this work we investigated the benefits and drawbacks of combining Interferometry, Confocal and focus variation to get better measurement of technical surfaces. We investigated a way of using Microdisplay Scanning type of Confocal Microscope to acquire on a simultaneous scan confocal and focus Variation information to reconstruct a three dimensional measurement. Several methods are presented to fuse the optical sectioning properties of both techniques as well as the topographical information. This work shows the benefit of this combination technique on several industrial samples where neither confocal nor focus variation is able to provide optimal results.

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Review of the mathematical foundations of data fusion techniques in surface metrology

Cited by 38 publications

References 86 publications

Study of weighted fusion methods for the measurement of surface geometry

Study of weighted fusion methods for the measurement of surface geometry

Multi-scale surface characterization in additive manufacturing using CT

Three-dimensional measurements with a novel technique combination of confocal and focus variation with a simultaneous scan

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