Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling

Lu, Wenlong; Pagani, Luca; Zhou, Liping; Liu, Xiaojun; Wang, Jian; Leach, Richard; Jiang, Xiangqian

doi:10.1016/j.precisioneng.2018.09.002

Cited by 29 publications

(13 citation statements)

References 57 publications

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“…What are the latest trends for uncertainty and traceability, especially in the context of a digital manufacturing setup? Uncertainty remains a difficult active area of research that continues to present a series of complex challenges [6,27,[123][124][125][126][127][128][129][130][131]. In the future, methods of traceability and calibration are expected to be incorporated into inline and on-machine measurement processes.…”

Section: Discussionmentioning

confidence: 99%

“…Common solutions will include the use of calibration artefacts to achieve reliability [6] and self-calibration methods within manufacturing [27,126], to better accommodate the specific setup and environment in which the equipment is operating. Additionally, standardised procedures and methods are expected to be developed for integrated, metrology-specific data processing applications, such as sampling strategy [127,128], defect identification and handling [129][130][131], and data acquisition and analysis.…”

Section: The Implication Of Measurement Uncertaintymentioning

confidence: 99%

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Optical metrology for digital manufacturing: a review

Catalucci

Thompson

Piano

et al. 2022

Int J Adv Manuf Technol

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With the increasing adoption of Industry 4.0, optical metrology has experienced a significant boom in its implementation, as an ever-increasing number of manufacturing processes are overhauled for in-process measurement and control. As such, optical metrology for digital manufacturing is currently a hot topic in manufacturing research. Whilst contact coordinate measurement solutions have been adopted for many years, the current trend is to increasingly exploit the advantages given by optical measurement technologies. Smart automated non-contact inspection devices allow for faster cycle times, reducing the inspection time and having a continuous monitoring of process quality. In this paper, a review for the state of the art in optical metrology is presented, highlighting the advantages and impacts of the integration of optical coordinate and surface texture measurement technologies in digital manufacturing processes. Also, the range of current software and hardware technologies for digital manufacturing metrology is discussed, as well as strategies for zero-defect manufacturing for greater sustainability, including examples and in-depth discussions of additive manufacturing applications. Finally, key current challenges are identified relating to measurement speed and data-processing bottlenecks; geometric complexity, part size and surface texture; user-dependent constraints, harsh environments and uncertainty evaluation.

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Section: Discussionmentioning

confidence: 99%

Section: The Implication Of Measurement Uncertaintymentioning

confidence: 99%

Optical metrology for digital manufacturing: a review

Catalucci

Thompson

Piano

et al. 2022

Int J Adv Manuf Technol

Self Cite

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“…Blind sampling methods, such as the uniform distributed method (UDM), Halton-Zaremba sequence sampling, Hammersley sequence sampling and random sampling are commonly used [11,12]. However, these methods do not consider the curvature characteristics and shape complexity of the surface.…”

Section: A Blind Sampling Methodsmentioning

confidence: 99%

“…Zahmati [21] used particle swarm method to optimize the sampling points. And Lu [22] et al applied a free-knotted B-spline to the parameter uncertainty model.…”

Section: B Adaptive Sampling Methodsmentioning

confidence: 99%

Sampling Point Planning for Complex Surface Inspection based on Feature Points under Area Division

Sun¹,

Xiang

Zhou³

et al. 2022

Preprint

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Traditional sampling point planning methods usually apply the same sampling strategy to all areas of the complex surface. It is very likely to miss the extreme points in areas with large curvature changes, which leads to low fitting accuracy. This paper proposes a new sampling method for complex surfaces based on feature points under area division. First, the curvature characteristics of the complex surface is analyzed, and the complex surface is divided into flat and sharply-edged areas. Then, for the flat areas the uniform distribution method is used, and for the sharply-edged areas, the feature points are defined and searched, and the curvature adaptive planning method based on the feature points is adopted. Finally, the repeated and redundant points are optimized and adjusted. The experimental verification results show that compared with four traditional sampling methods, the maximum and mean fitting errors of the proposed method are significantly reduced and the measuring accuracy is efficiently improved.

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“…The error-guided sampling methods analyze the machine error, reconstruction error, reconstruction uncertainty, and other errors and utilize the error analysis results to determine critical points. These methods typically first generate the initial point set by a simple method and then select the next-best point from the target curve or surface iteratively; this process continues until the desired sampling scale or inspection performance is achieved [11][12][13][14][15]. The hybrid sampling methods use optimization methods or other strategies, such as genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, and bending moment balance theory, to obtain inspection points.…”

Section: Introductionmentioning

confidence: 99%

Efficient Adaptive Sampling Methods Based on Deviation Analysis of Piecewise Cubic Spline Interpolation

Cheng¹,

zeng²,

Jiang³

et al. 2021

Preprint

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The on-machine inspection technique requires a certain manufacturing time, so it is important for a sampling approach to achieve high precision for a fixed number of inspection points. This study designs an efficient adaptive sampling method for the non-uniform rational basis spline (NURBS) curves and surfaces based on deviation analysis. For the free-form curves, it is an iterative method that is used to remove points that are less significant to the reconstruction error from the dense points on the curve. That is, the points are ranked by their maximum deviation from the theoretical curves. Different from the existing methods, a closed-form is derived to approximate the maximum deviation by analyzing the curve reconstruction method, i.e., piecewise cubic spline interpolation. The proposed method is compared with recent curve sampling methods, and the comparison results have shown that, under the same number of inspection points, the reconstruction error of the proposed method is reduced by 82%. The proposed curve sampling algorithm is then further extended to surface sampling, where the global characteristics of a surface are extracted as a series of curves on the surface. Thus, surface sampling is simplified to curve sampling in two directions. The proposed surface sampling strategy is compared with classic surface sampling methods using three representative surfaces. The results show that by using the proposed surface sampling strategy, the reconstruction error is reduced significantly. By applying our sampling method to the on-machine inspection system, the inspection accuracy can be greatly improved.

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Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling

Cited by 29 publications

References 57 publications

Optical metrology for digital manufacturing: a review

Optical metrology for digital manufacturing: a review

Sampling Point Planning for Complex Surface Inspection based on Feature Points under Area Division

Efficient Adaptive Sampling Methods Based on Deviation Analysis of Piecewise Cubic Spline Interpolation

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