Intelligent Sampling for the Measurement of Structured Surfaces

J. Wang, X. Jiang, L. A. Blunt, P. J. Scott, R. K. Leach

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Uniform sampling in metrology has known drawbacks such as coherent spectral aliasing and a lack of efficiency in terms of measuring time and data storage. The requirement for intelligent sampling strategies has been outlined over recent years, particularly where the measurement of structured surfaces is concerned. Most of the present research on intelligent sampling has focused on dimensional metrology using coordinate-measuring machines with little reported on the area of surface metrology. In the research reported here, potential intelligent sampling strategies for surface topography measurement of structured surfaces are investigated by using numerical simulation and experimental verification. The methods include the jittered uniform method, low-discrepancy pattern sampling and several adaptive methods which originate from computer graphics, coordinate metrology and previous research by the authors. By combining the use of advanced reconstruction methods and feature-based characterization techniques, the measurement performance of the sampling methods is studied using case studies. The advantages, stability and feasibility of these techniques for practical measurements are discussed.

Original languageEnglish
Article number085006
JournalMeasurement Science and Technology
Volume23
Issue number8
DOIs
Publication statusPublished - 10 Jul 2012

Fingerprint

Metrology
sampling
Sampling
Sampling Strategy
metrology
Coordinate Measuring Machine
Surface Topography
Aliasing
Performance Measurement
Data Storage
Adaptive Method
Sampling Methods
Computer graphics
Discrepancy
computer graphics
data storage
Numerical Simulation
Coordinate measuring machines
Requirements
topography

Cite this

@article{83df5ca67ef243d88239ef1b01a46cbd,
title = "Intelligent Sampling for the Measurement of Structured Surfaces",
abstract = "Uniform sampling in metrology has known drawbacks such as coherent spectral aliasing and a lack of efficiency in terms of measuring time and data storage. The requirement for intelligent sampling strategies has been outlined over recent years, particularly where the measurement of structured surfaces is concerned. Most of the present research on intelligent sampling has focused on dimensional metrology using coordinate-measuring machines with little reported on the area of surface metrology. In the research reported here, potential intelligent sampling strategies for surface topography measurement of structured surfaces are investigated by using numerical simulation and experimental verification. The methods include the jittered uniform method, low-discrepancy pattern sampling and several adaptive methods which originate from computer graphics, coordinate metrology and previous research by the authors. By combining the use of advanced reconstruction methods and feature-based characterization techniques, the measurement performance of the sampling methods is studied using case studies. The advantages, stability and feasibility of these techniques for practical measurements are discussed.",
keywords = "Feature Characterization, Intelligent Sampling, Reconstruction, Sampling Strategies, Structured Surfaces",
author = "J. Wang and X. Jiang and Blunt, {L. A.} and Scott, {P. J.} and Leach, {R. K.}",
year = "2012",
month = "7",
day = "10",
doi = "10.1088/0957-0233/23/8/085006",
language = "English",
volume = "23",
journal = "Measurement Science and Technology",
issn = "0957-0233",
publisher = "IOP Publishing",
number = "8",

}

Intelligent Sampling for the Measurement of Structured Surfaces. / Wang, J.; Jiang, X.; Blunt, L. A.; Scott, P. J.; Leach, R. K.

In: Measurement Science and Technology, Vol. 23, No. 8, 085006, 10.07.2012.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Intelligent Sampling for the Measurement of Structured Surfaces

AU - Wang, J.

AU - Jiang, X.

AU - Blunt, L. A.

AU - Scott, P. J.

AU - Leach, R. K.

PY - 2012/7/10

Y1 - 2012/7/10

N2 - Uniform sampling in metrology has known drawbacks such as coherent spectral aliasing and a lack of efficiency in terms of measuring time and data storage. The requirement for intelligent sampling strategies has been outlined over recent years, particularly where the measurement of structured surfaces is concerned. Most of the present research on intelligent sampling has focused on dimensional metrology using coordinate-measuring machines with little reported on the area of surface metrology. In the research reported here, potential intelligent sampling strategies for surface topography measurement of structured surfaces are investigated by using numerical simulation and experimental verification. The methods include the jittered uniform method, low-discrepancy pattern sampling and several adaptive methods which originate from computer graphics, coordinate metrology and previous research by the authors. By combining the use of advanced reconstruction methods and feature-based characterization techniques, the measurement performance of the sampling methods is studied using case studies. The advantages, stability and feasibility of these techniques for practical measurements are discussed.

AB - Uniform sampling in metrology has known drawbacks such as coherent spectral aliasing and a lack of efficiency in terms of measuring time and data storage. The requirement for intelligent sampling strategies has been outlined over recent years, particularly where the measurement of structured surfaces is concerned. Most of the present research on intelligent sampling has focused on dimensional metrology using coordinate-measuring machines with little reported on the area of surface metrology. In the research reported here, potential intelligent sampling strategies for surface topography measurement of structured surfaces are investigated by using numerical simulation and experimental verification. The methods include the jittered uniform method, low-discrepancy pattern sampling and several adaptive methods which originate from computer graphics, coordinate metrology and previous research by the authors. By combining the use of advanced reconstruction methods and feature-based characterization techniques, the measurement performance of the sampling methods is studied using case studies. The advantages, stability and feasibility of these techniques for practical measurements are discussed.

KW - Feature Characterization

KW - Intelligent Sampling

KW - Reconstruction

KW - Sampling Strategies

KW - Structured Surfaces

UR - http://www.scopus.com/inward/record.url?scp=84863812082&partnerID=8YFLogxK

U2 - 10.1088/0957-0233/23/8/085006

DO - 10.1088/0957-0233/23/8/085006

M3 - Article

VL - 23

JO - Measurement Science and Technology

JF - Measurement Science and Technology

SN - 0957-0233

IS - 8

M1 - 085006

ER -