Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data

Zhenyu Shi, Daniel Apley*, George C. Runger

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Optical dimensional metrology (ODM) technology that produces spatially dense surface measurement data is increasingly employed for quality-control purposes in discrete parts manufacturing. Such data contain a wealth of information on the surface dimensional characteristics of individual parts and on the nature of part-to-part variation. The large body of prior quality-control work on analyzing dimensional metrology data has focused heavily on fitting parametric geometric features such as circles or planes to the data for individual parts and checking whether the features are within specifications; and subsequent analysis of part-to-part variation is restricted to those specific features. In this article, we present an approach for identifying and visualizing the nature of part-to-part variation in a more general manner that is not restricted to a prespecified set of parametric features. The basis for the approach is manifold learning applied to the collective ODM data for a set of measured parts. Particular emphasis is on handling the extremely high dimensionality of ODM data.

Original languageEnglish (US)
Pages (from-to)3-20
Number of pages18
JournalJournal of Quality Technology
Volume51
Issue number1
DOIs
StatePublished - Jan 1 2019

Fingerprint

Quality control
Surface measurement
Specifications
Wealth
Dimensionality
Manufacturing

Keywords

  • Dimensional metrology
  • Independent component analysis
  • Manifold learning
  • Optical metrology
  • Principal component analysis

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

@article{57abbc8db721442ca84ed8495d860123,
title = "Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data",
abstract = "Optical dimensional metrology (ODM) technology that produces spatially dense surface measurement data is increasingly employed for quality-control purposes in discrete parts manufacturing. Such data contain a wealth of information on the surface dimensional characteristics of individual parts and on the nature of part-to-part variation. The large body of prior quality-control work on analyzing dimensional metrology data has focused heavily on fitting parametric geometric features such as circles or planes to the data for individual parts and checking whether the features are within specifications; and subsequent analysis of part-to-part variation is restricted to those specific features. In this article, we present an approach for identifying and visualizing the nature of part-to-part variation in a more general manner that is not restricted to a prespecified set of parametric features. The basis for the approach is manifold learning applied to the collective ODM data for a set of measured parts. Particular emphasis is on handling the extremely high dimensionality of ODM data.",
keywords = "Dimensional metrology, Independent component analysis, Manifold learning, Optical metrology, Principal component analysis",
author = "Zhenyu Shi and Daniel Apley and Runger, {George C.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/00224065.2018.1541380",
language = "English (US)",
volume = "51",
pages = "3--20",
journal = "Journal of Quality Technology",
issn = "0022-4065",
publisher = "American Society for Quality",
number = "1",

}

Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data. / Shi, Zhenyu; Apley, Daniel; Runger, George C.

In: Journal of Quality Technology, Vol. 51, No. 1, 01.01.2019, p. 3-20.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data

AU - Shi, Zhenyu

AU - Apley, Daniel

AU - Runger, George C.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Optical dimensional metrology (ODM) technology that produces spatially dense surface measurement data is increasingly employed for quality-control purposes in discrete parts manufacturing. Such data contain a wealth of information on the surface dimensional characteristics of individual parts and on the nature of part-to-part variation. The large body of prior quality-control work on analyzing dimensional metrology data has focused heavily on fitting parametric geometric features such as circles or planes to the data for individual parts and checking whether the features are within specifications; and subsequent analysis of part-to-part variation is restricted to those specific features. In this article, we present an approach for identifying and visualizing the nature of part-to-part variation in a more general manner that is not restricted to a prespecified set of parametric features. The basis for the approach is manifold learning applied to the collective ODM data for a set of measured parts. Particular emphasis is on handling the extremely high dimensionality of ODM data.

AB - Optical dimensional metrology (ODM) technology that produces spatially dense surface measurement data is increasingly employed for quality-control purposes in discrete parts manufacturing. Such data contain a wealth of information on the surface dimensional characteristics of individual parts and on the nature of part-to-part variation. The large body of prior quality-control work on analyzing dimensional metrology data has focused heavily on fitting parametric geometric features such as circles or planes to the data for individual parts and checking whether the features are within specifications; and subsequent analysis of part-to-part variation is restricted to those specific features. In this article, we present an approach for identifying and visualizing the nature of part-to-part variation in a more general manner that is not restricted to a prespecified set of parametric features. The basis for the approach is manifold learning applied to the collective ODM data for a set of measured parts. Particular emphasis is on handling the extremely high dimensionality of ODM data.

KW - Dimensional metrology

KW - Independent component analysis

KW - Manifold learning

KW - Optical metrology

KW - Principal component analysis

UR - http://www.scopus.com/inward/record.url?scp=85061178768&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061178768&partnerID=8YFLogxK

U2 - 10.1080/00224065.2018.1541380

DO - 10.1080/00224065.2018.1541380

M3 - Article

VL - 51

SP - 3

EP - 20

JO - Journal of Quality Technology

JF - Journal of Quality Technology

SN - 0022-4065

IS - 1

ER -