Measurement of bivariate attributes using a novel statistical model

Jrjung Lyu, Ming Nan Chen

Research output: Contribution to journalArticle

Abstract

Reducing process variability is essential to many organisations. According to the pertinent literature, a quality system that utilizes quality techniques to reduce process variability is necessary. Quality programs that respond to measurement precision are central to quality systems, and the most common method of assessing the precision of a measurement system is repeatability and reproducibility (R&R). Few studies have investigated R&R using attribute data. In modern manufacturing environments, automated manufacturing is becoming increasingly common; however, a measurement resolution problem exists in automatic inspection equipment, resulting in clusters and product defects. It is vital to monitor effectively these bivariate quality characteristics. This study presents a novel model for calculating R&R for bivariate attribute data. An alloy manufacturing case is utilized to illustrate the process and potential of the proposed model. Findings can be employed to evaluate and improve measurement systems with bivariate attribute data.

Original languageEnglish
Pages (from-to)1319-1334
Number of pages16
JournalJournal of Applied Statistics
Volume37
Issue number8
DOIs
Publication statusPublished - 2010 Aug 20

Fingerprint

Statistical Model
Attribute
Manufacturing
Measurement System
Repeatability
Reproducibility
Inspection
Monitor
Defects
Statistical model
Necessary
Evaluate
Quality system
Measurement system
Model
Quality programmes
Quality characteristics

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

@article{91971cb6dd43453db34532ed32d6d722,
title = "Measurement of bivariate attributes using a novel statistical model",
abstract = "Reducing process variability is essential to many organisations. According to the pertinent literature, a quality system that utilizes quality techniques to reduce process variability is necessary. Quality programs that respond to measurement precision are central to quality systems, and the most common method of assessing the precision of a measurement system is repeatability and reproducibility (R&R). Few studies have investigated R&R using attribute data. In modern manufacturing environments, automated manufacturing is becoming increasingly common; however, a measurement resolution problem exists in automatic inspection equipment, resulting in clusters and product defects. It is vital to monitor effectively these bivariate quality characteristics. This study presents a novel model for calculating R&R for bivariate attribute data. An alloy manufacturing case is utilized to illustrate the process and potential of the proposed model. Findings can be employed to evaluate and improve measurement systems with bivariate attribute data.",
author = "Jrjung Lyu and Chen, {Ming Nan}",
year = "2010",
month = "8",
day = "20",
doi = "10.1080/02664760903030221",
language = "English",
volume = "37",
pages = "1319--1334",
journal = "Journal of Applied Statistics",
issn = "0266-4763",
publisher = "Routledge",
number = "8",

}

Measurement of bivariate attributes using a novel statistical model. / Lyu, Jrjung; Chen, Ming Nan.

In: Journal of Applied Statistics, Vol. 37, No. 8, 20.08.2010, p. 1319-1334.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Measurement of bivariate attributes using a novel statistical model

AU - Lyu, Jrjung

AU - Chen, Ming Nan

PY - 2010/8/20

Y1 - 2010/8/20

N2 - Reducing process variability is essential to many organisations. According to the pertinent literature, a quality system that utilizes quality techniques to reduce process variability is necessary. Quality programs that respond to measurement precision are central to quality systems, and the most common method of assessing the precision of a measurement system is repeatability and reproducibility (R&R). Few studies have investigated R&R using attribute data. In modern manufacturing environments, automated manufacturing is becoming increasingly common; however, a measurement resolution problem exists in automatic inspection equipment, resulting in clusters and product defects. It is vital to monitor effectively these bivariate quality characteristics. This study presents a novel model for calculating R&R for bivariate attribute data. An alloy manufacturing case is utilized to illustrate the process and potential of the proposed model. Findings can be employed to evaluate and improve measurement systems with bivariate attribute data.

AB - Reducing process variability is essential to many organisations. According to the pertinent literature, a quality system that utilizes quality techniques to reduce process variability is necessary. Quality programs that respond to measurement precision are central to quality systems, and the most common method of assessing the precision of a measurement system is repeatability and reproducibility (R&R). Few studies have investigated R&R using attribute data. In modern manufacturing environments, automated manufacturing is becoming increasingly common; however, a measurement resolution problem exists in automatic inspection equipment, resulting in clusters and product defects. It is vital to monitor effectively these bivariate quality characteristics. This study presents a novel model for calculating R&R for bivariate attribute data. An alloy manufacturing case is utilized to illustrate the process and potential of the proposed model. Findings can be employed to evaluate and improve measurement systems with bivariate attribute data.

UR - http://www.scopus.com/inward/record.url?scp=77955614654&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955614654&partnerID=8YFLogxK

U2 - 10.1080/02664760903030221

DO - 10.1080/02664760903030221

M3 - Article

VL - 37

SP - 1319

EP - 1334

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

SN - 0266-4763

IS - 8

ER -