A new multivariate gage R&R method for correlated characteristics

Rogério Santana Peruchi, Pedro Paulo Balestrassi, Anderson Paulo de Paiva, João Roberto Ferreira and Michele de Santana Carmelossi

International Journal of Production Economics, 2013, vol. 144, issue 1, 301-315

Abstract: This article explores how measurement systems having correlated characteristics are analyzed through studies of gage repeatability and reproducibility (GR&R). The main contribution of this research is the proposal of a method for multivariate analysis of a measurement system, a method that considers the weighted principal components (WPC). To prove its efficiency, what was first evaluated were the measurements of the roughness parameters obtained from AISI 12L14 steel turning machined with carbide tools. This GR&R study considers 12 parts, 3'operators, 4'replicates, and 5'responses (Ra, Ry, Rz, Rq and Rt). The data set has a correlation structure that determines 86.2% of explanation for the first principal component. As another step in proving the method's efficiency, the study generates simulated data with different correlation structures for measurement systems classified as acceptable, marginal, and unacceptable. The proposed method is compared with classical univariate and multivariate methods. It was observed that, compared to the other methods, the WPC was more robust in estimating the assessment indexes of a multivariate measurement system.

Keywords: Measurement system analysis; Repeatability and reproducibility; Principal component analysis; Roughness (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0925527313000856
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:144:y:2013:i:1:p:301-315

DOI: 10.1016/j.ijpe.2013.02.018

Access Statistics for this article

International Journal of Production Economics is currently edited by Stefan Minner

More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().