IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v144y2013i1p301-315.html
   My bibliography  Save this article

A new multivariate gage R&R method for correlated characteristics

Author

Listed:
  • Peruchi, Rogério Santana
  • Balestrassi, Pedro Paulo
  • de Paiva, Anderson Paulo
  • Ferreira, João Roberto
  • de Santana Carmelossi, Michele

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.

Suggested Citation

  • Peruchi, Rogério Santana & Balestrassi, Pedro Paulo & de Paiva, Anderson Paulo & Ferreira, João Roberto & de Santana Carmelossi, Michele, 2013. "A new multivariate gage R&R method for correlated characteristics," International Journal of Production Economics, Elsevier, vol. 144(1), pages 301-315.
    • Handle: RePEc:eee:proeco:v:144:y:2013:i:1:p:301-315
    DOI: 10.1016/j.ijpe.2013.02.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527313000856
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2013.02.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, S.F. & Rahim, M.A., 2005. "Economic statistical process control for multivariate quality characteristics under Weibull shock model," International Journal of Production Economics, Elsevier, vol. 98(2), pages 215-226, November.
    2. Chen, K.S. & Yu, K.T. & Sheu, S.H., 2006. "Process capability monitoring chart with an application in the silicon-filler manufacturing process," International Journal of Production Economics, Elsevier, vol. 103(2), pages 565-571, October.
    3. Wu, Chien-Wei & Pearn, W.L. & Kotz, Samuel, 2009. "An overview of theory and practice on process capability indices for quality assurance," International Journal of Production Economics, Elsevier, vol. 117(2), pages 338-359, February.
    4. Chen, K.S. & Chen, T.W., 2008. "Multi-process capability plot and fuzzy inference evaluation," International Journal of Production Economics, Elsevier, vol. 111(1), pages 70-79, January.
    5. Pan, Xia & Jarrett, Jeffrey, 2007. "Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation," International Journal of Production Economics, Elsevier, vol. 106(1), pages 204-216, March.
    6. Machado, Marcela A.G. & Costa, Antonio F.B., 2008. "The double sampling and the EWMA charts based on the sample variances," International Journal of Production Economics, Elsevier, vol. 114(1), pages 134-148, July.
    7. Chen, Shaw K. & Mangiameli, Paul & Roethlein, Christopher J., 2005. "Predicting the output of a tube-bending process: A case study," International Journal of Production Economics, Elsevier, vol. 95(3), pages 307-316, March.
    8. Wang, Wenbin, 2012. "A simulation-based multivariate Bayesian control chart for real time condition-based maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 218(3), pages 726-734.
    9. Pan, J.N., 2007. "A study of multivariate pre-control charts," International Journal of Production Economics, Elsevier, vol. 105(1), pages 160-170, January.
    10. Villalobos, J. Rene & Munoz, Luis & Gutierrez, Marco A., 2005. "Using fixed and adaptive multivariate SPC charts for online SMD assembly monitoring," International Journal of Production Economics, Elsevier, vol. 95(1), pages 109-121, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. de Oliveira, Lucas Guedes & Aquila, Giancarlo & Balestrassi, Pedro Paulo & de Paiva, Anderson Paulo & de Queiroz, Anderson Rodrigo & de Oliveira Pamplona, Edson & Camatta, Ulisses Pessin, 2020. "Evaluating economic feasibility and maximization of social welfare of photovoltaic projects developed for the Brazilian northeastern coast: An attribute agreement analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    2. Mohammed Abduljaleel & Habshah Midi & Mostafa Karimi, 2019. "Outlier Detection In The Analysis Of Nested Gage R&R, Random Effect Model," Statistics in Transition New Series, Polish Statistical Association, vol. 20(3), pages 31-56, September.
    3. Abduljaleel Mohammed & Midi Habshah & Karimi Mostafa, 2019. "Outlier Detection In The Analysis Of Nested Gage R&R, Random Effect Model," Statistics in Transition New Series, Statistics Poland, vol. 20(3), pages 31-56, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pan, Xia & Jarrett, Jeffrey, 2007. "Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation," International Journal of Production Economics, Elsevier, vol. 106(1), pages 204-216, March.
    2. Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
    3. Kuen-Suan Chen & Hsi-Tien Chen & Tsang-Chuan Chang, 2017. "The construction and application of Six Sigma quality indices," International Journal of Production Research, Taylor & Francis Journals, vol. 55(8), pages 2365-2384, April.
    4. Jeffrey Jarrett, 2014. "The quality movement in hospital care," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3153-3167, November.
    5. Kuen-Suan Chen & Tsang-Chuan Chang & Chien-Che Huang, 2020. "Supplier Selection by Fuzzy Assessment and Testing for Process Quality under Consideration with Data Imprecision," Mathematics, MDPI, vol. 8(9), pages 1-14, August.
    6. Wang, Ching-Hsin & Chen, Kuen-Suan, 2020. "New process yield index of asymmetric tolerances for bootstrap method and six sigma approach," International Journal of Production Economics, Elsevier, vol. 219(C), pages 216-223.
    7. Costa, Antonio Fernando Branco & Machado, Marcela Aparecida Guerreiro, 2011. "Variable parameter and double sampling charts in the presence of correlation: The Markov chain approach," International Journal of Production Economics, Elsevier, vol. 130(2), pages 224-229, April.
    8. Pan, J.N., 2007. "A study of multivariate pre-control charts," International Journal of Production Economics, Elsevier, vol. 105(1), pages 160-170, January.
    9. Wu, Chien-Wei, 2012. "An efficient inspection scheme for variables based on Taguchi capability index," European Journal of Operational Research, Elsevier, vol. 223(1), pages 116-122.
    10. Ching-Hsin Wang & Feng-Chia Li, 2020. "Economic design under gamma shock model of the control chart for sustainable operations," Annals of Operations Research, Springer, vol. 290(1), pages 169-190, July.
    11. Chien-Wei Wu & Ming-Hung Shu & Pei-An Wang & Bi-Min Hsu, 2021. "Variables skip-lot sampling plans on the basis of process capability index for products with a low fraction of defectives," Computational Statistics, Springer, vol. 36(2), pages 1391-1413, June.
    12. Huwang, Longcheen & Huang, Chun-Jung & Wang, Yi-Hua Tina, 2010. "New EWMA control charts for monitoring process dispersion," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2328-2342, October.
    13. Hasan Rasay & Seyed Mohammad Hadian & Farnoosh Naderkhani & Fariba Azizi, 2024. "Optimal condition based maintenance using attribute Bayesian control chart," Journal of Risk and Reliability, , vol. 238(4), pages 754-763, August.
    14. Liu, J.Y. & Xie, M. & Goh, T.N. & Liu, Q.H. & Yang, Z.H., 2006. "Cumulative count of conforming chart with variable sampling intervals," International Journal of Production Economics, Elsevier, vol. 101(2), pages 286-297, June.
    15. Kampitsis, Dimitris & Panagiotidou, Sofia, 2022. "A Bayesian condition-based maintenance and monitoring policy with variable sampling intervals," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    16. Li, Der-Chiang & Lin, Liang-Sian, 2013. "A new approach to assess product lifetime performance for small data sets," European Journal of Operational Research, Elsevier, vol. 230(2), pages 290-298.
    17. Roberto Campos Leoni & Marcela Aparecida Guerreiro Machado & Antonio Fernando Branco Costa, 2016. "The T -super-2 chart with mixed samples to control bivariate autocorrelated processes," International Journal of Production Research, Taylor & Francis Journals, vol. 54(11), pages 3294-3310, June.
    18. Sabri-Laghaie, Kamyar & Fathi, Mahdi & Zio, Enrico & Mazhar, Maryam, 2022. "A novel reliability monitoring scheme based on the monitoring of manufacturing quality error rates," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    19. A. Snoussi, 2011. "SPC for short-run multivariate autocorrelated processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2303-2312.
    20. Marta Benková & Dagmar Bednárová & Gabriela Bogdanovská, 2024. "Process Capability Evaluation Using Capability Indices as a Part of Statistical Process Control," Mathematics, MDPI, vol. 12(11), pages 1-29, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:144:y:2013:i:1:p:301-315. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.