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Tytuł artykułu

Application of Special Cause Control Charts to Green Sand Process

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Statistical Process Control (SPC) based on the well known Shewhart control charts, is widely used in contemporary manufacturing industry, including many foundries. However, the classic SPC methods require that the measured quantities, e.g. process or product parameters, are not auto-correlated, i.e. their current values do not depend on the preceding ones. For the processes which do not obey this assumption the Special Cause Control (SCC) charts were proposed, utilizing the residual data obtained from the time-series analysis. In the present paper the results of application of SCC charts to a green sand processing system are presented. The tests, made on real industrial data collected in a big iron foundry, were aimed at the comparison of occurrences of out-of-control signals detected in the original data with those appeared in the residual data. It was found that application of the SCC charts reduces numbers of the signals in almost all cases It is concluded that it can be helpful in avoiding false signals, i.e. resulting from predictable factors.
Rocznik
Strony
55--60
Opis fizyczny
Bibliogr. 13 poz., tab., wykr.
Twórcy
autor
  • Institute of Manufacturing Technologies, Warsaw University of Technology, ul. Narbutta 85, 02-524 Warszawa, Poland
  • Institute of Manufacturing Technologies, Warsaw University of Technology, ul. Narbutta 85, 02-524 Warszawa, Poland
Bibliografia
  • [1] Alwan L.C. & Roberts H.V. (1988). Time-series modelling for statistical process control. Journal of Business & Economic Statistics. 6(1), 87–95.
  • [2] Price, B. Price, K. & Enright, T.P. (1992). SPC Modifications for Continuous Autocorrelated Processes. Manufacturing Review. 5, Sep. 184-192.
  • [3] Berthouex, P.M., Hunter, W.G. & Pallesen, L. (1978). Monitoring Sewage-Treatment Plants - Some Quality Control Aspects. Journal of Quality Technology. 10, 139-149.
  • [4] Harris, T.J. & Ross, W.H. Statistical Process Control Procedures for Correlated Observations, Canadian Journal of Chemical Engineering. 69.
  • [5] Montgomery, D.C. & Mastrangelo, C.M. (1991). Some Statistical Process Control Methods for Autocorrelated Data, Journal of Quality Technology. 23, 179-193.
  • [6] MacGregor, J.F. & Harris, T.J. (1993). The Exponentially Weighted Moving Variance. Journal of Quality Technology. 25, 106-118.
  • [7] Wardell, D.G., Moskowitz, H. & Plante, R.D. (1992). Control charts in the presence of data correlation. Management Science. 38, 1084-1105.
  • [8] Green, M.E. (2009). Critical Assessment of Current Metalcasting Green Sand System Control and Monitoring Processes (A Thesis in Industrial Engineering), The Pennsylvania State University The Graduate School Harold and Inge Marcus Department of Industrial and Manufacturing Engineering.
  • [9] Masters, T. (1996). Neural networks in practice. WNT Warszawa. (in Polish).
  • [10] Perzyk, M., Krawiec, K. & Kozłowski, J. (2009). Application of time-series analysis in foundry production, Archives of Foundry Engineering. 9(3), 109-114.
  • [11] Perzyk, M. Maciejak, P.S. & Kozłowski, J. (2011). Application of time-series analysis for prediction of molding sand properties in production cycle. Archives of Foundry Engineering. 11(2), 95-100.
  • [12] Perzyk, M. & Rodziewicz, A. (2012). Application of Time series Analysis in Control of Chemical Composition of Grey Cast Iron. Archives of Foundry Engineering. 12(4), 171-175.
  • [13] Stanley, G.M. (2013). Guide to Fault Detection and Diagnosis. Retrieved April 26, 2015, from: http://gregstanleyandassociates.com/whitepapers/FaultDiagn osis/faultdiagnosis.htm.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a871c0ff-0d68-4cfb-a2cd-e1bb335da40e
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