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Real-time tool condition monitoring in milling by means of control charts for auto-correlated data

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Real time monitoring of tool condirions and machining processes has been extensively studies in tne last decades, but a wide gap is stiil present between research activities and commercial tools. One of the factors which currently limit the utilization of these systems is the low flexibility of off-the-shelf solutions: in most cases they need dedicated off-line training sessions to acquire the reference patterns and thresholds, and/or the need for several input data to be defined a priori by a human operator. Instead of exploiting off-line learning sessions and a prior defined thresholds, this paper proposes an approach for automatic modelling of a cutting process and real-time monitoring of its stability that is based only on data acquired on-line during the process itself. This approach avoids any a-priori assumption about expected signal patterns, and it is characterized by an innovative implementation of well known Statistical Process Control techniques. In particular, with regard to milling processes, the paper proposes the utilization of cross-correlation coefficient between repeating signal profiles as the feature to be monitored, and an EWMA (Exponentially Weighted Moving Average) control chart for auto-correlated data as monitoring tool.
Rocznik
Strony
5--17
Opis fizyczny
Bibliogr. 14 poz., tab., rys.
Twórcy
  • Politecnico di Milano, Dipartimento di Meccanica, Via La Masa 34, 20156 Milano, Italy
autor
  • Politecnico di Milano, Dipartimento di Meccanica, Via La Masa 34, 20156 Milano, Italy
autor
  • MUSP Research Centre, Via Tirotti s/n, Loc. Le Mose, 29122 Piacenza, Italy
Bibliografia
  • [1] AKAIKE H., 1974, A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19/6/716–723.
  • [2] COLOSIMO B.M., PACELLA M., 2007, On the use of principal component analysis to identify systematic patterns in roundness profiles, Quality and reliability engineering international, 23/707 -725.
  • [3] COLOSIMO B.M., SEMERARO Q., PACELLA M., 2008, Statistical process control for geometric specifications: on the monitoring of roundness profiles, Journal of quality technology, 40/1/1-18.
  • [4] GARDNER M.M., LU J-C., GYURCSIK R.S., HORNUG B.E., RAO S., 1997, Equipment fault detection using spatial signatures, IEEE Transactions on components, packaging, and manufacturing technology – part C, 20/4.
  • [5] HERMANN G., 2003, Application of Neural Network Based Sensor Fusion in Drill Monitoring, Proc. of Symposium on Applied Machine Intelligence, Herlany, Slovakia, 11-24.
  • [6] JEONG M. K., LU J.-C., WANG N., 2006, Wavelet-based SPC procedure for complicated functional data, International Journal of Production Research, 44/8/1653-1653(1).
  • [7] KUO R.J., 2000, Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network, Engineering Applications of Artificial Intelligence, 13/249-261.
  • [8] LOMBARDO A., MASNATA A., SETTINERI L., 1997, In-process tool-failure detection by means of AR models, The International Journal of Advanced Manufacturing Technology, 13/2/86 - 94.
  • [9] MONTGOMERY D. C., 2008, Introduction to statistical quality control, John Wiley & Sons, Ed. 6.
  • [10] MONTGOMERY D.C., MASTRANGELO C.M., 1991, Some statistical process control methods for autocorrelated data, Journal of Quality Technology, 23/3/179-204.
  • [11] SONG D. Y., OHARA Y., TAMAKI H., SUGA M., 2009, Tool wear monitoring using time series analysis, Journal of Solid Mechanics and Materials Engineering, 3/4/635 - 646.
  • [12] WILLIAMS J.D., WOODALL W.H., BIRCH J.B., 2003, Phase 1 Monitoring of Nonlinear Profiles, Quality and Productivity Research Conference, Yorktown Heights, NY.
  • [13] WOODALL, W. H., 2007, Current research on profile monitoring. Produção, 17/3/420-425.
  • [14] ZHOU S., JIN N., JIN J., 2005, Cycle-based signal monitoring using a directionally variant multivariate control chart system, IEEE Transaction on Quality and Reliability, 37/971 – 982.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1ab342a0-44d2-421d-9c5e-5f6392ec1161
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