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Failure mode analysis to define process monitoring systems

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The high costs of using skilled operators in production processes has built a demand for reduced manning, 'lights out machining' manufacture. Process monitoring systems have become a widely researched area in recent years since there is a need for intelligent systems to replace the manual intervention in existing processes. Furthermore, using modern sensors and signal processing techniques, monitoring systems can obtain more informatio about a process and therefore reduce costs further though maximised life of cutting tools, optimised cutting parameters and reduced scrap or re-work. With many application areas available, such as tool condition monitoring, chatter avoidance or feedback control of cutting parameters, it is not always apparent what the key aspects required by an intelligent monitoring system are. In addition, different machining processes have different demands and limitations for monitoring. This paper considers an analytical approach to define the requirements of a monitoring system. A process failure mode effect analysis (FMEA) is carried out to determine the weaknesses of current production processes. From this analysis, the relationships between failures, causes and effects can be used to populate conditional relationships between process faults and sensor signal features in a monitoring system.
Rocznik
Strony
118--129
Opis fizyczny
Bibliogr. 11 poz., tab., rys.
Twórcy
autor
  • Advanced Manufacturing Research Centre with Boeing, University of Sheffield, UK
autor
  • Advanced Manufacturing Research Centre with Boeing, University of Sheffield, UK
Bibliografia
  • [1] BYRNE G., DORNFELD D., INASAKI I., KETTELER G., KONIG W., AND R. TETI, 1995, Tool Condition Monitoring (TCM) -- The Status of Research and Industrial Application, CIRP Annals - Manufacturing Technology, vol. 44/541-567.
  • [2] JANTUNEN E., 2002, A summary of methods applied to tool condition monitoring in drilling, International Journal of Machine Tools & Manufacture, 42/997-1010.
  • [3] REHORN A. G., JIANG J., ORBAN P. E., 2005, State-of-the-art methods and results in tool condition monitoring: a review, International Journal of Advanced Manufacturing Technology, 26/693-710.
  • [4] O'DONNELL G., YOUNG P., KELLY K., BYRNE G., 2001, Towards the improvement of tool condition monitoring systems in the manufacturing environment, Journal of Materials Processing Technology, 119/133-139.
  • [5] LIANG S. Y., HECKER R. L., LANDERS R. G., 2004, Machining process monitoring and control: The state-ofthe- art, Journal of Manufacturing Science and Engineering-Transactions of the Asme, 126/297-310.
  • [6] ABELLAN-NEBOT J. V. SUBIRON F. R., 2010, A review of machining monitoring systems based on artificial intelligence process models, International Journal of Advanced Manufacturing Technology, 47/237-257.
  • [7] RONG M., ZHAO T. D., YU Y., 2008, Advanced Human Factors Process Failure Modes and Effects Analysis, IEEE Annual Reliability and Maintainability Symposium, 366-371.
  • [8] JOHNSON K. G., KHAN M. K., 2003, A study into the use of the process failure mode and effects analysis (PFMEA) in the automotive industry in the UK, Journal of Materials Processing Technology, 139/348-356.
  • [9] MARINESCU I., AXINTE D. A., 2008, A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations, International Journal of Machine Tools & Manufacture, 48/1148-1160.
  • [10] ALAEDDINI A., DOGAN I., 2011, Using Bayesian networks for root cause analysis in statistical process control, Expert Systems with Applications, 38/11230-11243.
  • [11] DEY S., STORI J. A., 2005, A Bayesian network approach to root cause diagnosis of process variations, International Journal of Machine Tools & Manufacture, 45/75-91
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f3b97e6a-b84e-45b0-8a49-ad3006ef0e47
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