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Reliability estimation for manufacturing processes

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: of the current research is to develop a reliability assessment method with an extension of the existing ones and pooling them to a common framework. The system must identify the most unreliable parts of a production process and suggest the most efficient ways for the reliability improvement. Design/methodology/approach: FMEA is in the centre of the proposed framework,a reliability analysis type, the most widely used in enterprises. The current research suggests to extend the FMEA by introducing a classification of faults. In this procedure, Bayesian Belief Network is employed to analyze faults. Findings: An integrated modelling method based on a system modelling and complemented with a reliability evaluation mechanism has the capability to analyse and design manufacturing systems. The tool developed to analyse a production process, enables companies to analyse the process as a whole as well as its parts and achieve efficient prognosis for the production process reorganization. Research limitations/implications: The reliability analysis framework is developed for machinery manufacturing enterprises. Practical implications: The reliability assessment tool helps engineers quickly and with accurate estimate most unreliable places of production process and indicates ways of their elimination with great efficiency. Originality/value: Expansion of FMEA method, application of Bayesian Belief Network for process reliability estimation, usage of reliability estimation during production route creation.
Rocznik
Strony
7--13
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
autor
  • Department of Mechanical Engineering, Tallinn University of Technology, Ehitajate tee 5, Tallinn, Estonia
autor
  • Department of Mechanical Engineering, Tallinn University of Technology, Ehitajate tee 5, Tallinn, Estonia
autor
  • Department of Mechanical Engineering, Tallinn University of Technology, Ehitajate tee 5, Tallinn, Estonia
autor
  • UNIDEMI, Department of Mechanical and Industrial Engineering, Faculdade de Ciencias e Tecnologia da Universidade Nova de Lisboa, Portugal
Bibliografia
  • [1] J. Michalska, Quality costs in the production process, Special Issue of the Worldwide Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 425-428.
  • [2] C. Leangsuksun, H. Song, L. Shen, Reliability modeling using UML, Software Engineering Research and Practice 2003, 259-262.
  • [3] T. Karaulova, I. Preis, M. Pribytkova, Process analysis and reliability evaluation, Annals of DAAAM for 2008 & Proceedings of the 19th International DAAAM Symposium, Vienna, 2008.
  • [4] MIL-HDBK-338B Military Handbook, Electronic Reliability Design Handbook, 1998.
  • [5] M. Dudek-Burlikowska, Application of FMEA method in enterprise focused on quality, Journal of Achievements in Materials and Manufacturing Engineering 45/1 (2011) 89-102.
  • [6] K.J. Sharon, Combing QFD and FMEA to optimize performance, ASQC Quality Congress 52 (1998) 564-75.
  • [7] M. Dudek, D. Szewieczek, Usage of quality methods: Failure Mode and Effect Analysis (FMEA) and StaticticalProcess Control (SPC) as a element of continuous improvement of production process, Proceedings of the 12th International Scientific Conference “Achievements in Mechanical and Materials Engineering” AMME’2003, Gliwice-Zakopane, 2003, 317-321.
  • [8] DOE-NE-STD-1004-92, Root cause analysis guidance document US, Downloaded from http://www.everyspec. com/DOE/DOE+PUB S/DOE_NE_STD_1004_92_262, 1992.
  • [9] R.E. Neapolitan, Learning Bayesian networks, Prentice Hall, 2003.
  • [10] E. Shevtshenko, W. Yan, Decision support under uncertainties based on robust Bayesian networks, 2009.
  • [11] A. O'Hagan, Kendall's advanced theory of statistics 2B, Bayesian inference, Arnold, London, 1994.
  • [12] Heckerman, D.A (2006)Tutorial on Learning With Bayesian Networks Technical Report MSR-TR-95-06.
  • [13] Y. Wang, Imprecise probabilities based on generalised intervals for system reliability assessment, International Journal of Reliability and Safety 4/4 (2010) 319-342.
  • [14] M. Neil, N.E. Fenton, S. Forey, R. Harris, Using Bayesian belief networks to predict the reliability of military vehicles, IEE Computing and Control Engineering 12/1 (2001) 11-20.
  • [15] E. Shevtshenko, T. Karaulova, S. Kramarenko, Y. Wang, Manufacturing project management in the conglomerate enterprises supported by IDSS, Journal of Achievements in Materials and Manufacturing Engineering 33/1 (2009) 94-102.
  • [16] E. Shevtshenko, T. Karaulova, S. Kramarenko, Y. Wang, IDSS used as a framework for collaborative projects in conglomerate enterprises, Journal of Achievements in Materials and Manufacturing Engineering 22/1 (2007) 89-92.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cc1f38ea-9404-4c33-942b-f7ac20f96716
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