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Tytuł artykułu

Detection of wear parameters using existing sensors in the machines environment to reach higher machine precision

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents methods to plan predictive maintenance for precision assembly tasks. One of the key aspects of this approach is handling the abnormalities during the development phase, i.e. before and during process implementation. The goal is to identify abnormalities which are prone to failure and finding methods to monitor them. To achieve this, an example assembly system is presented. A Failure Mode and Effects Analysis is then applied to this assembly system to show which key elements influence the overall product quality. Methods to monitor these elements are presented. A unique aspect of this approach is exploring additional routines which can be incorporated in the process to identify machine specific problems. As explained within the paper, the Failure Mode and Effects Analysis shows that the resulting quality in a case study from a precision assembly task is dependent on the precision of the rotational axis. Although the rotational axis plays a significant role in the resulting error, it is hard to explicitly find a correlation between its degradation and produced parts. To overcome this, an additional routine is added to the production process, which directly collects information about the rotational axis. In addition to the overall concept, this routine is discussed and its ability to monitor the rotational axis is confirmed in the paper.
Rocznik
Strony
85--96
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
  • TU Braunschweig, Institute of Machine Tools and Production Technology, Braunschweig, Germany
autor
  • TU Braunschweig, Institute of Machine Tools and Production Technology, Braunschweig, Germany
autor
  • TU Braunschweig, Institute of Machine Tools and Production Technology, Braunschweig, Germany
autor
  • TU Braunschweig, Institute of Machine Tools and Production Technology, Braunschweig, Germany
Bibliografia
  • [1] RENTSCHLER U., 1995, Fehlertolerantes Präzisionsfügen, volume 104 of ISW Forschung und Praxis, Berichte aus dem Institut für Steuerungstechnik der Werkzeugmaschinen und Fertigungseinrichtungen der Universität Stuttgart. Springer Berlin Heidelberg, Berlin, Heidelberg and s.l.
  • [2] AHMAD R., KAMARUDDIN S., AZID I., ALMANAR I., 2011, Maintenance management decision model for preventive maintenance strategy on production equipment: Springer.
  • [3] Europäische Norm. Din 13306: Instandhaltung – Begriffe der Instandhaltung, September 2015.
  • [4] STRUNZ M., 2012, Instandhaltung: Grundlagen - Strategien - Werkstätten. Springer, Berlin and Heidelberg.
  • [5] AHMAD R., KAMARUDDIN S., AZID I., ALMANAR I., 2011, Maintenance Management Decision Model for Preventive Maintenance Strategy on Production Equipment, Springer.
  • [6] MATYAS K., 2002, Ganzheitliche Optimierung Durch Individuelle Instandhaltungsstrategien, Industrie Management, 18, 13-16.
  • [7] PARK K.S., 1993, Condition-Based Predictive Maintenance by Multiple Logistic Function, IEEE Transactions on Reliability, 42/4, 556-560.
  • [8] MOBLEY R.K., 2002, An Introduction to Predictive Maintenance, Butterworth-Heinemann, Amsterdam and New York, 2nd ed.
  • [9] PATIL S., GEIKWAD J., 2013, Vibration Analysis of Electrical Rotating Machines Using FFT: A Method of Predictive Maintenance, IEEE, DOI: 10.1109/ICCCNT.2013.6726711.
  • [10] FAYYAD, U.M., 1996, Advances in Knowledge Discovery and Data Mining, Menlo Park, Calif.: AAAI Press; MIT Press.
  • [11] DEMPSEY P., AFJEH A., 2002, Integrating Oil Debris and Vibration Gear Damage Detection Technologies Using Fuzzy Logic, NASA Center for Aerospace Information.
  • [12] VERL A., HEISEL U., WALTHER M., MAIER D., 2009, Sensorless Automated Condition Monitoring for the Control of the Predictive Maintenance of Machine Tools, CIRP Annals – Manufacturing Technology, 58/1, 375-378.
  • [13] HOSHI T., 2006, Damage Monitoring of Ball Bearing, CIRP Annals – Manufacturing Technology, 55/1, 427-430.
  • [14] [ESTER M., SANDER J., Knowledge Discovery in Databases, Techniken und Anwendungen. Berlin, New York: Springer.
  • [15] UHLMANN E., et al., 2017, Intelligent Pattern Recognition of SLM Machine Energy Data, Journal of Machine Engineering, 17/2.
  • [16] UHLMANN E., HOHWIELER E., GEISERT C., 2017, Intelligent Production Systems in the Era of Indutrie 4.0 – Changing Mindsets and Business Models, Journal of Machine Engineering, 17/2.
  • [17] SCHMITT R., RIEDEL J., 2017, A Methodology for a Structured Process Analysis of Complex Production Processes with a Small Database, WGP-Kongress.
  • [18] CHEN K.Y., LEE H.Y., TU S., 2016. Study on User's Satisfaction of Enterprise Resource Planning System - An Example of manufacturing, International Symposium on Computer, Consumer and Control (IEEE), 1010-1011.
  • [19] YANG L., SHEU S.H., 2006, Integrating Multivariate Engineering Process Control and Multivariate Statistical Process Control, Int. J. Adv. Manuf. Technol., 29/1-2, 129-136, DOI: 10.1007/s00170-004-2494-8.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7c5dde82-8b6a-417b-a12a-322ac338243a
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