BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Kocsi Balázs (University of Debrecen, Debrecen, Hungary), Oláh Judit (University of Debrecen, Debrecen, Hungary)
Tytuł
Potential Connections of Unique Manufacturing and Industry 4.0
Potencjalne połączenie produkcji jednostkowej oraz Industry 4.0
Potenzielle Verbindung von Einzelproduktion mit dem Industry 4.0-Konzept
Źródło
LogForum, 2017, vol. 13, nr 4, s. 389-400, rys., tab., bibliogr. 36 poz.
Słowa kluczowe
Przemysł, Rewolucja przemysłowa, Produkcja, Proces produkcji, Przemysł 4.0
Industry, Industrial revolution, Production, Production process, Industry 4.0
Uwagi
summ., streszcz., zfsg., This research was partially supported by the Pallas Athene Domus Scientiae Foundation. The views expressed are those of the authors and do not necessarily reflect the official opinion of the Pallas Athene Domus Scientiae Foundation.
Abstrakt
Wstęp: W oparciu o koncepcję Industry 4.0, nazywaną również czwartą rewolucją przemysłową, procesy produkcyjne są zoptymalizowane przy użyciu maszyn, połączonych ze sobą przez inteligentne systemy komunikacyjne (urządzenia rejestrują przebieg procesu i dostosowują odpowiednio swoje działanie). Celem tego badania było zwiększenie niezawodności procesu w połączeniu ze skróceniem czasu produkcji, a co z tym związane, niższymi kosztami produkcyjnymi.
Metody: Poddano testom możliwość użycia robota w procesie obróbki cięciem produkcji unikatowych mebli drewnianych.
Wyniki: Obecnie zastosowanie robotów w produkcji ma uzasadnienie ekonomiczne tylko w przypadku produkcji masowej. W celu sprawdzenia, w którym etapie obróbki mebla można zastosować robota oraz jaki problemy były by możliwe do rozwiązania przy takim sposobie produkcji, w pierwszym etapie ukształtowano proces w oparciu o analizę błędów i osiągnięć (Failure Mode and Effects Analysis). Analizując potencjalne możliwości niepowodzenia procesu, podjęto próbę użycia ramienia robota jako miernika poprawy. Ramię to zostało zaprogramowane w środowisku komputerowym. Parametry ramienia zostały ustawione przy użyciu oprogramowania Mitsubishi RV-2AJ Cosimir Educational. Następnie przeprowadzono symulację mierząc całkowity czas produkcji oraz koszty produkcji przy użyciu ramienia robota.
Wnioski: Zastosowanie robotów jest uzasadnioną opcją w systemie produkcji jednostkowej, gdyż jako inteligentne urządzenie, jest on w stanie identyfikować problemy nawet u samego źródła ich powstawania. (abstrakt oryginalny)

Background: Based on the concept of Industry 4.0, or the fourth industrial revolution, production processes are optimised by machines connected to each other via intelligent communication systems (machines keep track of the process and adjust their own settings accordingly). Our objective was to achieve more reliable processes with shorter production times and, consequently, lower production costs.
Methods: We examined the possibility of incorporating a robot into the panel cutting subprocess of the unique furniture manufacturing process of a timber company.
Results: Currently, using robots in industrial practice is economical only in the case of mass production. Using robots in unique manufacturing calls for higher resource need. In order to examine which part of the furniture manufacturing process a robot can be incorporated into and what problems can be solved with the robotic arm, the first step is to look for any potential failures in the process, as well as causes of failure, by performing a process model-based Failure Mode and Effects Analysis. Following the exploration of potential causes of failure, we examined the possibility of involving a robotic arm as a measure of improvement. Accordingly, the robotic arm was programmed in a computerized environment. The parameters of the robotic arm were set using the software Mitsubishi RV-2AJ Cosimir Educational. As a next step, process simulation was used to examine the total production time and cost of the process with using the robotic arm.
Conclusions: The implementation of robots is a relevant option in unique production systems, as an intelligent system is capable of identifying problems even at the origin of failures and therefore it allows to avoid delay and increase the precision of operation. (original abstract)
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. Bildstein A., Seidelmann J., 2014. Industrie 4.0-Readiness: Migration zur Industrie 4.0-Fertigung. [Migration to industrial production 4.0] in: Bauernhansl T.; ten Hompel M.; Vogel-Heuer B. (Eds.): Industrie 4.0 in Produktion', Automatisierung und Logistik, Springer Vieweg, Wiesbaden http://dx.doi.org/10.1007/978-3-658-04682-8_30
  2. Bowles J.B., Peláez C.E., 1995, Fuzzy logic prioritisation of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 50(2), 203-213. http://dx.doi.org/10.1016/0951-8320(95)00068-D
  3. Braaksma A.J.J., Klingenberg W., Veldman J., 2013. Failure mode and effect analysis in asset maintenance: a multiple case study in the process industry. International Journal of Production Research, 51(4), 1055-1071. http://dx.doi.org/10.1080/00207543.2012.674648
  4. Chang K.H., Cheng C.H., 2010. A risk assessment methodology using intuitionistic fuzzy set in FMEA. International Journal of Systems Science, 41(12), 1457-1471. http://dx.doi.org/10.1080/00207720903353633
  5. Chen J.K., 2007. 'Utility priority number evaluation for FMEA. Journal of Failure Analysis and Prevention , 7(5), 321-329. http://dx.doi.org/10.1007/s11668-007-9072-y
  6. Chin K.S., Wang Y.M., Poon G.K., Yang J.B., 2009. Failure mode and effects analysis using a groupbased evidential reasoning approach. Computers and Operations Research, 36(6), 1768-1779. http://dx.doi.org/10.1016/j.cor.2008.05.002
  7. Chin K.S., Wang Y.M., Poon G.K.K., Yang J.B., 2009. Failure mode and effects analysis by data envelopment analysis. Computers and Operations Research, 48(1), 246-256. http://dx.doi.org/10.1016/j.dss.2009.08.005
  8. Gao L., Zhou Y., Li C., Huo L., 2014. Reliability assessment of distribution systems with distributed generation based on Bayesian networks. Engineering Review, 34(1), 55-62.
  9. Gilchrist W., 1993. 'Modeling Failure Modes and Effects Analysis.' International Journal of Quality and Reliability Management, 10(5), 15-23. http://dx.doi.org/10.1108/02656719310040105
  10. Guerrero H.H., Bradley R.J., 2013. Failure Modes and Effects Analysis: An Evaluation of Group versus Individual Performance. Production and Operations Management, 22 (6), 1524-1539. http://dx.doi.org/10.1111/j.1937-5956.2012.01363.x
  11. Hu A.H., Hsu C.W., Kuo T.C., Wu W.C., 2009. Risk evaluation of green components to hazardous substance using FMEA and FAHP.' Expert Systems with Applications, 36(3), 7142-7147. http://dx.doi.org/10.1016/j.eswa.2008.08.031
  12. Jianpeng B., Xiaoyun, S., Jing, Y., 2015. Failure Mode and Effect Analysis of Power Transformer Based on Cloud Model of Weight. Telkomnika Telecommunication, Computing, Electronics and Control, 13(3), 776-782. http://dx.doi.org/10.12928/telkomnika.v13i3.1804
  13. Kagermann H., Wahlster W., Helbig J., 2013. Recommendations for Implementing the Strategic Initiative Industrie 4.0: Final Report of the Industrie 4.0 Working Group. Forschungsunion: Berlin, Germany.
  14. Lasi H., Fettke P., Kemper H.G., Feld T., Hoffmann M., 2014, Industrie 4.0. Wirtschaftsinformatik, 56(4), 261-264.
  15. Lee J., Kao H.A., Yang S., 2014. Service innovation and smart analytics for Industry 4.0 and big data environment.' Product Services Systems and Value Creation, Proceedings of the 6th CIRP Conference on Industrial Product-Service Systems. Procedia CIRP, 16, 3-8. http://dx.doi.org/10.1016/j.procir.2014.02.001
  16. Neagoe B.S., 2011. Solutions for the Improvement of the Failure Mode and Effects Analysis in the Automotive Industry. Recent Researches in Manufacturing Engineering, 127-132.
  17. Neagoe B.S., 2011. Solutions for the Improvement of the Failure Mode and Effects Analysis in the Automotive Industry.' Engineering, Quality and Production Systems, 127-132.
  18. Pedersen M.R., Nalpantidis L., Andresen R.S., Schou C., Bøgh S., Volker K., Madsen O., 2006. Robot skills for manufacturing: From concept to industrial deployment. Robotics and Computer-Integrated Manufacturing, 37, 282-291. http://dx.doi.org/10.1016/j.rcim.2015.04.002
  19. Puente J., Pino R., Priore P., Fuente D., 2002. A decision support system for applying failure mode and effects analysis.' Journal of Quality and Reliability Management, 19(2), 137-150. http://dx.doi.org/10.1108/02656710210413480
  20. Rajenthirakumar D., Karthik T., Janarthanan V., Nanthakumar S., 2014. Defect Reduction in Gas Tungsten ARC Welding Process Using Mode Effects Analysis. Acta Technica Corvininesis - Bulletin of Engineering, 7. 80-82.
  21. Reinhart G., Engelhardt P., Geiger F., Philipp T.R., Wahlster W., Zühlke D., Schlick J., Becker T., Löckelt M., Pirvu B., Stephan P., Hodek S., Scholz-Reiter B., Thoben K., Gorldt C., Hribernik K.A., Lappe D., Veigt M., 2013. Cyber physical Productionsysteme: Enhancement of productivity and flexibility by networking of intelligent systems in the factory. Springer VDI, 84-89.
  22. Rhee S.J., Ishii K. 2003. Using cost based FMEA to enhance reliability and serviceability. Advanced Engineering Informatics, 17(3-4), 179-188. http://dx.doi.org/10.1016/j.aei.2004.07.002
  23. Sankar N.R., Prabhu B.S., 2001. Modified approach for prioritisation of failures in a system failure mode and effects analysis.' International Journal of Quality and Reliability Management, 18(3), 324-335. http://dx.doi.org/10.1108/02656710110383737
  24. Schlechtendahl J., Keinert M., Kretschmer F., Lechler A., Verl A., 2015. Making existing production systems Industry 4.0-ready Holistic approach to the integration of existing production systems in Industry 4.0 environments'. Production Engineering, 9(1), 143-148. http://dx.doi.org/10.1007/s11740-014-0586-3
  25. Sellappan N., Sivasubramanian R., 2008. Modified Method for Evaluation of Risk Priority Number in Design FMEA. The ICAFI Journal of Operations Management, 7(1), 43-52.
  26. Sharma R.K., Kumar D., Kumar P., 2015, Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. International Journal of Quality and Reliability Management, 22(9), 986-1004. http://dx.doi.org/10.1108/02656710510625248
  27. Stamatis D.H., 1995. Failure mode and effect analysis: FMEA from theory to execution. Milwaukee. WI: ASQC Quality Press.
  28. Stamatis D.H., 2003. Failure Mode Effect Analysis: FMEA from Theory to execution. American Society for Quality, Quality Press, Milwaukee.
  29. Stamatis D.H., 1997. Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQC/Quality Press.
  30. Sundin E., 2008. Manufacturing Systems and Technologies for the New Frontier. The 41st CIRP Conference on Manufacturing Systems May 26-28, Tokyo, Japan.
  31. Toroa C., Barandiarana I., Posadaa J., 2015. A Perspective on Knowledge Based and Intelligent Systems Implementation in Industrie 4.0.' Procedia Computer Science, 60, 362-370. http://dx.doi.org/10.1016/j.procs.2015.08.143
  32. Vandenbrande W.W., 1998. How to use FMEA to reduce the size of your quality toolbin. Quality Progress, 31(11), 97-100.
  33. Vliegen H.J.W., van Mal H.H., 1990. Rational decision making: structuring of design meetings. IEEE Transactions on Engineering Management, 37(3), 185-190. http://dx.doi.org/10.1109/17.104287
  34. Wang S., Wana J., Zhang D., Li D., Zhang C., 2016. Towards smart factory for Industry 4.0: A self-organised multi-agent system with big databased feedback and coordination. Computer Networks, 101(4), 158-168. http://dx.doi.org/10.1016/j.comnet.2015.12.017
  35. Wenyan S., Xinguo M., Zhenyong W., Baoting Z., 2014. A rough TOPSIS Approach for Failure Mode and Effects Analysis in Uncertain Environments. Quality and Reliability Engeenering International, 30(4), 473-486. http://dx.doi.org/10.1002/qre.1500
  36. Yeh M.T., Chen Y.L., 2014. Fuzzy-based risk priority number in FMEA for semiconductor wafer processes. International Journal of Production Research, 52(2), 539-549. http://dx.doi.org/10.1080/00207543.2013.837984
Cytowane przez
Pokaż
ISSN
1895-2038
Język
eng
URI / DOI
http://dx.doi.org/10.17270/J.LOG.2017.4.1
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu