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2017 | Vol. 17, No. 2 | 65--76
Tytuł artykułu

Intelligent pattern recognition of SLM machine energy data

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Selective Laser Melting (SLM) is an additive manufacturing process, in which the research has been increasing over the past few years to meet customer-specific requirements. Different parameters from the process and the machine components have been monitored in order to obtain vital information such as productivity of the machine and quality of the manufactured workpiece. The monitoring of parameters related to energy is also realized, but the utilisation of such data is usually performed for determining basic information, for instance, from energy consumption. By applying machine learning algorithms on these data, it is possible to identify not only the steps of the manufacturing process, but also its behaviour patterns. Along with these algorithms, evidences regarding the conditions of components and anomalies can be detected in the acquired data. The results can be used to point out the process errors and component faults and can be adopted to analyse the energy efficiency of the SLM process by comparing energy consumption of one single layer during the manufacturing of different components. Moreover, the state of the manufacturing process and the machine can be determined automatically and applied to predict failures in order to launch appropriate counter measures.
Wydawca

Rocznik
Strony
65--76
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
  • Fraunhofer Institute for Production Systems and Design Technology (IPK), Germany
  • Technische Universität Berlin – Institute for Machine Tools and Factory Management (IWF), Germany
  • Fraunhofer Institute for Production Systems and Design Technology (IPK), Germany
  • Fraunhofer Institute for Production Systems and Design Technology (IPK), Germany
  • Fraunhofer Institute for Production Systems and Design Technology (IPK), Germany
Bibliografia
  • [1] ECOreporter.de: Umweltschutz: Ursachen und Auswirkungen von Umweltproblem, URL: http://www.ecoreporter.de/artikel/ ursachen-und-auswirkungen-von-umweltproblemen-25-04-2013.html. Access: 10.14.2016.
  • [2] SCHRÖTER M., WEIßFLOCH U., BUSCCHAK, D., 2009, Energieeffizienz in der Produktion-Wunsch oder Wirklichkeit, Fraunhofer ISI, November 2009, URL: http://isi-lehre.de/isi-wAssets/docs/i/de/pi-mitteilungen/pi51.pdf. Access: 01.08.2016.
  • [3] Fraunhofer-Gesellschaft, E3-production – sustainable manufacturing, https://www.fraunhofer.de/en/press/research-news/2014/march/E3-production.html. Access: 01.08.2016.
  • [4] CAGGIANO A., PEREZ R., SEGRETO T., TETI R., XIROUCHAKIS P., 2016, Advanced sensor signal feature extraction and pattern recognition for wire EDM process monitoring, 18th CIRP Conference on Electro Physical and Chemical Machining (ISEM XVIII), Procedia CIRP, 42, 34-39, ISSN 2212-8271.
  • [5] D’ADDONA D.M., ULLAH A.M.M.S., MATARAZZO D., 2015, Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing, Journal of Intelligent Manufacturing, ISSN 0956-5515 (printed version), ISSN 1572-8145.
  • [6] UHLMANN E., et al, 2013, Data mining and visualization of diagnostics messages for condition monitoring, Procedia, CIRP, 11, 225-228.
  • [7] UHLMANN E., PONTES R.P., LAGHMOUCHI A., BERGMANN A., 2016, Intelligent pattern recognition of a SLM machine process and sensor data, CIRP Annals.
  • [8] KRUTH J.P., 1991, Material incress manufacturing by rapid prototyping techniques, CIRP Annals - Manufacturing Technology, 40/2, 603-614.
  • [9] LEVY G. N., SCHINDEL R., KRUTH J.-P., 2003, Rapid manufacturing and rapid tooling with layer manufacturing (LM) technologies, State of the art and future perspectives, CIRP Annals – Manufacturing Technology, 52/2, 589-609.
  • [10] McALEA K., FORDERHASE P., HEJMADI U., NELSON C., 1997, Materials and applications for the selective laser sintering process, Proceedings of the 7th International Conference on Rapid Prototyping, San Francisco, 23-33.
  • [11] UHLMANN E., URBAN K., 2011, Markt- und Trendstudie 2010 Laserstrahlschmelzen, ISBN 978-3-98144 05-1-5; Fraunhofer IPK.
  • [12] GEBHARDT A., HÖTTER J., 2016, Additive manufacturing: 3D printing for prototyping and manufacturing, Munich, Hanser.
  • [13] ZÄH M.F., 2006, Wirtschaftliche Fertigung mit Rapid-Technologien – Anwender-Leitfaden zur Auswahl geeigneter Verfahren, Munich, Hanser.
  • [14] HALL P., PARK B. U., SAMWORTH, R. J., 2008, Choice of neighbour order in nearest-neighbour classification, The Annals of Statistics, 36, 2135 - 2152.
  • [15] SONG Y., HUANG J., ZHOU D., ZHA H., GILES, C.L., IKNN: Informative K-Nearest Neighbor Pattern Classification, 248-264.
  • [16] GAO J., PEILIN Z., BAOYUAN L., ZHENGJUM X., 2007, An integrated fault diagnosis method of gearboxes using oil analysis and vibration analysis, 8th International Conference on Electronic Measurement and Instruments, 371-374.
  • [17] PERNER P., 2007, Machine learning and data mining in pattern recognition, 5th international conference, MLDM 2007, Leipzig, Germany, July, proceedings, 18-20.
  • [18] UYSAL A., BAYIR R., 2013, Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network. Journal of Zhejiang University SCIENCE C 14, 941-952.
  • [19] VINICIUS A.D., 2005, Silva fault detection in induction motors based on artificial intelligence, URL:http://surveillance7.sciencesconf.org/conference/surveillance7/32_fault_detection_in_induction_motors_based_on_artificial_intelligence.pdf Access, 10.13.2015.
  • [20] STEINWART I., CHRISTMANN A., 2006, Support vector machines, New York: Springer.
  • [21] DEAK K., KOCSIS I., VAMOSI A., KEVICZKI Z., 2014, Support vector machine in condition monitoring and fault diagnosis, Mechanical Systems and Signal Processing, 19-24.
  • [22] WIDODO A., YANG B., 2008, Wavelet support vector machine for induction machine fault diagnosis based on transient current signal, Expert Systems with Applications, 35, 307-316.
  • [23] JOOST R., DUFLOU. J.W., DORNFELD D., HERRMANN C., JESWIET J., KARA S., et al, 2012, Towards energy and resource efficient manufacturing: A processes and systems approach, CIRP Annals – Manufacturing Technology, 61/2, 587-609.
  • [24] KELLEN K., YASA E., RENALDI, DEWULF W., KRUTH J.P., DUFLOU J.R., 2011, Resource efficiency of SLS/SLM processes, Solid Freedom Fabrication Symposium, 1-16.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.baztech-57f72597-b2e1-4c3b-b73a-9143aaf79955
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