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Tytuł artykułu

Introducing Advanced Data Analytics in Perspective of Industry 4.0 in a Die Casting Foundry

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents some aspects of a development project related to Industry 4.0 that was executed at Nemak, a leading manufacturer of the aluminium castings for the automotive industry, in its high pressure die casting foundry in Poland. The developed data analytics system aims at predicting the casting quality basing on the production data. The objective is to use these data for optimizing process parameters to raise the products’ quality as well as to improve the productivity. Characterization of the production data including the recorded process parameters and the role of mechanical properties of the castings as the process outputs is presented. The system incorporates advanced data analytics and computation tools based on the analysis of variance (ANOVA) and applying an MS Excel platform. It enables the foundry engineers and operators finding the most efficient process variables to ensure high mechanical properties of the aluminium engine block castings. The main features of the system are explained and illustrated by appropriate graphs. Chances and threats connected with applications of the data-driven modelling in die casting are discussed.
Rocznik
Tom
Strony
53--57
Opis fizyczny
Bibliogr. 10 poz., rys., wykr.
Twórcy
autor
  • Warsaw University of Technology, Institute of Manufacturing Technologies, Warszawa, Poland
autor
  • Nemak Poland, ul. Komorowicka 53, 43-300 Bielsko-Biała, Poland
  • Warsaw University of Technology, Institute of Manufacturing Technologies, Warszawa, Poland
Bibliografia
  • [1] Lewis, M. (2016). Industry 4.0 and what it means to the foundry industry. Retrieved November 12, 2017, from http://www.foundrytradejournal.com/features/industry-40-and-what-it-means-to-the-foundry-industry.
  • [2] Lewis, M. (2018). Seeing through the Cloud of Industry 4.0. In 73th WFC, 23-27 September 2018 (pp. 519-520). Krakow, Poland: Polish Foundrymen’s Association (STOP).
  • [3] Rowland, A. (2016). Industry 4.0 – is it all about industrial data and analytics. Retrieved November 15, 2017, from https://www.i-scoop.eu/industrial-data-analytics.
  • [4] Zavalishina, J. (2016, May). Man to Machine: Manufacturing and the Fourth Revolution. Retrieved November 14, 2017, from https://internetofbusiness.com/manufacturings-4th-revolution.
  • [5] Gramegna, N. & Bonollo, F. (2016). HPDC foundry competitiveness based on smart Control and Cognitive system in Al-alloy products. La Metallurgia Italiana. 6, 21-24.
  • [6] Bonollo, F., Gramegna, N. & Timelli, G. (2015). High-Pressure Die-Casting: Contradictions and Challenges. JOM: the journal of the Minerals, Metals & Materials Society. 67(5), 901-908. DOI: 10.1007/s11837-015-1333-8.
  • [7] Perzyk, M., Biernacki, R. & Kozlowski, J. (2008). Data mining in manufacturing: significance analysis of process parameters. Journal of Engineering Manufacture. 222(11), 1503-1516. DOI: 10.1243/09544054JEM1182.
  • [8] StatSoft. (2019) Introduction to ANOVA / MANOVA Retrieved April 13, 2016, from http://www.statsoft.com/ Textbook/ANOVA-MANOVA.
  • [9] Romero, D., Stahre, J., Wuest, T., Noran, O., Bernus, P., Fast-Berglund, Å. & Gorecky, D. (2016). Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. In CIE46 Proceedings, 29-31 October 2016 (pp. 1-11). Tianjin, China.
  • [10] Perzyk, M., Kozlowski, J., Kochanski, A. (2018). Intelligent data analytics for foundry industry 4.0. In 73th WFC, 23-27 September 2018 (pp. 399-400). Krakow, Poland, Polish Foundrymen’s Association (STOP).
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-611dd5a5-e720-401c-8a7f-fba2743ca18a
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