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Cause-effect Analysis Using A&DM System for Casting Quality Prediction

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Języki publikacji
EN
Abstrakty
EN
The paper indicates the significance of the problem of foundry processes parameters stability supervision and assessment. The parameters, which can be effectively tracked and analysed using dedicated computer systems for data acquisition and exploration (Acquisition and Data Mining systems, A&D systems) were pointed out. The state of research and methods of solving production problems with the help of computational intelligence systems (Computational Intelligence, CI) were characterised. The research part shows capabilities of an original A&DM system in the aspect of selected analyses of recorded data for cast defects (effect) forecast on the example of a chosen iron foundry. Implementation tests and analyses were performed based on selected assortments for grey and nodular cast iron grades (castings with 50 kg maximum weight, casting on automatic moulding lines for disposable green sand moulds). Validation tests results, applied methods and algorithms (the original system’s operation in real production conditions) confirmed the effectiveness of the assumptions and application of the methods described. Usability, as well as benefits of using A&DM systems in foundries are measurable and lead to stabilisation of production conditions in particular sections included in the area of use of these systems, and as a result to improvement of casting quality and reduction of defect number.
Rocznik
Strony
5--12
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr.
Twórcy
autor
  • Poznan University of Technology, 3 Piotrowo Street, 60-965 Poznan, Poland
autor
  • Poznan University of Technology, 3 Piotrowo Street, 60-965 Poznan, Poland
Bibliografia
  • [1] 49th Census of Word Casting Production. (2015, December). Modest Growth in Worldwide Casting Market. Retrieved October 22, 2018, from http://www.globalcasting magazine.com.
  • [2] Dańko, J. & Holtzer, M. (2006). The State of Art and Foresight of World’s Casting Production. Metalurgija. 45(4), 333-340.
  • [3] Foundry Chamber of Commerce Catalog, 2017.
  • [4] Yokokawa, T., Koizumi, Y., Harada, H., Suzuki, T., Aoyagi, T. & Kimura, T. (2008). Estimation of metal temperature at failed part of turbine blade for civil jet engine. Journal of the Japan Institute of Metals. 71(9), 693-698.
  • [5] Choi, S., Yang, JS. & Park, HW. (2015). The Triple Helix and International Collaboration in Science. Journal of the Association for Information Science and Technology. 66(1), 201-212.
  • [6] Fedoryszyn, A. (2007). Assessment of systems for mechanisation of casting production. Archives of Foundry Engineering. 7(3), 83-86.
  • [7] Perzyk, M. and others (2000). Foundry. Warsaw: Scientific and Technical Publication.
  • [8] Ajax Foundry, The DISA MATCH Moulding Principle, Sydney-Australia, Retrieved November 9, 2018, from http://www.ajaxfoundry.com.au/services.html.
  • [9] Sika, R. (2013). Acquisition and data mining for the Data Mining systems needs on the example applications in foundry industry. PhD Thesis – Prom. Zenon Ignaszak. Doctoral dissertation. Poznan University of Technology, Poznan, Poland.
  • [10] Sika, R., Jarczyński, A. & Kroma, A. (2019). The influence of selected parameters of the cast iron casting process using the DISA MATCH automatic moulding line on the formation of alloy-mould contact defects. Mechanical Engineering. In Gapiński B., Szostak M., Ivanov V. (Eds.), Advances in Manufacturing II. 4, 416-433. Springer.
  • [11] Perzyk, M. & Soroczynski, A. (2010). Comparative Study of Decision Trees and Rough Sets Theory as Knowledge Extraction Tools for Design and Control of Industrial Processes. International Journal of Industrial and Manufacturing Engineering. 4(1), 234-239.
  • [12] Perzyk, M. (2007). Statistical and Visualization Data Mining Tools for Foundry Production. Archive of Foundry Engineering. 7(3), 111-116.
  • [13] Saalem, M. and others (2014). Advanced Quality Control. In Foundry Manufacturing Process, 71st Word Foundry Congress, Bilbao.
  • [14] Sika, R. (2006). Studies on the structure of the SAP R/3 and the possibility of its adaptation to the management and quality control in the Iron Foundry in Srem,. MsC Thesis – Prom. Zenon Ignaszak. Master of Science dissertation. Poznan University of Technology, Poznan, Poland.
  • [15] Sika, R. & Ignaszak, Z. (2019). Data Acquisition procedures for A&DM systems dedicated for foundry industry, Lecture Notes in Mechanical Engineering, In Ivanov V. and others (Eds.). Advances in Design, Simulation and Manufacturing II. 4, 692-701. Springer.
  • [16] Malinowski, P., Suchy, J.S., Lelito, J. (2014). New trends in simulation process and data management in foundry industry, 71st Word Foundry Congress, Bilbao.
  • [17] Gramegna, N. (2017). Smart casting process control and real time quality prediction The digitalization of foundry plays a key role in competitiveness introducing new integrated platform to Control the process and predict in real-time the Quality and the cost of the casting, 9th VDI Conference with Specialist Exhibition on Casting Technology in Engine Construction: Potential for the Next generation of Vehicle Propulsion, vol. 2304, (pp. 227-234).
  • [18] Górski, F., Zawadzki, P. & Hamrol, A. (2016). Knowledge based engineering as a condition of effective mass production of configurable products by design automation. Journal of Machine Engineering. 16(4), 5-30.
  • [19] Ćwikła, G., Skołud, B. (2013). Data acquisition characteristics from production systems for the needs of business management, In Midor K., Biały W. (Eds.). Support systems in production engineering. 32-45.
  • [20] Cottyn, J., Van Landeghem, H., Stockman, K., Derammelaere, S. (2009). The combined adoption of production IT and strategic initiatives - Initial considerations for a Lean MES analysis. In Computers & Industrial Engineering, 2009, (1629-1634), Troyes, France: IEEE.
  • [21] Younus, M., Peiyong, C., Lu, Hu, Fan, Yuqing (2010). MES Development and Significant Applications in Manufacturing - A Review. In Education Technology and Computer (ICETC), Vol. 5 (97-101), Shanghai, China: IEEE.
  • [22] Larose, D.T. (2006). Discovering Knowledge in Data: An Introduction to Data Mining. New York: Wiley-Interscience.
  • [23] Kochański, A. (2010). Data preparation. Computer Methods in Materials Science. 10(1), 25-29.
  • [24] Pyle, D. (2003). Data Collection, Preparation, Quality, and Visualization. San Francisco: Morgan Kaufmann Publishers.
  • [25] PN-85 H-83105, Casting Division and terminology of defects, Poland, 1985.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3c0d7cc2-3828-46b4-9800-8f3778628859
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