Contribution to the Assessment of the Data Acquisition Effectiveness in the Aspect of Gas Porosity Defects Prediction in Ductile Cast Iron Castings

• Autorzy: Ignaszak Z. , Sika R. , Rogalewicz M.

Identyfikatory

DOI

10.24425/118808

• Języki publikacji: EN

Abstrakty

EN

The article presents an example of analysis of the influence of selected parameters deriving from data acquisition in foundries on the occurrence of Gas porosity defects (detected by Visual testing) in castings of ductile cast iron. The possibilities as well as related effectiveness of prediction of this kind of defects were assessed. The need to rationally limit the number of possible parameters affecting this kind of porosity was indicated. Authors also benefited from expert group's expertise in evaluating possible causes associated with the creation of the aforementioned defect. A ranking of these parameters was created and their impact on the occurrence of the defect was determined. The classic statistical tools were used. The possibility of unexpected links between parameters in case of uncritical use of these typical statistical tools was indicated. It was emphasized also that the acquisition realized in production conditions must be subject to a specific procedure ordering chronology and frequency of data measurements as well improving the casting quality control. Failure to meet these conditions will significantly affect the difficulties in implementing and correcting analysis results, from which INput/OUTput data is expected to be the basis for modelling for quality control.

Słowa kluczowe

EN

foundry processes; information technology; gas porosity; data acquisition

PL

procesy odlewnicze; technologie informatyczne; porowatość gazowa; akwizycja danych

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2018
• Tom: Vol. 18, iss. 1
• Strony: 35--40
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Ignaszak Z.

• Poznan University of Technology, , Division of Foundry, CAD/CAE Laboratory, Poznań, Poland

autor

Sika R.

• Poznan University of Technology, , Division of Foundry, CAD/CAE Laboratory, Poznań, Poland

autor

Rogalewicz M.

• Poznan University of Technology, Chair of Management and Production Engineering, Piotrowo 3, Poznań, Poland

Bibliografia

• [1] Saalem, M., Malik, S., Gottschling, J., Hartman, D. (2014). Advanced quality control in foundry manufacturing process. Proceedings, 71st World Foundry Congress (WFC), Bilbao, Spain. 1, 96-108.
• [2] Vijayaram, T.R., Sulajman, S., Hamouda, A.M.S. & Ahmad, M.H.M (2006). Foundry quality control aspects and prospects to reduce scrap rework and rejection in metal casting manufacturing industries. Journal of Materials Processing Technology. 178, 39-43. DOI: 10.1016/j.jmatprotec.2005.09.027.
• [3] Binczyk, F., Szymszal, J. & Smoliński, A. (2007). IX-MR Control Chart as a Tool in Assessment of the Cast Iron Properties Stability. Archive of Foundry Engineering. 7(3), 25-28. ISSN (1897-3310).
• [4] Sika, R., & Ignaszak, Z. (2011). Data acquisition in modelling using neural networks and decision trees. Archive of Foundry Engineering. 11(2), 113-122. ISSN (1897-3310).
• [5] Thacker, K.B. (2015). Analysis of parameters for casting ductile iron pipe. International Journal of Engineering Research and General Science. 3(1), 496-503. ISSN: 2091-2830.
• [6] Piłkowski, Z., Mika, B., Soiński, S.M. (1978). Mathematical methods of planning experience (in polish). Proceedings: Training symposium of the inter-ministerial problem of fundamental research, 30 May 1978 (pp. 16-31). Częstochowa, Poland.
• [7] Kujawińska, A., Rogalewicz, M., Piłacińska, M., Kochański, A. & Hamrol, A. (2016). Application of dominance-based rough set approach (DRSA) for quality prediction in a casting proces. Metalurgija. 55(4). 821-824. UDK 621.74.04:669.13.001.3:168.2.
• [8] FLEXICAST Project (2012-2016), http://flexicast-euproject.com/, Z.Ignaszak as PUT team head & all project supervisor. Unpublished reports. Poznan 2012-2016.
• [9] Sika, R., Ignaszak, Z., Perzyk, M., Kochański, A. & Kozłowski, J. (2016). Effectiveness of SCADA systems in control of green sands properties. Archives of Foundry Engineering. 16(1), 145-153. DOI: 10.1515/afe-2015-0094.
• [10] Monroe, R. (2005). Porosity in Castings, AFS Transactions, 05-245(4), 1-28. DOI: 10.1002/ chin.200642218.
• [11] Naro, R.L. (1999). Porosity defects in iron castings from mold-metal interface reactions. AFS Transactions. 107, 839-851.
• [12] PN-85 H-83105. (1985). Castings. Classification and terminology of defects.
• [13] Szymszal, J., Gajdzik, B. & Kaczmarczyk, G. (2016). The use of modern statistical methods to optimize production systems in foundries. Archive of Foundry Engineering. 11(2), 115-120. ISSN (1897-3310). DOI: https://doi.org/10.1515/afe-2016-0061.
• [14] Rogalewicz, M. & Sika, R. (2016). Methodologies of knowledge discovery from data and data mining methods in mechanical engineering. Management and Production Engineering Review. 7(4), 97-108. DOI: 10.1515/mper-2016-0040.
• [15] Information from participants of FLEXICAST project and from final report (2016).
• [16] International Committee of Technical Associations of Foundry – CIATF. (2004). Committee on Metallurgy and Foundry Property: Castings defects book. Edited by Institute of Foundry – Cracow.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)