Szczegóły

Tytuł artykułu

Methodology for Diagnosing the Causes of Die-Casting Defects, Based on Advanced Big Data Modelling

Tytuł czasopisma

Archives of Foundry Engineering

Rocznik

2021

Wolumin

vo. 21

Numer

No 3

Afiliacje

Okuniewska, A. : Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland ; Perzyk, M.A. : Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland ; Kozłowski, J. : Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland

Autorzy

Słowa kluczowe

fault diagnosis ; die casting ; process control ; Data analytics ; Application of information technology to the foundry industry

Wydział PAN

Nauki Techniczne

Zakres

103-109

Wydawca

The Katowice Branch of the Polish Academy of Sciences

Bibliografia

[1] Paturi, R.U.M., Cheruki S. (2020). Application, and performance of machine learning techniques in manufacturing sector from the past two decades: A review. Materials Today: Proceedings. 38(5), 2392-2401. DOI: https://doi.org/10.1016/j.matpr.2020.07.209
[2] Campbell, J. (2003). Castings, the new metallurgy of cast materials, second edition. Elsevier Science Ltd., ISBN: 9780750647908, 307-312.
[3] Kochański, A.W. & Perzyk, M. (2002). Identification of causes of porosity defects in steel castings with the use of artificial neural networks. Archives of Foundry. 2(5), 87-92. ISSN 1642-5308.
[4] Falęcki, Z. (1997). Analysis of casting defects. Kraków: AGH Publishers.
[5] Kim, J., Kim, J., Lee, J. (2020). Die-Casting defect prediction and diagnosis system using process condition data. Procedia Manufacturing. 51, 359-364. DOI: 10.1016/j.promfg.2020.10.051.
[6] Lewis, M. (2018). Seeing through the Cloud of Industry 4.0. In 73rd WFC, 23-27, (pp. 519-520). Krakow, Poland: Polish Foundrymen’s Association.
[7] Perzyk, M., Dybowski, B. & Kozłowski, J. (2019). Introducing advanced data analytics in perspective of industry 4.0. in die casting foundry. Archives of Foundry Engineering. 19(1), 53-57.
[8] Perzyk, M., Kozłowski, J. & Wisłocki, M., (2013). Advanced methods of foundry processes control. Archives of Metallurgy and Materials. 58(3), 899-902. DOI: 10.2478/amm-2013-0096
[9] Makhlouf, M.M., Apelian, D. & Wang, L. (1998). Microstructures and properties of aluminum die casting alloys. North American Die Casting. https://doi.org/10.2172/751030
[10] Tariq, S., Tariq, A., Masud, M. & Rehman, Z. (2021). Minimizing the casting defects in high pressure die casting using taguchi analysis. Scientia Iranica. DOI: 10.24200/sci.2021.56545.4779.
[11] Fracchia, E., Lombardo, S., & Rosso, M. (2018). Case study of a functionally graded aluminum part. Applied Sciences. 8(7), 1113.
[12] Dargusch, M.S., Dour, G., Schauer, N., Dinnis, C.M. & Savage, G. (2006). The influence of pressure during solidification of high pressure die cast aluminium telecommunications components. Journal of Materials Processing Technology. 180(1-3), 37-43.
[13] Bonollo, F., Gramegna, N., Timelli, G. 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.
[14] Adamane, A.R., Arnberg, L., Fiorese, E., Timelli, G., Bonollo, F. (2015). Influence of injection parameters on the porosity and tensile properties of high-pressure die cast Al-Si alloys: A Review. International Journal of Meterials. 9(1), 43-53.
[15] 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.
[16] Łuszczak, M. & Dańko, R. (2013). State the issues in the casting of large structural castings in aluminium alloys. Archives of Foundry Engineering. 13(3), 113-116. ISSN (1897-3310).
[17] Davis, J.R. (1990). ASM handbook. ASM, Metals Park, OH. 123-151, 166-16.
[18] Perzyk, M., Biernacki, R. & Kozłowski, J. (2008). Data mining in manufacturing: significance analysis of process parameters. Journal of Engineering Manufacture. 222(11), 1503-1516. DOI: 10.1243/09544054JEM1182.
[19] Koronacki, J., Mielniczuk J. Statistics for students of technical and natural sciences. WNT (209-210, 458). (in Polish).
[20] Okuniewska, A., Methods review of advanced data analysis tools, in process control and diagnostics. Piech K. (red.) Issues Actually Addressed by Young Scientists, 17, 2020, Krakow, Poland, Creativetime, 328 p., ISBN 978-83-63058-97-5
[21] Lawrence, S., Giles, C.L., Tsoi, A.C. (1996). What size neural network gives optimal generalization? Convergence Properties of Backpropagation. Technical Report UMIACS-TR-96-22 and CS-TR-3617. Institute for Advanced Computer Studies, University of Maryland. College Park, MD 20742.
[22] Tadeusiewicz, R. (2005). First electronic brain model.
[23] https://natemat.pl/blogi/ryszardtadeusiewicz/129195,pierwszy-dzialajacy-techniczny-model-mozgu

Data

2021.12.23

Typ

Article

Identyfikator

DOI: 10.24425/afe.2021.138687 ; ISSN 2299-2944
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