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

• Autorzy: Okuniewska A. , Perzyk M. A. , Kozłowski J.

Identyfikatory

DOI

10.24425/afe.2021.138687

• Języki publikacji: EN

Abstrakty

EN

The purpose of this paper was to develop a methodology for diagnosing the causes of die-casting defects based on advanced modelling, to correctly diagnose and identify process parameters that have a significant impact on product defect generation, optimize the process parameters and rise the products’ quality, thereby improving the manufacturing process efficiency. The industrial data used for modelling came from foundry being a leading manufacturer of the high-pressure die-casting production process of aluminum cylinder blocks for the world's leading automotive brands. The paper presents some aspects related to data analytics in the era of Industry 4.0. and Smart Factory concepts. The methodology includes computation tools for advanced data analysis and modelling, such as ANOVA (analysis of variance), ANN (artificial neural networks) both applied on the Statistica platform, then gradient and evolutionary optimization methods applied in MS Excel program’s Solver add-in. The main features of the presented methodology are explained and presented in tables and illustrated with appropriate graphs. All opportunities and risks of implementing data-driven modelling systems in high-pressure die-casting processes have been considered.

Słowa kluczowe

EN

foundry industry; die casting; castings defects; process control; data analytics; application of information technology

PL

przemysł odlewniczy; odlewanie ciśnieniowe; wady odlewów; kontrola procesu; analiza danych; zastosowanie technologii informatycznych

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2021
• Tom: Vol. 21, iss. 4
• Strony: 103--109
• Opis fizyczny: Bibliogr. 22 poz., wykr.

Twórcy

autor

Okuniewska A.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Warsaw, Poland

autor

Perzyk M. A.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Warsaw, Poland

autor

Kozłowski J.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Warsaw, Poland

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. https://natemat.pl/blogi/ryszardtadeusiewicz/129195,pierwszy-dzialajacy-techniczny-model-mozgu.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).