Comparing deterministic and statistical approaches for predicting “short can” defects in aluminium beverage can production

• Autorzy: Baran Wojciech , Regulski Krzysztof , Kąc Sławomir , Milenin Andrij

Identyfikatory

DOI

10.7494/cmms.2023.2.0812

• Języki publikacji: EN

Abstrakty

EN

In the production of beverage cans, “short can” defects in the form of material discontinuities can occur during the deep drawing of cylindrical thin-walled aluminium products. These defects have a significant impact on production efficiency and scrap generation, and their occurrence is influenced by material and process properties. To determine the main influence of material on defect occurrence, two approaches were used: deterministic analysis of mechanical properties and microstructure, as well as statistical processing of production data using decision tree models. The latter approach was found to be more efficient, and a numerical tool was developed based on this approach to predict and reduce defect occurrence in the production process.

Słowa kluczowe

EN

short can; deterministic; analysis; statistical methods; predict; defect; reduce; decizion trees

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2023
• Tom: Vol. 23, No. 2
• Strony: 29--38
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Baran Wojciech

• CanPack S.A., Business Support Service, 32-800 Brzesko, Poland

• https://orcid.org/0000-0001-7368-1255

autor

Regulski Krzysztof

• AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, 30-059 Krakow, Poland

• https://orcid.org/0000-0001-8080-2254

autor

Kąc Sławomir

• AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, 30-059 Krakow, Poland

• https://orcid.org/0000-0001-9556-2345

autor

Milenin Andrij

• AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, 30-059 Krakow, Poland

• https://orcid.org/0000-0002-6266-2881

Bibliografia

• Baran, W., Regulski, K., & Milenin, A. (2022). Influence of materials parameters of the coil sheet on the formation of defects during the manufacture of deep-drawn cups. Processes, 10(3), 578. https://doi.org/10.3390/pr10030578.
• Chang, D.-F., & Wang, J.E. (1997). Influence of Process Parameters on the Ironing of Deep-Drawn Cups. Journal of Manufacturing Science and Engineering, 119(4B), 699–705. https://doi.org/10.1115/1.2836812.
• Folle, L.F., Silveira Netto, S.E., & Schaeffer, L. (2008). Analysis of the manufacturing process of beverage cans using aluminum alloy. Journal of Materials Processing Technology, 205(1–3), 347–352. https://doi.org/10.1016/j.jmatprotec.2007.11.249.
• Gao, E.-z., Li, H.-w., Kou, H.-ch., Chang, H., Li, J.-s., & Zhou, L. (2009). Influences of material parameters on deep drawing of thin-walled hemispheric surface part. Transactions of Nonferrous Metals Society of China, 19(2), 433–437. https://doi.org/10.1016/S1003-6326(08)60291-5.
• Lewicki, P., & Hill, E. (2006). Statistics. Methods and Applications. StatSoft.
• Milenin, A., Byrska, D.J., & Grydin, O. (2011). The multi-scale physical and numerical modeling of fracture phenomena in the MgCa0.8 alloy. Computers & Structures, 89(11–12), 1038–1049. https://doi.org/10.1016/j.compstruc.2011.01.003.
• Rękas, A., Latos, T., Budzyn, R., Fijałkowski, M., & Brodawka, Ł. (2014a). The analysis of influence of sheet properties on the ironing process of thin-walled cylindrical shell products from aluminum alloys. Key Engineering Materials, 641, 232–245. https://doi.org/10.4028/www.scientific.net/KEM.641.232.
• Rękas, A., Latos, T., Budzyn, R., Furman, A., & Siedlik, M. (2014b). The analysis of mechanical properties of 3XXX series aluminum sheets used for beverage can production. Key Engineering Materials, 641, 246–256 https://doi.org/10.4028/www.scientific.net/KEM.641.246.
• Rodríguez, J.J., Quintana, G., Bustillo, A., & Ciurana, J. (2016). A decision-making tool based on decision trees for roughness prediction in face milling. International Journal of Computer Integrated Manufacturing, 30(9), 943–957. https://doi.org/10.1080/0951192X.2016.1247991.
• Schünemann, M., Ahmetoglu, M.A., & Altan, T. (1996). Prediction of process conditions in drawing and ironing of cans. Journal of Materials Processing Technology, 59(1–2), 1–9. https://doi.org/10.1016/0924-0136(96)02280-7.
• Simões, V.M., Coër, J., Laurent, H., Oliveira, M.C., Alves, J.L., Manach, P.Y., & Menezes, L.F. (2013). Sensitivity analysis of process parameters in the drawing and ironing processes. Key Engineering Materials, 554–557, 2256–2265. https://doi.org/10.4028/www.scientific.net/KEM.554-557.2256.
• Stewart, R., Niero, M., Murdock, K., & Olsen, S.I. (2018). Exploring the implementation of a circular economy strategy: the case of a closed-loop supply of aluminum beverage cans. Procedia CIRP, 69, 810–815. https://doi.org/10.1016/j.procir.2017.11.006.
• Venkateswarlu, G., Davidson, M.J., & Tagore, G.R.N. (2010). Influence of process parameters on the cup drawing of aluminium 7075 sheet. International Journal of Engineering, Science and Technology, 2(11), https://doi.org/10.4314/IJEST.V2I11.64553.
• Wędrychowicz, P., Kustra, P., Paćko, M., & Milenin, A. (2021). A flow stress model of the AA3104-H19 alloy for the FEM simulation of the beverage can manufacturing process under large plastic deformations. Materials, 14(21), 6408, https://doi.org/10.3390/MA14216408.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).