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New Approach to Develop Ductile Cast Iron Digital Grade for Automotive Components

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Abstract

This research work demonstrates that new cast iron materials can be developed aided by an expert digital twin (manufacturing twin) and model predictive controls developed based on artificial intelligent tools. The well-known EN GJS-550 high strength standard material grade with yield strength exceeding 370 MPa, commonly used for safety components, can be replaced with the new HS420/250 grade with a yield strength higher than 420 MPa, elongation higher than 5%, with the resulting hardness maintained below 250 HB (Brinell Hardness). The new high yield strength material grade was developed by expert analysis of industrial manufacturing data with monitored automotive brake anchors for more than one year. This 13.5% increase in yield strength opens new design opportunities for lightweight cast iron components. Using the model predictive control and the corrective protocols developed, a light weight designed automotive brake anchors component was successfully manufactured with the new high yield strength material.

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References

  1. A.I. Fernadez-Calvo, J. Garay, D. Johnson, C. Monteiro, A. Gonzalez-Zabala, R. Suarez, New ductile cast iron digital grades for automotive component. AFS Trans. 130, 1–9 (2022)

    Google Scholar 

  2. T. Kvackaj, J. Bidulská, R. Bidulský, Overview of HSS steel grades development and study of reheating condition effects on austenite grain size changes. Materials 14, 1–20 (2021)

    Article  Google Scholar 

  3. EMIRI technology roadmap, EMIRI (2019), https://emiri.eu/wp-content/uploads/2021/07/EMIRI-TechnologyRoadmap-September-2019-cond-1.pdf

  4. M.J. Lalich, C.R. Loper, Effects of pearlite promoting elements on the kinetics of the eutectoid transformation in ductile cast irons. AFS Trans. 81, 217–228 (1973)

    CAS  Google Scholar 

  5. J. Lacaze, J. Sertucha, Effect of Cu, Mn and Sn on pearlite growth kinetics in as-cast ductile irons. Int. J. Cast Met. Res. 29(1–2), 73–77 (2016)

    Google Scholar 

  6. J. Lacaze, S. Ford, C. Wilson, E. Dubu, Effects of alloying elements upon the eutectoid transformation in as-cast spheroidal graphite cast iron. Scand. J. Metall. 22, 300–309 (1993)

    CAS  Google Scholar 

  7. J. Lacaze, C. Wilson, C. Bak, Experimental study of the eutectoid transformation in as-cast spheroidal graphite cast iron. Scand. J. Metall. 23, 151–163 (1994)

    CAS  Google Scholar 

  8. I. Asenjo, P. Larrañaga, J. Garay, J. Sertucha, Influencia de la composición química de diferentes chatarras de acero sobre las propiedades mecánicas de la fundición con grafito esferoidal. Rev. Metal. 47(4), 307–318 (2011)

    Article  CAS  Google Scholar 

  9. Ductile iron data for design engineers, Section 3, https://www.ductile.org/Ductile-Iron-Data, Rio Tinto Iron & Titanium INC, Montreal, Canada

  10. I. Riposan, M. Chisamera, S. Stan, Influencing factors on as-cast and heat treated 400–18 ductile iron grade characteristics. China Foundry 4(4), 300–303 (2007)

    CAS  Google Scholar 

  11. J.M. Tartaglia, R.B. Gundlach, G.M. Goodrich, Optimizing structure-property relationships in ductile iron. Int. J. Met. 8(4), 7–38 (2014)

    Google Scholar 

  12. F. Zanardi, F. Bonollo, G. Angella, N. Bonora, G. Iannitti, A. Ruggiero, A contribution to new material standards for ductile irons and austempered ductile irons. Int. J. Met. 11, 136–147 (2016). https://doi.org/10.1007/s40962-016-0095-6

    Article  Google Scholar 

  13. W. Menk, A new high strength high ductile nodular iron. Mater. Sci. Forum 925, 224–230 (2018)

    Article  Google Scholar 

  14. E.N. Pan, M.S. Lou, C.R. Loper, Effects of copper, tin, and manganese on the eutectoid transformation of graphitic cast irons. AFS Trans. 9, 819–840 (1987)

    Google Scholar 

  15. J. Lacaze, A. Boudot, V. Gerval, D. Oquab, H. Santos, The role of manganese and copper in the eutectoid transformation of spheroidal graphite cast iron. Metall. Mater. Trans. 28A, 2015–2025 (1997)

    CAS  Google Scholar 

  16. J. Sertucha, P. Larrañaga, J. Lacaze et al., Experimental investigation on the effect of copper upon eutectoid transformation of as-cast and austenitized spheroidal graphite cast iron. Int. J. Met. 4, 51–58 (2010). https://doi.org/10.1007/BF03355486

    Article  CAS  Google Scholar 

  17. E. Negri, L. Fumagalli, M. Macchi, A review of the roles of digital twin in CPS based production systems. Proc. Manuf. 11, 939–948 (2017)

    Google Scholar 

  18. S. Boschert, R. Rosen, Digital twin—the simulation aspect (Mech. Futur., Springer, 2016), pp.59–77

    Google Scholar 

  19. F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, F. Sui, Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 10(4), 2233 (2017)

    Google Scholar 

  20. R. Rosen, G. Wichert, G. Lo, K.D. Battenhausen, About the importance of autonomy and digital twins for the future of manufacturing. IFAC PapersOnLine 48(3), 567–572 (2015)

    Article  Google Scholar 

  21. B. He, K.-J. Bai, Digital twin-based sustainable intelligent manufacturing: a review. Adv. Manuf. 9, 1–21 (2021). https://doi.org/10.1007/s40436-020-00302-5

    Article  CAS  Google Scholar 

  22. D.M. D’Addona, A.M.M.S. Ullah, D. Matarazzo, Tool-wear prediction and pattern recognition using artificial neural network and DNA-based computing. J. Intell. Manuf. 28(6), 1285–1301 (2017)

    Article  Google Scholar 

  23. G.A. Susto, A. Schirru, S. Pampuri, S. McLoone, A. Beghi, Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Ind. Inf. 11(3), 812–820 (2015)

    Article  Google Scholar 

  24. http://www.zupreff.de/

  25. P. Solding, Reducing the energy use through simulation. Foundry Trade J. 181, 3648 (2007)

  26. T. Bayes, An essay towards solving a problem in the doctrine of chances 1763. Philos. Trans. R. Soc. 53, 370–418 (1991). https://doi.org/10.1098/rstl.1763.0053

    Article  Google Scholar 

  27. D.A. Sparkman, in Microstructure by Thermal Analysis, AFS conference proceeding, 2011

  28. J. Sertucha, R. Suárez, J. Izaga, Prediction of solid state structure based on eutectic and euctectoid transformation parameters in spheroidal graphite irons. Int. J. Cast Met. Res. 19(6), 315–322 (2006)

    Article  CAS  Google Scholar 

  29. P. Larrañaga, J.M. Gutiérrez, A. Loizaga, J. Sertucha, R. Suarez, A computer-aided system for melt quality and shrinkage propensity evaluation based on the solidification process of ductile iron. Trans. AFS 112, 547–561 (2008)

    Google Scholar 

  30. J. Nieves, Universidad de Deusto, 2012. Spain: Salomón: un Nuevo Enfoque para la Mejora de Procesos de Negocio Mediante la Producción Inteligente Basada en Modelos Predictivos de Control Híbridos y Autoadaptativos

  31. D.J. Celentano, A large strain thermoviscoplastic formulation for the solidification of SG cast iron in a green sand mould. Int. J. Plast. 17, 1623–1658 (2001)

    Article  CAS  Google Scholar 

  32. J.R. Quinlan, Induction of decision trees. Mac. Learn. 1(1), 81–106 (1986)

    Article  Google Scholar 

  33. E. Fix, J.L. Hodges, Discriminatory analysis-nonparametric discrimination: Small sample performance. Technical Report Project 21-49-004, Report Number 11 (1952)

  34. X. Yao, Evolutionary artificial neural networks. Int. J. Neural Syst. 4(3), 203–222 (1993)

    Article  CAS  Google Scholar 

  35. V.N. Vapnik, The nature of statistical learning theory (Springer, 2000)

    Book  Google Scholar 

  36. E. Frank, M.A. Hall, I.H. Witten, The WEKA Workbench. Online appendix for Data mining: practical machine learning tools and techniques, Morgan Kaufmann (2016)

  37. J. Demsar, T. Curk, A. Erjavec, C. Gorup, T. Hocevar, M. Milutinovic, M. Mozina, M. Polajnar, M. Toplak, A. Staric, M. Stajdohar, L. Umek, L. Zagar, J. Zbontar, M. Zitnik, B. Zupan, Orange: data mining toolbox in Python. J. Mac. Learn. Res. 14, 2349–2353 (2013)

    Google Scholar 

  38. I. Kononenko, On Biases in Estimating Multi-Valued Attributes. Int. Joint Conf. Artif. Int. 95, 1034–1040 (1995)

    Google Scholar 

  39. E. Alpaydin, Introduction to machine learning (MIT press, 2014)

    Google Scholar 

Download references

Acknowledgments

This research was financially supported by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 83090 3: H2020-EIC-FTI-2018-2020: DigiMAT project.

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Authors and Affiliations

  1. AZTERLAN, Basque Research and Technology Alliance (BRTA), Durango, Spain

    A. I. Fernández-Calvo & J. Garay

  2. Continental Teves AG & Co. oHG, Frankfurt, Germany

    D. Johnson

  3. AAPICO, Maia, Portugal

    C. Monteiro

  4. Veigalan Estudio 2010 S.L.U, Durango, Spain

    A. González-Zabala & R. Suárez

Authors
  1. A. I. Fernández-Calvo
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  2. J. Garay
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  3. D. Johnson
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  4. C. Monteiro
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  5. A. González-Zabala
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  6. R. Suárez
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Correspondence to A. I. Fernández-Calvo.

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Fernández-Calvo, A.I., Garay, J., Johnson, D. et al. New Approach to Develop Ductile Cast Iron Digital Grade for Automotive Components. Inter Metalcast 17, 1558–1568 (2023). https://doi.org/10.1007/s40962-022-00923-5

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