Skip to main content
SpringerLink
Log in

Casting hybrid twin: physics-based reduced order models enriched with data-driven models enabling the highest accuracy in real-time

  • Manufacturing empowered by digital technologies and twins
  • Published:
International Journal of Material Forming Aims and scope Submit manuscript

Abstract

Knowing the thermo-mechanical history of a part during its processing is essential to master the final properties of the product. During forming processes, several parameters can affect it. The development of a surrogate model makes it possible to access history in real time without having to resort to a numerical simulation. We restrict ourselves in this study to the cooling phase of the casting process. The thermal problem has been formulated taking into account the metal as well as the mould. Physical constants such as latent heat, conductivities and heat transfer coefficients has been kept constant. The problem has been parametrized by the coolant temperatures in five different cooling channels. To establish the offline model, multiple simulations are performed based on well-chosen combinations of parameters. The space-time solution of the thermal problem has been solved parametrically. In this work we propose a strategy based on the solution decomposition in space, time, and parameter modes. By applying a machine learning strategy, one should be able to produce modes of the parametric space for new sets of parameters. The machine learning strategy uses either random forest or polynomial fitting regressors. The reconstruction of the thermal solution can then be done using those modes obtained from the parametric space, with the same spatial and temporal basis previously established. This rationale is further extended to establish a model for the ignored part of the physics, in order to describe experimental measures. We present a strategy that makes it possible to calculate this ignorance using the same spatio-temporal basis obtained during the implementation of the numerical model, enabling the efficient construction of processing hybrid twins.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. Lehmhus D (2022) Advances in metal casting technology: a review of state of the art, challenges and trends; part i: changing markets, changing products. Metals 12(11)

  2. Campbell J (2015) Complete casting handbook. Butterworth-Heinemann

    Google Scholar 

  3. Kridli GT, Friedman PA, Boileau JM (2021) Chapter 7 - manufacturing processes for light alloys. In: Mallick PK (ed) Introduction to aerospace materials. Woodhead Publishing, pp 267–320

    Google Scholar 

  4. Mouritz AP (2012) Introduction to aerospace materials. In: Mouritz AP (ed) Introduction to aerospace materials. Woodhead Publishing, pp 1–14

    Google Scholar 

  5. Fang HC, Chao H, Chen KH (2014) Effect of zr, er and cr additions on microstructures and properties of al-zn-mg-cu alloys. Mater Sci Eng A 610:10–16

    Article  CAS  Google Scholar 

  6. Miladinovic S, Stojanovic B, Gajevic S, Vencl A (2023) Hypereutectic aluminum alloys and composites: a review. Silicon 15:2507–2527

    Article  CAS  Google Scholar 

  7. Rams J, Torres B (2022) Casting aluminum alloys. In: Caballero FG (ed) Encyclopedia of materials: metals and alloys. Elsevier, pp 123–131

    Chapter  Google Scholar 

  8. Bayraktar S, Hekimoglu AP (2021) Chapter 19 - current technologies for aluminum castings and their machinability. In: Davim JP, Gupta K (eds) Advanced welding and deforming. Elsevier, pp 585–614

    Chapter  Google Scholar 

  9. Mouritz AP (2012) Production and casting of aerospace metals. In: Mouritz AP (ed) Introduction to aerospace materials. Woodhead Publishing, pp 128–153

    Google Scholar 

  10. Tiwari SK, Singh RK, Srivastava SC (2016) Optimisation of green sand casting process parameters for enhancing quality of mild steel castings. Int J Product Qual Manag 17:127–141

    Article  Google Scholar 

  11. Ahmadein M, Ammar HE, Naser AA (2022) Modeling of cooling and heat conduction in permanent mold casting process. Alex Eng J 61:1757–1768

    Article  Google Scholar 

  12. Gunduz M, Kaya H, Cadirli E, Ozmen A (2004) Interflake spacings and undercoolings in al-si irregular eutectic alloy. Mater Sci Eng A 369:215–229

    Article  Google Scholar 

  13. Gras Ch, Meredith M, Hunt JD (2005) Microdefects formation during the twin-roll casting of al-mg-mn aluminium alloys. J Mater Process Technol 167:62–72

    Article  CAS  Google Scholar 

  14. Wang L, Sun Y, Bo L, Zuo M, Zhao D (2019) Effects of melt cooling rate on the microstructure and mechanical properties of al-cu alloy. Mater Res Express 6:116507

    Article  ADS  CAS  Google Scholar 

  15. Kurtulus K, Bolatturk A, Coskun A, Gürel B (2021) An experimental investigation of the cooling and heating performance of a gravity die casting mold with conformal cooling channels. Appl Therm Eng 194:117105

    Article  Google Scholar 

  16. Sachs E, Wylonis E, Allen S, Cima M, Guo H (2000) Production of injection molding tooling with conformal cooling channels using the three dimensional printing process. Polym Eng Sci 40:1232–1247

    Article  CAS  Google Scholar 

  17. Feng S, Kamat AM, Pei Y (2021) Design and fabrication of conformal cooling channels in molds: review and progress updates. Int J Heat Mass Transf 171:121082

    Article  CAS  Google Scholar 

  18. Bertoli C, Stoll P, Philipp, Hora P (2019) Thermo-mechanical analysis of additively manufactured hybrid extrusion dies with conformal cooling channels. In: COMPLAS XV: proceedings of the XV international conference on computational plasticity: fundamentals and applications. CIMNE, pp 519–528

  19. Muller B, Gebauer M, Polster S, Neugebauer R, Malek R, Kotzian M, Hund R (2013) Ressource-efficient hot sheet metal forming by innovative die cooling with laser beam melted tooling components. In: High value manufacturing: advanced research in virtual and rapid prototyping: proceedings of the 6th international conference on advanced research in virtual and rapid prototyping, pp 321–326

  20. Behrens BA, Bouguecha A, Vucetic M, Bonhage M, Malik IY (2016) Numerical investigation for the design of a hot forging die with integrated cooling channels. Proc Technol 26:51–58

    Article  Google Scholar 

  21. Norwood AJ, Dickens PM, Soar RC, Harris R, Gibbons G, Hansell R (2004) Analysis of cooling channels performance. Int J Comput Integr Manuf 17:669–678

    Article  Google Scholar 

  22. Karakoc C, Dizdar KC, Dispinar D (2022) Investigation of effect of conformal cooling inserts in high-pressure die casting of alsi9cu3. Int J Adv Manuf Technol 121:7311–7323

    Article  Google Scholar 

  23. Yang Xw ZHU, JC, Nong ZS, Dong HE, LAI ZH, Liu Y, Liu FW, (2013) Prediction of mechanical properties of a357 alloy using artificial neural network. Trans Nonferrous Met Soc China 23:788–795

  24. Suleiman LTI, Bala KC, Lawal SA, Abdulllahi AA, Godfrey M (2020) Applications of artificial intelligence techniques in metal casting-a review, pp 97–102

  25. Cemernek D, Cemernek S, Gursch H, Pandeshwar A, Leitner T, Berger M, Klösch G, Kern R (2022) Machine learning in continuous casting of steel: a state-of-the-art survey. J Intell Manuf 33:1561–1579

    Article  Google Scholar 

  26. Jiang LH, Wang AG, Tian NY, Zhang WC, Fan QL (2011) Bp neural network of continuous casting technological parameters and secondary dendrite arm spacing of spring steel. J Iron Steel Res Int 18:25–29

    Article  Google Scholar 

  27. Bouhouche S, Lahreche M, Bast J (2008) Control of heat transfer in continuous casting process using neural networks. Acta Automatica Sinica 34:701–706

    Article  MathSciNet  Google Scholar 

  28. Susac F, Tăbăcaru V, Baroiu N, Viorel P (2018) Prediction of thermal field dynamics of mould in casting using artificial neural networks. In: MATEC Web Conferences, vol 178. EDP Sciences, p 06012

  29. Vasileiou AN (2015) Determination of local heat transfer coefficients in precision castings by genetic optimisation aided by numerical simulation. J Mech Eng Sci 229:735–750

    Article  Google Scholar 

  30. Borzacchiello D, Aguado JV, Chinesta F (2019) Non-intrusive sparse subspace learning for parametrized problems. Archives of computational methods in engineering 26:303–326

    Article  MathSciNet  Google Scholar 

  31. Ibanez R, Abisset-Chavanne E, Ammar A, Gonzalez D, Cueto E, Huerta A (2018) Duval JL, Chinesta F (2018) A multi-dimensional data-driven sparse identification technique: the sparse proper generalized decomposition. Complexity, 5608286

  32. Brunton S, Proctor JL, Kutz N (2016) Discovering governing equations from data by sparse identification of nonlinear dynamical systems. PNAS 113(15):3932–3937

    Article  ADS  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sancarlos A, Champaney V, Cueto E, Chinesta F (2023) Regularized regressions for parametric models based on separated representations. Adv Model and Simul in Eng Sci 10:4

    Article  Google Scholar 

  34. Goodfellow I, Bengioand Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    Google Scholar 

  35. Chinesta F, Cueto E, Abisset-Chavanne E, Duval JL, El Khaldi F (2020) Virtual, digital and hybrid twins: a new paradigm in data-based engineering and engineered data. Archives of Computational Methods in Engineering 27:105–134

    Article  MathSciNet  Google Scholar 

  36. Sancarlos A, Cameron M, Abel A, Cueto E, Duval JL, Chinesta F (2021) From rom of electrochemistry to ai-based battery digital and hybrid twin. Archives of Computational Methods in Engineering 28:979–1015

    Article  MathSciNet  Google Scholar 

  37. Moya B, Badias A, Alfaro I, Chinesta F, Cueto E (2022) Digital twins that learn and correct themselves. Int J Numer Methods Eng 123(13):3034–3044

    Article  Google Scholar 

  38. Argerich C, Carazo A, Sainges O, Petiot E, Barasinski A, Piana M, Ratier L, Chinesta F (2020) Empowering design based on hybrid twin: application to acoustic resonators. Designs 4:44

    Article  Google Scholar 

  39. Sancarlos A, Le Peuvedic JM, Groulier J, Duval JL, Cueto E, Chinesta F (2021) Learning stable reduced-order models for hybrid twins. Data Centric Engineering 2:e10

    Article  Google Scholar 

  40. Nouri M, Artozoul J, Caillaud A, Ammar A, Chinesta F, Köser O (2022) Shrinkage porosity prediction empowered by physics-based and data-driven hybrid models. Int J Mater Form 15(3)

  41. Sancarlos A, Champaney V, Cueto E, Chinesta F (2023) Regularized regressions for parametric models based on separated representations. Advanced Modeling and Simulation in Engineering Sciences 10(1)

  42. Samarskii AA, Vabishchevich PN, Iliev OP, Churbanov AG (1993) Numerical simulation of convection/diffusion phase change problems-a review. Int J Heat Mass Transfer 36(17):4095–4106

    Article  CAS  Google Scholar 

  43. Heim D, Clarke JA (2004) Numerical modelling and thermal simulation of pcm-gypsum composites with esp-r. Energy and Buildings 36(8):795–805

Download references

Acknowledgements

This material is based upon work supported in part by the Army Research Laboratory and the Army Research Office under contract/grant number W911NF2210271. This work has also been partially funded by the Spanish Ministry of Science and Innovation, AEI /10.13039/501100011033, through Grant number PID2020-113463RB-C31 and the Regional Government of Aragon and the European Social Fund, group T24-20R. The support of ESI Group through the Chairs at ENSAM and Universidad de Zaragoza is also gratefully acknowledged.

Author information

Authors and Affiliations

  1. LAMPA Laboratory & ESI Group Chair, Arts et Metiers Institute of Technology, 2 boulevard du Ronceray, BP 93525, 49035, Angers cedex 01, France

    Amine Ammar & Mariem Ben Saada

  2. ESI Group Chair, Aragon Institute of Engineering Research, Universidad de Zaragoza, Maria de Luna s/n, 50018, Zaragoza, Spain

    Elias Cueto

  3. PIMM Laboratory & ESI Group Chair, Arts et Metiers Institute of Technology, CNRS, Cnam, HESAM Université, 151 boulevard de l’Hôpital, 75013, Paris, France

    Francisco Chinesta

Authors
  1. Amine Ammar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariem Ben Saada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elias Cueto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francisco Chinesta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amine Ammar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ammar, A., Ben Saada, M., Cueto, E. et al. Casting hybrid twin: physics-based reduced order models enriched with data-driven models enabling the highest accuracy in real-time. Int J Mater Form 17, 16 (2024). https://doi.org/10.1007/s12289-024-01812-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12289-024-01812-4

Keywords

Search

Navigation