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On the Possibility of Replacing Scheil-Gulliver Modeling with Machine Learning and Neural Network Models
Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
Jönköping University, School of Engineering, JTH, Materials and Manufacturing.ORCID iD: 0000-0002-2361-8810
Jönköping University, School of Engineering, JTH, Materials and Manufacturing.ORCID iD: 0000-0002-0101-0062
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2023 (English)In: Solid State Phenomena / [ed] A. Pola, M. Tocci and A. Rassili, Trans Tech Publications, 2023, Vol. 347, p. 157-163Chapter in book (Refereed)
Abstract [en]

Resource-efficient manufacturing is a foundation for sustainable and circular manufacturing. Semi-solid processing typically reduces material loss and improves productivity but generally requires a better understanding and control of the solidification of the cast material. Thermal analysis is commonly used in high-pressure die casting (HPDC) processes to determine casting process parameters, such as liquidus and solidus temperatures. However, this method is inadequate for semi-solid casting processes because the eutectic temperature is also a crucial parameter for successful semi-solid casting. This study explores the feasibility of using machine learning and artificial neural networks to predict fundamental values in Al-Si alloy casting. The Thermo-Calc 2022 software Scheil-Gulliver calculation function was used to generate the training and the test datasets, which included features such as melting temperature, alpha aluminium solidification temperature, eutectic temperature, and the solid fraction amounts at eutectic temperature. The results show that both models have a symmetric mean absolute percentage error (SMAPE) of less than 2 % with temperature prediction, with the machine learning model achieving a better accuracy of less than 1 %. A case study comparing practical measurements with prediction results is also discussed, demonstrating the potential of AI methods for predicting semi-solid casting processes.

Place, publisher, year, edition, pages
Trans Tech Publications, 2023. Vol. 347, p. 157-163
Series
Solid State Phenomena, ISSN 1012-0394, E-ISSN 1662-9779 ; 347
Keywords [en]
Aluminium; Semi-solid casting, Machine learning, Neural network, Segregation, Solidification, Training data collection
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:hj:diva-62495DOI: 10.4028/p-m0SusZScopus ID: 2-s2.0-85170515196OAI: oai:DiVA.org:hj-62495DiVA, id: diva2:1798400
Funder
Knowledge FoundationAvailable from: 2023-09-19 Created: 2023-09-19 Last updated: 2024-02-09Bibliographically approved

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Li, ZiyuTan, HeLattanzi, LuciaJarfors, Anders E.W.

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