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Multi-scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 749))

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Abstract

Glass transitions are an important phenomenon in amorphous materials with potential for various applications. The Tool-Narayan-aswamy-Moynihan (TNM) model is a widely used empirical model that describes the enthalpy relaxation behavior of these materials. However, determining the appropriate values for its parameters can be challenging. To address this issue, a multi-scale convolutional neural model is proposed that can accurately predict the TNM parameters directly from the set of differential scanning calorimetry curves, experimentally measured using the sample of the considered amorphous material. The resulting Mean Absolute Error of the model over the test set is found to be 0.0252, indicating a high level of accuracy. Overall, the proposed neural model has the potential to become a valuable tool for practical application of the TNM model in the glass industry and related fields.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Informatics, University of Pardubice, 532 10, Pardubice, Czech Republic

    Marek Pakosta & Petr Dolezel

  2. Faculty of Chemical Technology, University of Pardubice, 532 10, Pardubice, Czech Republic

    Roman Svoboda

  3. University of Burgos, Edificio A, Avda. Cantabria s/n, 09001, Burgos, Spain

    Bruno Baruque Zanón

Authors
  1. Marek Pakosta
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  2. Petr Dolezel
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  3. Roman Svoboda
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  4. Bruno Baruque Zanón
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Corresponding author

Correspondence to Petr Dolezel .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Pakosta, M., Dolezel, P., Svoboda, R., Zanón, B.B. (2023). Multi-scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_3

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