Multi-Scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction - Pakosta, Marek; Doležel, Petr; Svoboda, Roman; Baruque Zanon, Bruno (Springer Nature Switzerland AG, 2023)
Glass transitions are an important phenomenon in amor phous materials with potential for various applications. The Tool-Narayanaswamy-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 challeng ing. To address this issue, a multi-scale convolutional neural model is pro posed 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.
Multiškálový neuronový model pro odhad parametrů TNM modelu
Keywords: Tool-Narayanaswamy-Moynihan (TNM) model; multi-scale neural model; theoretical kinetic; enthalpy relaxation dynamics; glass transition; differential scanning calorimetry (DSC) data; deep learning; TNM model; neuronový model; hluboké učení