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Analysis of institutional authors

Moreno Labella, Juan JoseAuthorGonzalez Fernandez De Castro, MilagrosaCorresponding AuthorPanizo Laiz, MiguelAuthorMartín álvarez, YolandaAuthor

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August 28, 2025
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Machine Learning-Based Prediction of Time Required to Reach the Melting Temperature of Metals in Domestic Microwaves Using Dimensionless Modeling and XGBoost

Publicated to: MATERIALS. 18 (14): 3400- - 2025-07-20 18(14), DOI: 10.3390/ma18143400

Authors:

Labella, JJM; de Castro, MGF; Sevilla, VS; Laiz, MP; Alvarez, YM
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Affiliations

Univ Politecn Madrid UPM, Ctr Laser UPM, Madrid 28660, Spain - Author
Univ Politecn Madrid UPM, Escuela Tecn Super Ingn Ind, Steelmaking Teaching Unit, Madrid 28006, Spain - Author

Abstract

A novel and cost-effective methodology is introduced for the precise prediction of the melting time of metals and alloys in a 700 W domestic microwave oven, using a hybrid SiC-graphite susceptor to ensure efficient heating without direct interaction with microwaves. The study includes experimental trials with multiple alloys (Sn-Bi, Zn, Zamak, and Al-Si, among others) and variable masses, whose results made it possible to construct a dimensionless model, trained with XGBoost on easily measurable thermophysical properties (specific heat, density, thermal conductivity, mass, and melting temperature). The model achieves high accuracy, with a relative error below 5%, and metrics of MAE = 4.8 s, RMSE = 6.1 s, and R2 = 0.9996. The generalization of the model to different microwave powers (600-1100 W) is also validated through analytical adjustment, without the need for additional experiments. The proposal is implemented as a Python application with a graphical interface, suitable for any academic or teaching laboratory, and its performance is compared with classical models. This approach effectively contributes to the democratization of thermal testing of metals in educational and research settings with limited resources, providing thermodynamic rigor and advanced artificial intelligence tools.
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Keywords

Broad access to scienceBroader access to sciencBroader access to scienceCost effectiveCost effectivenessDimensionless modelsDomestic microwave ovensEducational applicationEducational applicationsLearning systemsMachine learningMachine-learningMelting pointMelting timeMetalsMetals and alloysMicrowavesPredictive modelingPredictive modelsSilicon alloysSilicon carbideSpecific heatThermal conductivityThermal modelingXgboost

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal MATERIALS due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 25/97, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Metallurgy & Metallurgical Engineering. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Condensed Matter Physics.

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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 2 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/95520/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 12
    • Downloads: 3
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    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MORENO LABELLA, JUAN JOSE) and Last Author (MARTIN ALVAREZ, MARIA YOLANDA).

    the author responsible for correspondence tasks has been GONZALEZ FERNANDEZ DE CASTRO, MILAGROSA.

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