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Modelación empírica de la conductividad térmica para un grupo de aceros
Empirical modeling of thermal conductivity for a group of steels
DOI:
https://doi.org/10.15446/dyna.v89n224.103879Palabras clave:
acero; modelación empírica; conductividad térmica; machine learning (es)steel; empirical modelling; thermal conductivity; machine learning (en)
La relación entre la composición química y la temperatura de trabajo del acero no son lineales con la conductividad térmica por lo que se proponen modelos empíricos para la predicción de esta. Se realizaron mediciones a32 marcaciones de acero AISI laminados y recocidos. Se utilizó el algoritmo de machine learning K- Nearest Neighbor, además se entrenó una red neuronal empleando el software RStudio, específicamente la librería caret, para obtener un modelo empírico que permitió predecir con un adecuado nivel de incertidumbre la conductividad térmica en el rango de temperaturas de 0−800℃. El modelo se probó con un grupo de valores reservados para este fin, obteniendo bajos niveles de incertidumbre. Los mejores resultados se obtienen al entrenar una red neuronal con 25 neuronas en la capa oculta y un valor de regularización de 0,001, obteniendo un error de 5,4%y un RMSE de 0,0228.
The relationship between chemical composition and working temperature of the steel are not linear with the thermal conductivity, so empirical models are proposed for its prediction. Measurements were made on 32 rolled and annealed AISI steel markings. The K-Nearest Neighbor machine learning algorithm was used; in addition, a neural network was trained using the RStudio software, specifically the caret library, to obtain an empirical model that allowed predicting, with an adequate level of uncertainty, the thermal conductivity in the temperature range from 0-800℃. The model was tested with a group of values reserved for this purpose, obtaining low levels of uncertainty. The best results are obtained by training a neural network with 25 neurons in the hidden layer and a regularization value of 0,001, obtaining an error of 5,4% and an RMSE of 0,0228.
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1. Yanan Camaraza‐Medina. (2025). Experimental Correlation of the Steel's Thermophysical Properties for Thermal Engineering Applications. Heat Transfer, 54(5), p.3418. https://doi.org/10.1002/htj.23369.
2. Roman Perez-Castañeda, Osvaldo F. Garcia-Morales, Yanan Camaraza-Medina. (2023). Development of expression for resistance to erosion by solid particles in turbine blades. CT&F - Ciencia, Tecnología y Futuro, 13(1), p.5. https://doi.org/10.29047/01225383.662.
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