Predictive modeling of Young's modulus for alpha alumina using artificial neural network and multiple linear regression

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dc.contributor.authorFissah, Belgacem
dc.contributor.authorBelghalem, Hadj
dc.contributor.authorDjaddou, Messaoud
dc.date.accessioned2025-04-20T15:54:16Z
dc.date.available2025-04-20T15:54:16Z
dc.date.issued2021
dc.description.abstractIn the present work, we have constructed a predictive model for one of the important mechanical properties in the study of the mechanical and thermal behavior of materials, this property is Young's modulus. The samples on which the experiments to determine the above property of alumina (α-Al2O3) were performed were made by Spark Plasma Sintering (SPS). The experimental results were exploited using the radial basis function (RBF) neural network model and multiple linear regression (MLR) to predict and construct the mathematical model. A comparison was made of the multiple linear regression model with the radial basis function (RBF) neural network model. Then, the two proposed models were compared with the experimental results. The study obtained showed good agreement between the experimental results and the proposed RBFNN models. But the MLR models were modest in predicting the studied mechanical property.
dc.identifier.urihttp://dspace.univ-oeb.dz:4000/handle/123456789/21903
dc.language.isoen
dc.publisherUniversity of Oum El Bouaghi
dc.subjectSPS : Alumina Spark Plasma Sintering ; RBF : Neural network ; MLR : Multiple linear regression
dc.titlePredictive modeling of Young's modulus for alpha alumina using artificial neural network and multiple linear regression
dc.typeArticle
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The 1 ST international conference on electronics, artificial intelligence and technologies ICEAINT 2021