Steklo i Keramika (Glass and Ceramics). Monthly scientific, technical and industrial journal

 

ISSN 0131-9582 (Online)

PREDICTIVE MODELING OF THE MECHANICAL PROPERTIES OF ALPHA ALUMINA USING ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION

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Autors:

H. Belghalem, B. Fissah, M. Djeddou, M. Hamidouche

  • Continuous numbering: 1148
  • Pages: 62-64
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In the present study, we built predictive models of the mechanical properties (Young's modulus, fracture strength and toughness) of ?-Al2O3. Experiments carried out on samples produced by spark plasma sintering (SPS). The experimental results were the basis for the evaluation of mathematical models and predictions by both the radial basis function neural network (RBFNN) and multiple linear regression (MLR) models. The results of the comparison of MLR and RBFNN models showed good agreement between the experimental data and the RBFNN model predictions whereas the MLR model reveals modest agreement with the studied mechanical properties.

H. Щ. Belghalem – Mines laboratory, Faculty of Sciences and Technology, Larbi T?bessi University, Tebessa, Algeria. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it..
B. Щ. Fissah – Mines laboratory, Faculty of Sciences and Technology, Larbi T?bessi University, Tebessa, Algeria. E-mail: Ошибка! Закладка не определена.
M. Щ. Djeddou – Research Laboratory in Subterranean and Surface Hydraulics (LARHYSS), Faculty of Sciences and Technology, Mohamed Khider University, Biskra, Algeria. E-mail: Ошибка! Закладка не определена.
M. Щ. Hamidouche – Institut d’optique et mecanique de precision, universite Ferhat ABBAS, Setif, Algeriа. E-mail: Ошибка! Закладка не определена.

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DOI: 10.14489/glc.2023.08.pp.062-064
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Keywords

Alumina, Spark plasma sintering (SPS), Radial Basis Function Neural Network (RBFNN), multiple linear regression (MLR).

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The full article will be published in the translated version of the journal Glass and Ceramics, 2023, V. 80, No. 7–8.

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Lyudmila Vladimirovna Sokolova