Predictive Modeling of the Mechanical Properties of Alpha Alumina Using Artificial Neural Networks and Multiple Linear Regression

In the present study, we built predictive models of the mechanical properties (Young’s modulus, fracture strength and toughness) of α-Al 2 O 3 . Experiments carried out on samples produced by spark plasma sintering (SPS). The experimental results were the basis for the evaluation of mathematical mod...

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Bibliographic Details
Published in:Glass and ceramics 2023-11, Vol.80 (7-8), p.347-354
Main Authors: Belghalem, H., Fissah, B., Djeddou, M., Hamidouche, M.
Format: Article
Language:English
Subjects:
Aluminum compounds
Aluminum oxide
Analysis
Artificial neural networks
Ceramic materials
Ceramics
Characterization and Evaluation of Materials
Chemistry and Materials Science
Composites
Fracture strength
Fracture toughness
Glass
Materials Science
Mathematical models
Mechanical properties
Modulus of elasticity
Natural Materials
Neural networks
Prediction models
Radial basis function
Regression analysis
Sintering
Spark plasma sintering
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Summary:In the present study, we built predictive models of the mechanical properties (Young’s modulus, fracture strength and toughness) of α-Al 2 O 3 . 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.
ISSN:0361-7610
1573-8515
DOI:10.1007/s10717-023-00612-7