Estimation of Young’s modulus of cast aluminum alloys due to the effect of the alloying elements by using ANN
Rushikesh GOHAD, Swanand KULKARNI
Abstract. Artificial Neural Network was used to develop the model for estimating Young’s modulus of cast aluminum alloys. MATLAB ANN Toolbox was used to develop the feed-forward backpropagation model. The cast aluminum alloys with their alloying elements percentage are given as input to develop the model while Young’s modulus of the respective alloy is given as output for the ANN model. The model was developed by using three training algorithms namely Lervenberg-Marquardt (LM), Bayesain-Regularization (BR) & Scaled conjugate gradient (SCG). Various trials were taken to develop the best suitable model. The evaluation was carried out by using Mean Squared Error (MSE) and Coefficient of correlation(R). After evaluation by MSE and R, Bayesain-Regularization showed the least error and gave the best predicted results.
Keywords
Artificial Neural Network, MATLAB, Aluminum Alloy, Young’s Modulus
Published online 3/1/2025, 11 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Rushikesh GOHAD, Swanand KULKARNI, Estimation of Young’s modulus of cast aluminum alloys due to the effect of the alloying elements by using ANN, Materials Research Proceedings, Vol. 49, pp 464-474, 2025
DOI: https://doi.org/10.21741/9781644903438-47
The article was published as article 47 of the book Mechanical Engineering for Sustainable Development
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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