Fracture and Structural Integrity e-ISSN 1971-8993 Open access
Analytical, Numerical and Physical Models Vol. 17, No. 66 (2023)

Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM)

Author and affiliation details
  • Khalissa Saada Corresponding author Department of Mechanical Engineering, University of Mohamed Boudiaf-M’Sila, Algeria. Laboratoire de Matériaux, et Mécanique des Structures (LMMS), Université de M’sila. Algérie. khalissa.saada@univ-msila.dz
  • Salah Amroune Department of Mechanical Engineering, University of Mohamed Boudiaf-M’Sila, Algeria. Laboratoire de Matériaux, et Mécanique des Structures (LMMS), Université de M’sila. Algérie.
  • Moussa Zaoui Department of Mechanical Engineering, University of Mohamed Boudiaf-M’Sila, Algeria. Laboratoire de Matériaux, et Mécanique des Structures (LMMS), Université de M’sila. Algérie.
DOI
10.3221/IGF-ESIS.66.12
Pages
191-206
Published
Licence
CC BY 4.0
Published inVol. 17, No. 66 (2023)Go to the issue

Abstract

The aim of this study is to analyze the effect of different geometries and sections on the mechanical properties of epoxy specimens. Five tensile tests were carried out on three types of series. The experimental results obtained were 1812.21 MPa, 3.90% and 41.91 MPa for intact specimens, 1450.41 MPa, 2.16% and 21.28 MPa for specimens with hole and 750.77 MPa, 2.77% and 11.89 MPa for specimens with elliptical -notched for Young's modulus, strain and stress respectively. In addition, the experimental results indicated that the mechanical properties of both (Young's modulus value and stress value) were higher in an intact specimen. Afterwards, the nonlinear functional relationship of input parameters between epoxy sample geometries and sections was established using the response surface model (RSM) and the artificial neural network (ANN) to predict the output parameters of mechanical properties (Young's modulus and stress). In addition, the design of experiment was developed by the Analysis of the Application of Variance (ANOVA). The results showed the superiority of the ANN model over the RSM model, where the correlation coefficient values for the model datasets exceed ANN (R2 = 0.984 for Young's modulus and R2 = 0.981 for the constraint).

Keywords

Article history

  1. ReceivedUntil the next stage: 65 days
  2. RevisedUntil the next stage: 10 days
  3. AcceptedUntil the next stage: 47 days
  4. Published

How to cite

APA

Saada, K., Amroune, S., & Zaoui, M. (2023). Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM). Fracture and Structural Integrity, 17(66), 191–206. https://doi.org/10.3221/IGF-ESIS.66.12

IEEE

K. Saada, S. Amroune, and M. Zaoui, “Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM),” Fracture and Structural Integrity, vol. 17, no. 66, pp. 191–206, 2023, doi: 10.3221/IGF-ESIS.66.12.

MLA

Saada, Khalissa, et al. “Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM).” Fracture and Structural Integrity, vol. 17, no. 66, 2023, pp. 191–206. https://doi.org/10.3221/IGF-ESIS.66.12.

Chicago

Saada, Khalissa, Salah Amroune, and Moussa Zaoui. 2023. “Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM).” Fracture and Structural Integrity 17 (66): 191–206. https://doi.org/10.3221/IGF-ESIS.66.12.

Harvard

Saada, K., Amroune, S. and Zaoui, M. (2023) ‘Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM)’, Fracture and Structural Integrity, 17(66), pp. 191–206. doi: 10.3221/IGF-ESIS.66.12.

Vancouver

Saada K, Amroune S, Zaoui M. Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM). Fracture and Structural Integrity. 2023;17(66):191-206. doi:10.3221/IGF-ESIS.66.12

BibTeX

@article{saada2023, author = {Saada, Khalissa and Amroune, Salah and Zaoui, Moussa}, title = {Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM)}, journal = {Fracture and Structural Integrity}, year = {2023}, volume = {17}, number = {66}, pages = {191--206}, doi = {10.3221/IGF-ESIS.66.12}, url = {https://doi.org/10.3221/IGF-ESIS.66.12}, issn = {1971-8993} }

RIS

TY - JOUR AU - Saada, Khalissa AU - Amroune, Salah AU - Zaoui, Moussa TI - Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM) JO - Fracture and Structural Integrity T2 - Fracture and Structural Integrity PY - 2023 DA - 2023/09/27 VL - 17 IS - 66 SP - 191 EP - 206 DO - 10.3221/IGF-ESIS.66.12 UR - https://doi.org/10.3221/IGF-ESIS.66.12 SN - 1971-8993 ER -

Licence

Creative Commons Attribution 4.0 International

May be shared and adapted, provided appropriate credit is given. Copyright © 2023 Khalissa Saada, Salah Amroune, Moussa Zaoui.

Copyright 
Authors are allowed to retain both the copyright and the publishing rights of their articles without restrictions.  

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