Issue 66

K. Saada et alii, Frattura ed Integrità Strutturale, 66 (2023) 191-206; DOI: 10.3221/IGF-ESIS.66.12

Figure 12: Comparison between experimental and predicted RSM and ANN models tensile data for: (a) Stress - (b) Young‘s Modulus.

C ONCLUSION

F

or this research, a study was conducted on the mechanical properties of epoxy tensile samples (intact specimens, specimens with hole-notched and specimens with elliptical -notched) to evaluate the effect of sample geometry and cross-section on tensile test results. In addition, ANN and RSM models were used to predict mechanical properties, and the following results were obtained:  The experimental results showed, through tensile tests of the samples studied, that the diameter and shape of the hole affect the mechanical properties of the materials. The lowest stress and Young‘s Modulus were observed in the specimen with elliptical -notched .  The results we obtained with the ANN and RSM models were excellent, however, the ANN model can be considered the best since it can predict the coefficient and the Young stress due to its high correction coefficient, which is close to 1. As a result, the results of the ANN model are closer to the experimental results. As it is shown in the Tab.5

ANN

RSM

correlation coefficients (R) of stress

0.98146 0.98427

0.9598 0.9458

correlation coefficients (R) of Young's Modulus

Table 5 : Comparison between RSM and ANN models.

 We have obtained the results of the correlation coefficients (R) for ANN are excellent because correlation coefficients (R) of Young‘s Modulus were all greater than 0.984, with R = 0.99 for training, R = 1 for validation, R = 1 for test and R=0.984 for all .In terms of stress, the R's of all datasets were greater than 0.981 and R=0.97,1 and 1 for training, validation and test respectively .  After comparing the experimental data to the expected data, the ANN modeling demonstrated an outstanding correlation, with an estimated average error value of 10 − 2 for stress and 10 -0 for Young‘s Modulus .  For the prediction of mechanical properties like stress and Young's Modulus values, an optimal network was employed with a training set size of 70%, validation set size of 15%, and test set size of 15%. The mean squared error (MSE) and the correlation coefficients were utilized as the evaluation metric in this study for determining the optimal network performance of ANN.

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