In welded tubular joints, when the fatigue crack depth is less than 20% of chord wall thickness, the crack growing process is highly affected by weld geometry.
Hence, T-butt solution and weld magnification factor (Mk) are applicable tools for evaluating the crack growth rate in this domain. In this research, the capability of Artificial Neural Network (ANN) for estimating the Mk of weld toe cracks in T-butt joints is investigated. Four Multi-Layer Perceptron (MLP) networks are designed and trained to predict the Mk in deepest point and ends of weld toe cracks under membrane and bending stresses. Training and testing data of networks are extracted from a reputable resource on finite element modeling. Comparison of the results obtained and those from the most recently published equations shows that using ANN seems to be very beneficial in this field
A. Fathi, , A. A. Aghakuchak, , & and Gh. A. Montazer, (2022). Evaluating Weld Magnification Factor in Welded Tubular Joints Using Artifitial Neural Networks. Journal of Advanced Materials in Engineering (Esteghlal), 26(2), 15-29.
MLA
A. Fathi; A. A. Aghakuchak; and Gh. A. Montazer. "Evaluating Weld Magnification Factor in Welded Tubular Joints Using Artifitial Neural Networks", Journal of Advanced Materials in Engineering (Esteghlal), 26, 2, 2022, 15-29.
HARVARD
A. Fathi, , A. A. Aghakuchak, , and Gh. A. Montazer, (2022). 'Evaluating Weld Magnification Factor in Welded Tubular Joints Using Artifitial Neural Networks', Journal of Advanced Materials in Engineering (Esteghlal), 26(2), pp. 15-29.
VANCOUVER
A. Fathi, , A. A. Aghakuchak, , and Gh. A. Montazer, Evaluating Weld Magnification Factor in Welded Tubular Joints Using Artifitial Neural Networks. Journal of Advanced Materials in Engineering (Esteghlal), 2022; 26(2): 15-29.
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