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Pavan Kumar Asur Vijaya Kumar
Institute of Lightweight Design and Structural Biomechanics, TU Wien, Getreidemarkt 9, 1060 Wien
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering

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Stress Distribution Prediction in front of the crack tip using Artificial Neural Networks: A Data-Driven Approach in Fracture Mechanics Khairul Anam; Anindito Purnowidodo; Pavan Kumar Asur Vijaya Kumar; Heinz E. Pettermann
Journal of Engineering and Technological Sciences Vol. 58 No. 6 (2026): Vol. 58 No. 6(2026): December
Publisher : Directorate for Research and Innovation, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2026.58.6.10

Abstract

Understanding the stress distribution in front of the crack tip is essential because this region experiences high stress concentration that can control crack initiation and propagation. This study proposes an artificial neural network (ANN) model as a computationally efficient surrogate model for predicting the stress distribution and stress values in front of the crack tip in a simple cracked geometry. The proposed ANN model complements conventional finite element method (FEM) analyses by enabling rapid stress prediction once the model has been trained. The results show that the ANN model can capture the general relationship between the stress intensity factor, radial distance, angular position, and crack-tip stress. However, its predictive performance strongly depends on the magnitude of the stress intensity factor. At low K values, the ANN model shows significant discrepancies in both stress values and contour visualization, and it cannot accurately reproduce the theoretical butterfly-shaped stress distribution. At higher K values, the predicted stress distribution is closer to the analytical solution, and the contour shape is more consistent. These findings indicate that the present ANN model should be regarded as a proof-of-concept surrogate model with limitations, rather than a replacement for analytical LEFM solutions or FEM analysis.
Co-Authors Anindito Purnowidodo Heinz E. Pettermann Khairul Anam