Delamination Prediction in Layered Composites Using Optimized ANN Algorithms: A Comparative Analysis

Abstract

This study investigates the effectiveness of Artificial Neural Networks (ANNs) in predicting the outcomes of Double Cantilever Beam (DCB) tests, focusing on time and force as input variables and displacement as the predicted output. Three ANN training algorithms—Scaled Conjugate Gradient (SCG), Broyden Fletcher Goldfarb Shanno (BFGS) Quasi-Newton, and Levenberg-Marquardt (LM)—were evaluated based on prediction accuracy and computational efficiency. A parametric study was performed by varying the number of neurons (from 10 to 100) in a single hidden layer to optimize network structure. Among the evaluated algorithms, LM demonstrated superior performance, achieving prediction accuracies of 99.6% for force and 99.3% for displacement. In contrast, SCG exhibited the fastest convergence but had a significantly higher error rate of 8.6%. The BFGS algorithm provided a compromise between accuracy and speed but was ultimately outperformed by LM in terms of overall precision. In addition, configurations with up to 100 neurons were tested, indicating that although slightly lower error rates could be achieved, the increase in computation time was substantial. Consequently, the LM algorithm with 50 neurons delivered the best balance between accuracy and computational cost. These findings underscore the potential of ANNs, particularly LM-based models, to enhance material design processes by providing reliable predictions from limited experimental data, thereby reducing both resource utilization and the time required for testing.