  This study presents an innovative application of the Taguchi design of
experiment method to optimize the structure of an Artificial Neural Network
(ANN) model for the prediction of elastic properties of short fiber reinforced
composites. The main goal is to minimize the required computational effort for
hyperparameter optimization while enhancing the prediction accuracy. Utilizing
a robust design of experiment framework, the structure of an ANN model is
optimized. This essentially is the identification of a combination of
hyperparameters that yields an optimal predictive accuracy with the fewest
algorithmic runs, thereby achieving a significant reduction of the required
computational effort. Our findings demonstrate that the Taguchi method not only
streamlines the hyperparameter tuning process but also could substantially
improve the algorithm's performance. These results underscore the potential of
the Taguchi method as a powerful tool for optimizing machine learning
algorithms, particularly in scenarios where computational resources are
limited. The implications of this study are far-reaching, offering insights for
future research in the optimization of different algorithms for improved
accuracies and computational efficiencies.
