An active-learning Gaussian process model predicted WAAM bead geometry with lower error (RMSE 1.00, R² 0.89) than a Taguchi L25 design (RMSE 1.33, R² 0.80) while using fewer training experiments.
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Enhancing Experimental Efficiency in Materials Design: A Comparative Study of Taguchi and Machine Learning Methods
An active-learning Gaussian process model predicted WAAM bead geometry with lower error (RMSE 1.00, R² 0.89) than a Taguchi L25 design (RMSE 1.33, R² 0.80) while using fewer training experiments.