<ul><li>Materials design often involves optimizing multiple variables, making full factorial exploration impractical.</li><li>While Taguchi technique is commonly used for efficient sampling, it struggles to capture non-linear variable dependencies.</li><li>A study compared Taguchi method with a machine learning approach using Gaussian process regression in a wire arc additive manufacturing process.</li><li>The machine learning model outperformed Taguchi in accuracy and efficiency, showcasing its potential for exploring complex material processing parameters.</li></ul>

Enhancing Experimental Efficiency in Materials Design: A Comparative Study of Taguchi and Machine Learning Methods

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