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Comparative analysis of machine learning models for engineering material classification using explainable artificial intelligence

Samuel Appiah-Kubi, Adobea Rosalinda Okyere

Classifying engineering materials automatically and accurately remains one of the persistent challenges in materials informatics, with practical conse- quences for materials selection, quality control, and database management. This study compares five supervised machine learning (ML) algorithms Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), Logis…

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Structural modifications of hexa-chiral auxetics for lightweight energy absorption

Alexander Engel, Sergej Grednev, Prateek Sharma, Anne Jung

Auxetic materials, characterized by their negative Poisson’s ratio, exhibit a unique deformation behavior and exceptional energy absorption properties. This study focuses on the structural modification of a hexa-chiral auxetic structure to enhance its energy absorption capacity by counteracting premature buckling of boundary layers and cylindrical units. Hence, the standard hex…

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