ArticleScientific reports2025
Machine learning-based prediction of wear behaviour of AZ31 hybrid composites reinforced with NbC and ZrC.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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5 authors.
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Abstract
AZ31/ (NbC + ZrC) hybrid composites were fabricated using friction stir processing by incorporating a combination of rare-earth NbC and ZrC particulates. An investigation was carried out on the microstructure, tensile strength, hardness and wear behaviour of the hybrid composites. Compared to AZ31 alloy, hybrid composites microstructure showed significant reduction in the mean grain size (4–6 μm). The tensile strength and hardness of the hybrid composite was enhanced from 239 to 316 MPa and 62 ± 2 HV to 122 ± 3 HV for AZ31/12 vol% NbC and ZrC hybrid composites. Hybrid composites exhibited significant reduction in friction coefficient and wear rate. SEM analysis on worn surface was carried out to find the wear mechanisms. The scatter plot assesses the performance of Linear regression in predicting wear rates and friction coefficient yielding an exceptionally high correlation coefficient (R = 0.9959) and (R = 0.9637) indicating a near-perfect relationship between predicted and actual values respectively.
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