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Machine Learning-Based Prediction of Fatigue Strength in Additively Manufactured Ti-6Al-4V Parts: A Sensitivity Analysis of Input Features

Crossref DOI link: https://doi.org/10.1007/s11665-025-11115-7

Published Online: 2025-04-23

Published Print: 2025-08

Update policy: https://doi.org/10.1007/springer_crossmark_policy

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