Predicting stress–strain relationships of additively manufactured materials under compression and tension using transfer learning and Wasserstein distance-based dataset pruning

Predicting stress–strain relationships of additively manufactured materials under compression and tension using transfer learning and Wasserstein distance-based dataset pruning

Additive manufacturing enables the design of materials and structures with tunable mechanical properties. However, predicting the stress–strain relationships of additively manufactured materials remains a challenge due to complex process-structure–property relationships in additive manufacturing. Wh...

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Bibliographic Details
Main Authors: Chenglong Duan, Dazhong Wu
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Materials & Design
Subjects:
Transfer learning
Stress–strain relationship
Additive manufacturing
Dataset pruning
Wasserstein distance
Online Access:http://www.sciencedirect.com/science/article/pii/S026412752500629X
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