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Learning composition-sensitive signatures in multi-material PBF-LB: a lightweight, modality-aware, explainable graph-attention sensor fusion framework for in-situ monitoring of graded 316L–CuCrZr alloys

Crossref DOI link: https://doi.org/10.1007/s40964-026-01854-x

Published Online: 2026-07-23

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

Authors

Pandiyan, Vigneashwara https://orcid.org/0000-0001-6043-5696
Baganis, Antonios

Salminen, Antti

Leinenbach, Christian
Funding

Funding for this research was provided by:

University of Turku
License Information

Text and Data Mining valid from 2026-07-23

Version of Record valid from 2026-07-23
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Article History

Received: 21 January 2026

Accepted: 6 July 2026

First Online: 23 July 2026

Declarations

Conflict Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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