Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility

Jeong Ah Lee, Jaejung Park, Man Jae Sagong, Soung Yeoul Ahn, Jung-Wook Cho, Seungchul Lee () and Hyoung Seop Kim ()
Additional contact information
Jeong Ah Lee: Pohang University of Science and Technology (POSTECH)
Jaejung Park: Korea Advanced Institute of Science and Technology (KAIST)
Man Jae Sagong: Pohang University of Science and Technology (POSTECH)
Soung Yeoul Ahn: Pohang University of Science and Technology (POSTECH)
Jung-Wook Cho: Pohang University of Science and Technology (POSTECH)
Seungchul Lee: Korea Advanced Institute of Science and Technology (KAIST)
Hyoung Seop Kim: Pohang University of Science and Technology (POSTECH)

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Optimizing process and heat-treatment parameters of laser powder bed fusion for producing Ti-6Al-4V alloys with high strength and ductility is crucial to meet performance demands in various applications. Nevertheless, inherent trade-offs between strength and ductility render traditional trial-and-error methods inefficient. Herein, we present Pareto active learning framework with targeted experimental validation to efficiently explore vast parameter space of 296 candidates, pinpointing optimal parameters to augment both strength and ductility. All Ti-6Al-4V alloys produced with the pinpointed parameters exhibit higher ductility at similar strength levels and greater strength at similar ductility levels compared to those in previous studies. By improving one property without significantly compromising the other, the framework demonstrates efficiency in overcoming the inherent trade-offs. Ultimately, Ti-6Al-4V alloys with ultimate tensile strength and total elongation of 1190 MPa and 16.5%, respectively, are produced. The proposed framework streamlines discovery of optimal processing parameters and promises accelerated development of high-performance alloys.

Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-56267-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56267-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-56267-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().