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

Author

Listed:
  • 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)
    Pohang University of Science and Technology (POSTECH)
    Yonsei University
    Tohoku University)

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.

Suggested Citation

  • Jeong Ah Lee & Jaejung Park & Man Jae Sagong & Soung Yeoul Ahn & Jung-Wook Cho & Seungchul Lee & Hyoung Seop Kim, 2025. "Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56267-1
    DOI: 10.1038/s41467-025-56267-1
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