Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility
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 tri...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56267-1 |
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| author | Jeong Ah Lee Jaejung Park Man Jae Sagong Soung Yeoul Ahn Jung-Wook Cho Seungchul Lee Hyoung Seop Kim |
| author_facet | Jeong Ah Lee Jaejung Park Man Jae Sagong Soung Yeoul Ahn Jung-Wook Cho Seungchul Lee Hyoung Seop Kim |
| author_sort | Jeong Ah Lee |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-48a7ec704edc48399d9a907fa9c1fe68 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-48a7ec704edc48399d9a907fa9c1fe682025-01-26T12:40:18ZengNature PortfolioNature Communications2041-17232025-01-0116111410.1038/s41467-025-56267-1Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductilityJeong Ah Lee0Jaejung Park1Man Jae Sagong2Soung Yeoul Ahn3Jung-Wook Cho4Seungchul Lee5Hyoung Seop Kim6Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST)Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)Graduate Institute of Ferrous & Eco Materials Technology, Pohang University of Science and Technology (POSTECH)Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST)Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH)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.https://doi.org/10.1038/s41467-025-56267-1 |
| spellingShingle | Jeong Ah Lee Jaejung Park Man Jae Sagong Soung Yeoul Ahn Jung-Wook Cho Seungchul Lee Hyoung Seop Kim Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility Nature Communications |
| title | Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility |
| title_full | Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility |
| title_fullStr | Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility |
| title_full_unstemmed | Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility |
| title_short | Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility |
| title_sort | active learning framework to optimize process parameters for additive manufactured ti 6al 4v with high strength and ductility |
| url | https://doi.org/10.1038/s41467-025-56267-1 |
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