High-throughput alloy and process design for metal additive manufacturing
Abstract Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01670-x |
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| _version_ | 1850218583422926848 |
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| author | Sofia Sheikh Brent Vela Pejman Honarmandi Peter Morcos David Shoukr Abdelrahman Mostafa Kotb Ibrahim Karaman Alaa Elwany Raymundo Arróyave |
| author_facet | Sofia Sheikh Brent Vela Pejman Honarmandi Peter Morcos David Shoukr Abdelrahman Mostafa Kotb Ibrahim Karaman Alaa Elwany Raymundo Arróyave |
| author_sort | Sofia Sheikh |
| collection | DOAJ |
| description | Abstract Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces. |
| format | Article |
| id | doaj-art-a19bfbeea07d429b90d49e470bb1793f |
| institution | OA Journals |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-a19bfbeea07d429b90d49e470bb1793f2025-08-20T02:07:40ZengNature Portfolionpj Computational Materials2057-39602025-06-0111112110.1038/s41524-025-01670-xHigh-throughput alloy and process design for metal additive manufacturingSofia Sheikh0Brent Vela1Pejman Honarmandi2Peter Morcos3David Shoukr4Abdelrahman Mostafa Kotb5Ibrahim Karaman6Alaa Elwany7Raymundo Arróyave8Department of Materials Science and Engineering, Texas A&M UniversityDepartment of Materials Science and Engineering, Texas A&M UniversityDepartment of Materials Science and Engineering, Texas A&M UniversityDepartment of Materials Science and Engineering, Texas A&M UniversityDepartment of Industrial and Systems Engineering, Texas A&M UniversityDepartment of Industrial and Systems Engineering, Texas A&M UniversityDepartment of Materials Science and Engineering, Texas A&M UniversityDepartment of Materials Science and Engineering, Texas A&M UniversityDepartment of Materials Science and Engineering, Texas A&M UniversityAbstract Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces.https://doi.org/10.1038/s41524-025-01670-x |
| spellingShingle | Sofia Sheikh Brent Vela Pejman Honarmandi Peter Morcos David Shoukr Abdelrahman Mostafa Kotb Ibrahim Karaman Alaa Elwany Raymundo Arróyave High-throughput alloy and process design for metal additive manufacturing npj Computational Materials |
| title | High-throughput alloy and process design for metal additive manufacturing |
| title_full | High-throughput alloy and process design for metal additive manufacturing |
| title_fullStr | High-throughput alloy and process design for metal additive manufacturing |
| title_full_unstemmed | High-throughput alloy and process design for metal additive manufacturing |
| title_short | High-throughput alloy and process design for metal additive manufacturing |
| title_sort | high throughput alloy and process design for metal additive manufacturing |
| url | https://doi.org/10.1038/s41524-025-01670-x |
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