Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook
Metal additive manufacturing is gaining immense research attention. Some of these research efforts are associated with physics, statistical, or artificial intelligence-driven process modelling and optimisation, structure–property characterisation, structural design optimisation, or equipment enhance...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Virtual and Physical Prototyping |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17452759.2023.2181192 |
| _version_ | 1827811243529863168 |
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| author | Osezua Ibhadode Zhidong Zhang Jeffrey Sixt Ken M. Nsiempba Joseph Orakwe Alexander Martinez-Marchese Osazee Ero Shahriar Imani Shahabad Ali Bonakdar Ehsan Toyserkani |
| author_facet | Osezua Ibhadode Zhidong Zhang Jeffrey Sixt Ken M. Nsiempba Joseph Orakwe Alexander Martinez-Marchese Osazee Ero Shahriar Imani Shahabad Ali Bonakdar Ehsan Toyserkani |
| author_sort | Osezua Ibhadode |
| collection | DOAJ |
| description | Metal additive manufacturing is gaining immense research attention. Some of these research efforts are associated with physics, statistical, or artificial intelligence-driven process modelling and optimisation, structure–property characterisation, structural design optimisation, or equipment enhancements for cost reduction and faster throughputs. In this review, the focus is drawn on the utilisation of topology optimisation for structural design in metal additive manufacturing. First, the symbiotic relationship between topology optimisation and metal additive manufacturing in aerospace, medical, automotive, and other industries is investigated. Second, support structure design by topology optimisation for thermal-based powder-bed processes is discussed. Third, the introduction of capabilities to limit manufacturing constraints and generate porous features in topology optimisation is examined. Fourth, emerging efforts to adopt artificial intelligence models are examined. Finally, some open-source and commercial software with capabilities for topology optimisation and metal additive manufacturing are explored. This study considers the challenges faced while providing perceptions on future research directions. |
| first_indexed | 2024-03-11T23:03:06Z |
| format | Article |
| id | doaj.art-eec0aac0021746fa9de6f4ebfcac6696 |
| institution | Directory Open Access Journal |
| issn | 1745-2759 1745-2767 |
| language | English |
| last_indexed | 2024-03-11T23:03:06Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Virtual and Physical Prototyping |
| spelling | doaj.art-eec0aac0021746fa9de6f4ebfcac66962023-09-21T14:38:04ZengTaylor & Francis GroupVirtual and Physical Prototyping1745-27591745-27672023-12-0118110.1080/17452759.2023.21811922181192Topology optimization for metal additive manufacturing: current trends, challenges, and future outlookOsezua Ibhadode0Zhidong Zhang1Jeffrey Sixt2Ken M. Nsiempba3Joseph Orakwe4Alexander Martinez-Marchese5Osazee Ero6Shahriar Imani Shahabad7Ali Bonakdar8Ehsan Toyserkani9University of AlbertaUniversity of WaterlooUniversity of WaterlooUniversity of WaterlooUniversity of WaterlooUniversity of WaterlooUniversity of WaterlooUniversity of WaterlooSiemens Energy Canada LimitedUniversity of WaterlooMetal additive manufacturing is gaining immense research attention. Some of these research efforts are associated with physics, statistical, or artificial intelligence-driven process modelling and optimisation, structure–property characterisation, structural design optimisation, or equipment enhancements for cost reduction and faster throughputs. In this review, the focus is drawn on the utilisation of topology optimisation for structural design in metal additive manufacturing. First, the symbiotic relationship between topology optimisation and metal additive manufacturing in aerospace, medical, automotive, and other industries is investigated. Second, support structure design by topology optimisation for thermal-based powder-bed processes is discussed. Third, the introduction of capabilities to limit manufacturing constraints and generate porous features in topology optimisation is examined. Fourth, emerging efforts to adopt artificial intelligence models are examined. Finally, some open-source and commercial software with capabilities for topology optimisation and metal additive manufacturing are explored. This study considers the challenges faced while providing perceptions on future research directions.http://dx.doi.org/10.1080/17452759.2023.2181192metal additive manufacturingadditive manufacturingtopology optimisationaerospaceautomotivemedical |
| spellingShingle | Osezua Ibhadode Zhidong Zhang Jeffrey Sixt Ken M. Nsiempba Joseph Orakwe Alexander Martinez-Marchese Osazee Ero Shahriar Imani Shahabad Ali Bonakdar Ehsan Toyserkani Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook Virtual and Physical Prototyping metal additive manufacturing additive manufacturing topology optimisation aerospace automotive medical |
| title | Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook |
| title_full | Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook |
| title_fullStr | Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook |
| title_full_unstemmed | Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook |
| title_short | Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook |
| title_sort | topology optimization for metal additive manufacturing current trends challenges and future outlook |
| topic | metal additive manufacturing additive manufacturing topology optimisation aerospace automotive medical |
| url | http://dx.doi.org/10.1080/17452759.2023.2181192 |
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