New era towards autonomous additive manufacturing: a review of recent trends and future perspectives
The additive manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of artificial intelligence (AI) and digital twins (DT). However, current intelligent AM (IAM) systems face limitations such as fragmented AI tool usa...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | International Journal of Extreme Manufacturing |
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| Online Access: | https://doi.org/10.1088/2631-7990/ada8e4 |
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| _version_ | 1832576015951462400 |
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| author | Haolin Fan Chenshu Liu Shijie Bian Changyu Ma Junlin Huang Xuan Liu Marshall Doyle Thomas Lu Edward Chow Lianyi Chen Jerry Ying Hsi Fuh Wen Feng Lu Bingbing Li |
| author_facet | Haolin Fan Chenshu Liu Shijie Bian Changyu Ma Junlin Huang Xuan Liu Marshall Doyle Thomas Lu Edward Chow Lianyi Chen Jerry Ying Hsi Fuh Wen Feng Lu Bingbing Li |
| author_sort | Haolin Fan |
| collection | DOAJ |
| description | The additive manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of artificial intelligence (AI) and digital twins (DT). However, current intelligent AM (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction. This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to autonomous AM, featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing. Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem. |
| format | Article |
| id | doaj-art-cd5b171addb746a284d5a125fdc4a479 |
| institution | Kabale University |
| issn | 2631-7990 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | International Journal of Extreme Manufacturing |
| spelling | doaj-art-cd5b171addb746a284d5a125fdc4a4792025-01-31T13:49:28ZengIOP PublishingInternational Journal of Extreme Manufacturing2631-79902025-01-017303200610.1088/2631-7990/ada8e4New era towards autonomous additive manufacturing: a review of recent trends and future perspectivesHaolin Fan0https://orcid.org/0000-0002-1808-2660Chenshu Liu1https://orcid.org/0009-0006-5037-3093Shijie Bian2https://orcid.org/0000-0003-2814-9854Changyu Ma3https://orcid.org/0000-0003-2321-8080Junlin Huang4https://orcid.org/0009-0001-4292-1786Xuan Liu5https://orcid.org/0000-0002-7922-9451Marshall Doyle6Thomas Lu7https://orcid.org/0009-0002-6507-5954Edward Chow8https://orcid.org/0000-0003-2155-2464Lianyi Chen9https://orcid.org/0000-0003-3720-398XJerry Ying Hsi Fuh10https://orcid.org/0000-0002-5225-7460Wen Feng Lu11https://orcid.org/0000-0003-4022-6912Bingbing Li12https://orcid.org/0000-0001-6140-4189Autonomy Research Center for STEAHM (ARCS), California State University Northridge , 18111 Nordhoff St, Northridge, CA 91330, United States of America; Department of Mechanical Engineering, National University of Singapore , 9 Engineering Drive 1, Singapore 117575, SingaporeAutonomy Research Center for STEAHM (ARCS), California State University Northridge , 18111 Nordhoff St, Northridge, CA 91330, United States of AmericaAutonomy Research Center for STEAHM (ARCS), California State University Northridge , 18111 Nordhoff St, Northridge, CA 91330, United States of AmericaAutonomy Research Center for STEAHM (ARCS), California State University Northridge , 18111 Nordhoff St, Northridge, CA 91330, United States of AmericaDepartment of Mechanical Engineering, National University of Singapore , 9 Engineering Drive 1, Singapore 117575, SingaporeDepartment of Mechanical Engineering, National University of Singapore , 9 Engineering Drive 1, Singapore 117575, SingaporeAutonomy Research Center for STEAHM (ARCS), California State University Northridge , 18111 Nordhoff St, Northridge, CA 91330, United States of AmericaJet Propulsion Laboratory, California Institute of Technology , 4800 Oak Grove Dr, Pasadena, CA 91109, United States of AmericaJet Propulsion Laboratory, California Institute of Technology , 4800 Oak Grove Dr, Pasadena, CA 91109, United States of AmericaDepartment of Mechanical Engineering, University of Wisconsin-Madison , 1513 University Ave, Madison, WI 53706, United States of AmericaDepartment of Mechanical Engineering, National University of Singapore , 9 Engineering Drive 1, Singapore 117575, SingaporeDepartment of Mechanical Engineering, National University of Singapore , 9 Engineering Drive 1, Singapore 117575, SingaporeAutonomy Research Center for STEAHM (ARCS), California State University Northridge , 18111 Nordhoff St, Northridge, CA 91330, United States of America; Department of Mechanical Engineering, National University of Singapore , 9 Engineering Drive 1, Singapore 117575, SingaporeThe additive manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of artificial intelligence (AI) and digital twins (DT). However, current intelligent AM (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction. This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to autonomous AM, featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing. Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem.https://doi.org/10.1088/2631-7990/ada8e4future manufacturingautonomous additive manufacturingartificial intelligence agentlarge multimodal modelsknowledge graphs |
| spellingShingle | Haolin Fan Chenshu Liu Shijie Bian Changyu Ma Junlin Huang Xuan Liu Marshall Doyle Thomas Lu Edward Chow Lianyi Chen Jerry Ying Hsi Fuh Wen Feng Lu Bingbing Li New era towards autonomous additive manufacturing: a review of recent trends and future perspectives International Journal of Extreme Manufacturing future manufacturing autonomous additive manufacturing artificial intelligence agent large multimodal models knowledge graphs |
| title | New era towards autonomous additive manufacturing: a review of recent trends and future perspectives |
| title_full | New era towards autonomous additive manufacturing: a review of recent trends and future perspectives |
| title_fullStr | New era towards autonomous additive manufacturing: a review of recent trends and future perspectives |
| title_full_unstemmed | New era towards autonomous additive manufacturing: a review of recent trends and future perspectives |
| title_short | New era towards autonomous additive manufacturing: a review of recent trends and future perspectives |
| title_sort | new era towards autonomous additive manufacturing a review of recent trends and future perspectives |
| topic | future manufacturing autonomous additive manufacturing artificial intelligence agent large multimodal models knowledge graphs |
| url | https://doi.org/10.1088/2631-7990/ada8e4 |
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