Machine Learning in Additive Manufacturing: A Review
| dc.contributor.author | Meng, Lingbin | |
| dc.contributor.author | McWilliams, Brandon | |
| dc.contributor.author | Jarosinski, William | |
| dc.contributor.author | Park, Hye-Yeong | |
| dc.contributor.author | Jung, Yeon-Gil | |
| dc.contributor.author | Lee, Jehyun | |
| dc.contributor.author | Zhang, Jing | |
| dc.contributor.department | Engineering Technology, School of Engineering and Technology | en_US |
| dc.date.accessioned | 2020-06-17T17:10:40Z | |
| dc.date.available | 2020-06-17T17:10:40Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering. The performance of various ML algorithms in these types of AM tasks are compared and evaluated. Finally, several future research directions are suggested. | en_US |
| dc.eprint.version | Author's manuscript | en_US |
| dc.identifier.citation | Meng, L., McWilliams, B., Jarosinski, W., Park, H. Y., Jung, Y. G., Lee, J., & Zhang, J. (2020). Machine Learning in Additive Manufacturing: A Review. JOM, 1-15. https://doi.org/10.1007/s11837-020-04155-y | en_US |
| dc.identifier.uri | https://hdl.handle.net/1805/22982 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.relation.isversionof | 10.1007/s11837-020-04155-y | en_US |
| dc.relation.journal | JOM | en_US |
| dc.rights | IUPUI Open Access Policy | en_US |
| dc.source | Author | en_US |
| dc.subject | additive manufacturing | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | ML algorithms | en_US |
| dc.title | Machine Learning in Additive Manufacturing: A Review | en_US |
| dc.type | Article | en_US |