| Autor: |
Luke N. Carter, Victor M. Villapún, James Andrews, Thomas R.B. Grandjean, John Dardis, Sophie C. Cox |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Additive Manufacturing Letters, Vol 11, Iss , Pp 100252- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2772-3690 |
| DOI: |
10.1016/j.addlet.2024.100252 |
| Popis: |
Quality assurance remains a significant challenge for laser powder bed fusion and metal additive manufacturing. Despite system manufacturers offering process monitoring as a possible solution, datasets are large and cumbersome with practical use limited without direct comparative data. Model datasets would enable individual build validation, highlight deviations, and facilitate intelligent build planning whereby challenging features or build strategies could be pre-emptively assessed.Herein a pragmatic approach has been developed to model process monitoring data from a commercial system using a relatively simple algorithm. Using a heuristic method, the algorithm response has been fitted to an experimental dataset to derive governing constants and their relationship to key process parameters. Predictability of constants and model fit has been shown to improve with increasing line energy up to a maximum R2=0.8. Algorithm variable trends, supported by corresponding sensitivity analysis, identified two different behavioural regimes. Under low linear energy density ( |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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