The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Melt pool temperature and cooling rates in laser powder bed fusion
Open accessAbstract
In laser powder bed fusion, melt pool dynamics and stability are driven by the temperature field in the melt pool. If the temperature field is unfavourable defects are likely to form. The localised and rapid heating and cooling in the process presents a challenge for the experimental methods used to measure temperature. As a result, understanding of these process fundamentals is limited. In this paper a method is developed that uses coaxial imaging with high-speed cameras to give both the spatial and temporal resolution necessary to resolve the surface temperature of the melt pool. A two wavelength imaging setup is used to account for changes in emissivity. Temperature fields are captured at 100 kHz with a resolution of 20 μm during the processing of a simple Ti6Al4V component. Thermal gradients in the range 5–20 K/μm and cooling rates in range 1–40 K/μs are measured. The results presented give new insight into the effect of parameters, geometry and scan path on the melt pool temperature and cooling rates. The method developed here provides a new tool to assist in optimising scan strategies and parameters, identifying the causes of defect prone locations and controlling cooling rates for local microstructure development.
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Khairallah, S. A., Anderson, A. T., Rubenchik, A., & King, W. E. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.02.014 · Cited by 2,766
Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing
Everton, S. K., Hirsch, M., Stravroulakis, P., Leach, R. K., & Clare, A. T. (2016) · Materials & Design · doi:10.1016/j.matdes.2016.01.099 · Cited by 1,469
Denudation of metal powder layers in laser powder bed fusion processes
Matthews, M. J., Guss, G., Khairallah, S. A., Rubenchik, A. M., Depond, P. J., & King, W. E. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.05.017 · Cited by 809
Zhao, C., Fezzaa, K., Cunningham, R. W., Wen, H., De Carlo, F., Chen, L., Rollett, A. D., & Sun, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-03761-2 · Cited by 758
In-process sensing in selective laser melting (SLM) additive manufacturing
Spears, T. G., & Gold, S. A. (2016) · Integrating Materials and Manufacturing Innovation · doi:10.1186/s40192-016-0045-4 · Cited by 495
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Quality control of laser- and powder bed-based Additive Manufacturing (AM) technologies
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Cited by
Alloys-by-design: Application to new superalloys for additive manufacturing
Tang, Y. T., Panwisawas, C., Ghoussoub, J. N., Gong, Y., Clark, J. W. G., Németh, A. A. N., McCartney, D. G., & Reed, R. C. (2021) · Acta Materialia · doi:10.1016/j.actamat.2020.09.023 · Cited by 678
Additive manufacturing of steels: a review of achievements and challenges
Haghdadi, N., Laleh, M., Moyle, M., & Primig, S. (2021) · Journal of Materials Science · doi:10.1007/s10853-020-05109-0 · Cited by 622
Metadata
- Authors
- Hooper, P. A.
- Venue
- Additive Manufacturing
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 22
- Pages
- 548-559
- ISSN
- 2214-7810 (ISSN-L)
- 2214-8604
- OpenAlex
- W2803999279
- Cited by
- 707
- References
- 13
- FWCI
- 30.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
