The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges
Open accessAbstract
The production of metal parts via laser powder bed fusion additive manufacturing is growing exponentially. However, the transition of this technology from production of prototypes to production of critical parts is hindered by a lack of confidence in the quality of the part. Confidence can be established via a fundamental understanding of the physics of the process. It is generally accepted that this understanding will be increasingly achieved through modeling and simulation. However, there are significant physics, computational, and materials challenges stemming from the broad range of length and time scales and temperature ranges associated with the process. In this paper, we review the current state of the art and describe the challenges that need to be met to achieve the desired fundamental understanding of the physics of the process.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Classification and regression trees
Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053
Inverse Problem Theory and Methods for Model Parameter Estimation
Tarantola, A. (2005) · doi:10.1137/1.9780898717921 · Cited by 6,585
Metal Additive Manufacturing: A Review
Frazier, W. E. (2014) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-014-0958-z · Cited by 5,984
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Hall, M. A. (2000) · Cited by 1,715
Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing
King, W. E., Barth, H. D., Castillo, V. M., Gallegos, G. F., Gibbs, J. W., Hahn, D. E., Kamath, C., & Rubenchik, A. M. (2014) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2014.06.005 · Cited by 1,616
Self-citationDesign and Modeling for Computer Experiments
Fang, K.-T., Li, R., & Sudjianto, A. (2005) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420034899 · Cited by 1,138
Design and Analysis of Experiments: Advanced Experimental Design
Hinkelmann, K., & Kempthorne, O. (2005) · Wiley Series in Probability and Statistics · doi:10.1002/0471709948 · Cited by 961
Single track formation in selective laser melting of metal powders
Yadroitsev, I., Gusarov, A., Yadroitsava, I., & Smurov, I. (2010) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2010.05.010 · Cited by 827
Hussein, A., Hao, L., Yan, C., & Everson, R. (2013) · Materials & Design (1980-2015) · doi:10.1016/j.matdes.2013.05.070 · Cited by 706
Roberts, I. A., Wang, C., Esterlein, R., et al. (2009) · International Journal of Machine Tools and Manufacture · doi:10.1016/j.ijmachtools.2009.07.004 · Cited by 705
Cited by
Additive manufacturing of metallic components – Process, structure and properties
DebRoy, T., Wei, H. L., Zuback, J. S., Mukherjee, T., Elmer, J. W., Milewski, J. O., Beese, A. M., Wilson-Heid, A., De, A., & Zhang, W. (2018) · Progress in Materials Science · doi:10.1016/j.pmatsci.2017.10.001 · Cited by 8,805
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
Self-citationMetal additive manufacturing in aerospace: A review
Blakey-Milner, B., Gradl, P., Snedden, G., Brooks, M., Pitot, J., Lopez, E., Leary, M., Berto, F., & du Plessis, A. (2021) · Materials & Design · doi:10.1016/j.matdes.2021.110008 · Cited by 2,263
An overview of residual stresses in metal powder bed fusion
Bartlett, J. L., & Li, X. (2019) · Additive Manufacturing · doi:10.1016/j.addma.2019.02.020 · Cited by 616
Change in microstructure of selectively laser melted AlSi10Mg alloy with heat treatments
Takata, N., Kodaira, H., Sekizawa, K., Suzuki, A., & Kobashi, M. (2017) · Materials Science and Engineering: A · doi:10.1016/j.msea.2017.08.029 · Cited by 574
Metadata
- Authors
- King, W. E., Anderson, A. T., Ferencz, R. M., Hodge, N. E., Kamath, C., Khairallah, S. A., & Rubenchik, A. M.
- Venue
- Applied Physics Reviews
- Publisher
- AIP Publishing
- Published
- 2015-12
- Volume
- 2
- Issue
- 4
- Pages
- 041304
- ISSN
- 1931-9401 (ISSN-L)
- OpenAlex
- W2210796340
- Cited by
- 1,258
- References
- 43
- FWCI
- 46.42
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges. https://app.cochat.ai/papers/10.1063/1.4937809
- The paper
- King, W. E., Anderson, A. T., Ferencz, R. M., Hodge, N. E., Kamath, C., Khairallah, S. A., & Rubenchik, A. M. (2015). Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges. Applied Physics Reviews. https://doi.org/10.1063/1.4937809
Data sources
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.
