The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Machine Learning in Additive Manufacturing: A Review
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
(2014) · Cited by 33,419
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,414
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,218
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
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276
Cited by
Sing, S. L., Huang, S., Goh, G. D., Goh, G. L., Tey, C. F., Tan, J. H. K., & Yeong, W. Y. (2021) · Progress in Materials Science · doi:10.1016/j.pmatsci.2021.100795 · Cited by 328
Ukwaththa, J., Herath, S., & Meddage, D. P. P. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.110294 · Cited by 118
Tawil, A.-R. H., Mohamed, M., Schmoor, X., Vlachos, K., & Haidar, D. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8070079 · Cited by 44
Hyperspectral imaging for prediction of surface roughness in laser powder bed fusion
Gerdes, N., Hoff, C., Hermsdorf, J., Kaierle, S., & Overmeyer, L. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-07274-1 · Cited by 36
Metadata
- Authors
- Meng, L., McWilliams, B., Jarosinski, W., Park, H.-Y., Jung, Y.-G., Lee, J., & Zhang, J.
- Venue
- JOM
- Published
- 2020-04-17
- Volume
- 72
- Issue
- 6
- Pages
- 2363-2377
- ISSN
- 0148-6608 (ISSN-L)
- 1047-4838
- 1543-1851
- OpenAlex
- W3016989108
- Cited by
- 470
- References
- 54
- FWCI
- 23.22
- Type
- journal article
- Language
- en
- Open access
- green
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- 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. https://doi.org/10.1007/s11837-020-04155-y
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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.
