The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Machine learning models can be developed to predict mechanical properties of materials fabricated through additive manufacturing as they can learn and model highly complex relationships [438-445].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [440]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
(2013) · Medical Entomology and Zoology · Cited by 690
Campbell, I., Diegel, O., Kowen, J., & Wohlers, T. T. (2018) · ResearchSpace (University of Auckland) · Cited by 377
A hybrid RNN-GPOD surrogate model for real-time settlement predictions in mechanised tunnelling
Cao, B. T., Freitag, S., & Meschke, G. (2016) · Advanced Modeling and Simulation in Engineering Sciences · doi:10.1186/s40323-016-0057-9 · Cited by 58
Model reduction and submodelling using neural networks
Koeppe, A., Bamer, F., & Markert, B. A. (2016) · PAMM · doi:10.1002/pamm.201610257 · Cited by 9
Self-citation
Cited by
A review on machine learning in 3D printing: applications, potential, and challenges
Goh, G. D., Sing, S. L., & Yeong, W. Y. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09876-9 · Cited by 652
Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019) · Engineering · doi:10.1016/j.eng.2019.04.012 · Cited by 529
Metallurgy, mechanistic models and machine learning in metal printing
DebRoy, T., Mukherjee, T., Wei, H. L., Elmer, J. W., & Milewski, J. O. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00236-1 · Cited by 497
Research and application of machine learning for additive manufacturing
Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., Simpson, T. W., Lu, Y., & Tang, Q. (2022) · Additive Manufacturing · doi:10.1016/j.addma.2022.102691 · Cited by 391
Machine Learning‐Driven Biomaterials Evolution
Suwardi, A., Wang, F., Xue, K., Han, M., Teo, P., Wang, P., Wang, S., Liu, Y., Ye, E., Li, Z., et al. (2022) · Advanced Materials · doi:10.1002/adma.202102703 · Cited by 299
Metadata
- Authors
- Koeppe, A., Hernandez Padilla, C. A., Voshage, M., Schleifenbaum, J. H., & Markert, B.
- Affiliations
- RWTH Aachen University (DE)
- Fraunhofer Institute for Laser Technology (DE)
- Corresponding author
- Koeppe, Arnd
- Venue
- Manufacturing Letters
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 15
- Pages
- 147-150
- ISSN
- 2213-8463 (ISSN-L)
- OpenAlex
- W2784327700
- Cited by
- 77
- References
- 7
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 13 · 2023: 7 · 2022: 14 · 2021: 13 · 2020: 9 · 2019: 5 · 2018: 1
- FWCI
- 4.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Manufacturing Letters (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Cellular and Composite Structures
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- 3D printing, additive manufacturing, artificial neural networks, stress prediction, finite element model reduction, lightweight structures, mechanical behavior
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.
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks. https://app.cochat.ai/papers/10.1016/j.mfglet.2018.01.002
- The paper
- Koeppe, A., Hernandez Padilla, C. A., Voshage, M., Schleifenbaum, J. H., & Markert, B. (2018). Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks. Manufacturing Letters. https://doi.org/10.1016/j.mfglet.2018.01.002
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.
