The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
Accelerating extrusion-based additive manufacturing optimization processes with surrogate-based multi-fidelity models
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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 [441]
Citations
Cites
Artificial neural networks: fundamentals, computing, design, and application
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Mohamed, O. A., Masood, S. H., & Bhowmik, J. L. (2015) · Advances in Manufacturing · doi:10.1007/s40436-014-0097-7 · Cited by 1,481
Space mapping technique for electromagnetic optimization
Bandler, J. W., Biernacki, R. M., Chen, S. H., Grobelny, P. A., & Hemmers, R. H. (1994) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/22.339794 · Cited by 697
Rayegani, F., & Onwubolu, G. C. (2014) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-014-5835-2 · Cited by 358
Material Property Testing of 3D-Printed Specimen in PLA on an Entry-Level 3D Printer
Letcher, T., & Waytashek, M. (2014) · Volume 2A: Advanced Manufacturing · doi:10.1115/imece2014-39379 · Cited by 325
Omar, O. M., MOHAMED, M., SyedH., Masood, ., & Bhowmik (2015) · Acta Scientiarum Naturalium Universitatis Sunyatseni · Cited by 302
Torres, J., Cole, M., Owji, A., DeMastry, Z., & Gordon, A. P. (2016) · Rapid Prototyping Journal · doi:10.1108/rpj-07-2014-0083 · Cited by 295
Mechanical Properties Optimization of Poly-Ether-Ether-Ketone via Fused Deposition Modeling
Deng, X., Zeng, Z., Peng, B., Yan, S., & Ke, W. (2018) · Materials · doi:10.3390/ma11020216 · Cited by 265
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Surrogate-Based Modeling and Optimization: Applications in Engineering
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Research works printed but not linked: 3
- Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, vol 5. Springer, New York, NY, USA
- Haykin SS (2009) Neural networks and learning machines/Simon Haykin. Prentice Hall, New York
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Cited by
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
A comprehensive review of lunar-based manufacturing and construction
Azami, M., Kazemi, Z., Moazen, S., Dubé, M., Potvin, M.-J., & Skonieczny, K. (2024) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2024.101045 · Cited by 57
Multi-Fidelity Methods for Optimization: A Survey
Li, K., & Li, F. (2026) · ACM Computing Surveys · doi:10.1145/3801959 · Cited by 52
Data-driven design strategy in fused filament fabrication: status and opportunities
Zhang, Y. J., & Moon, S. K. (2020) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwaa094 · Cited by 38
Surrogate modeling for injection molding processes using deep learning
Uglov, A., Nikolaev, S., Belov, S., Padalitsa, D. I., Greenkina, T., Biagio, M. S., & Cacciatori, F. M. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03380-0 · Cited by 18
Metadata
- Authors
- Zhou, X., Hsieh, S.-J., & Wang, J.-C.
- Affiliations
- Texas A&M University (US)
- National Taipei University of Technology (TW)
- Corresponding author
- Hsieh, Sheng-Jen
- Venue
- The International Journal of Advanced Manufacturing Technology
- Published
- 2019-05-17
- Volume
- 103
- Issue
- 9-12
- Pages
- 4071-4083
- ISSN
- 0268-3768 (ISSN-L)
- 1433-3015
- OpenAlex
- W2945442452
- Cited by
- 13
- References
- 31
- Citations by year
- 2026: 2 · 2024: 3 · 2022: 5 · 2021: 1 · 2020: 2
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- The International Journal of Advanced Manufacturing Technology (publishedVersion)
- Funders
- High Performance Research Computing, Texas A and M University
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Keywords
- fused deposition modeling, extrusion-based additive manufacturing, process parameter optimization, noise immunity, product quality
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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: Accelerating extrusion-based additive manufacturing optimization processes with surrogate-based multi-fidelity models. https://app.cochat.ai/papers/10.1007/s00170-019-03813-z
- The paper
- Zhou, X., Hsieh, S.-J., & Wang, J.-C. (2019). Accelerating extrusion-based additive manufacturing optimization processes with surrogate-based multi-fidelity models. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-019-03813-z
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.
