Laser scan strategy descriptor for defect prognosis in metal additive manufacturing using neural networks
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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.
“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 [443]
Cites
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 · see citing works
Additive manufacturing of metals
Herzog, D., Seyda, V., Wycisk, E., & Emmelmann, C. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.07.019 · Cited by 4,785 · see citing works
Khairallah, S. A., Anderson, A. T., Rubenchik, A. M., & King, W. E. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.02.014 · Cited by 2,797 · see citing works
Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book
(2012) · Lecture Notes in Computational Science and Engineering · doi:10.1007/978-3-642-23099-8 · Cited by 2,204 · see citing works
Additive manufacturing: scientific and technological challenges, market uptake and opportunities
Tofail, S. A. M., Koumoulos, E. P., Bandyopadhyay, A. K., Bose, S., O’Donoghue, L., & Charitidis, C. A. (2017) · Materials Today · doi:10.1016/j.mattod.2017.07.001 · Cited by 2,166 · see citing works
Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems
Alcácer, V., & Machado, V. A. C. (2019) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2019.01.006 · Cited by 1,110 · see citing works
The Role of Additive Manufacturing in the Era of Industry 4.0
Dilberoglu, U. M., Gharehpapagh, B., Yaman, U., & Dölen, M. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.148 · Cited by 1,006 · see citing works
Accelerating materials property predictions using machine learning
Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., & Ramprasad, R. (2013) · Scientific Reports · doi:10.1038/srep02810 · Cited by 851 · see citing works
3D printing in aerospace and its long-term sustainability
Joshi, S. C., & Sheikh, A. A. (2015) · Virtual and Physical Prototyping · doi:10.1080/17452759.2015.1111519 · Cited by 628 · see citing works
Metal additive manufacturing in the commercial aviation industry: A review
Gisario, A., Kazarian, M., Martina, F., & Mehrpouya, M. (2019) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.08.005 · Cited by 575 · see citing works
Fluid and particle dynamics in laser powder bed fusion
Bidare, P., Bitharas, I., Ward, R., Attallah, M. M., & Moore, A. J. (2017) · Acta Materialia · doi:10.1016/j.actamat.2017.09.051 · Cited by 557 · see citing works
Prediction of composite microstructure stress-strain curves using convolutional neural networks
Yang, C., Kim, Y., Ryu, S., & Gu, G. X. (2020) · Materials & Design · doi:10.1016/j.matdes.2020.108509 · Cited by 456 · see citing works
Self-citationHierarchically Enhanced Impact Resistance of Bioinspired Composites
Gu, G. X., Takaffoli, M., & Buehler, M. J. (2017) · Advanced Materials · doi:10.1002/adma.201700060 · Cited by 400 · see citing works
Self-citationDevelopments in 4D-printing: a review on current smart materials, technologies, and applications
Zhang, Z., Demir, K. G., & Gu, G. X. (2019) · International Journal of Smart and Nano Materials · doi:10.1080/19475411.2019.1591541 · Cited by 393 · see citing works
Self-citationMetal 3D printing as a disruptive technology for superalloys
Panwisawas, C., Tang, Y. T., & Reed, R. C. (2020) · Nature Communications · doi:10.1038/s41467-020-16188-7 · Cited by 360 · see citing works
Chen, C., & Gu, G. X. (2020) · Advanced Science · doi:10.1002/advs.201902607 · Cited by 332 · see citing works
Self-citationBegg, R. K., & Kamruzzaman, J. (2004) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2004.05.002 · Cited by 326 · see citing works
Machine Learning for Advanced Additive Manufacturing
Jin, Z., Zhang, Z., Demir, K. G., & Gu, G. X. (2020) · Matter · doi:10.1016/j.matt.2020.08.023 · Cited by 275 · see citing works
Self-citationRashid, R. A. R., Masood, S. H., Ruan, D., et al. (2017) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2017.06.023 · Cited by 242 · see citing works
Machine learning for the structure–energy–property landscapes of molecular crystals
Musil, F., De, S., Yang, J., Campbell, J. E., Day, G. M., & Ceriotti, M. (2018) · Chemical Science · doi:10.1039/c7sc04665k · Cited by 216 · see citing works
Levkulich, N. C., Semiatin, S. L., Gockel, J., et al. (2019) · Additive manufacturing · doi:10.1016/j.addma.2019.05.015 · Cited by 211 · see citing works
On thermal modeling of Additive Manufacturing processes
Foteinopoulos, P., Papacharalampopoulos, A., & Stavropoulos, P. G. (2017) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2017.09.007 · Cited by 209 · see citing works
Arısoy, Y. M., Criales, L. E., Özel, T., et al. (2016) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-016-9429-z · Cited by 198 · see citing works
Solidification and solid-state transformation sciences in metals additive manufacturing
Kirka, M. M., Nandwana, P., Lee, Y., et al. (2017) · Scripta Materialia · doi:10.1016/j.scriptamat.2017.01.005 · Cited by 140 · see citing works
A review on additive manufacturing and its way into the oil and gas industry
Sireesha, M., Lee, J., Kiran, A. S., Babu, V. J., Kee, B. B. T., & Ramakrishna, S. (2018) · RSC Advances · doi:10.1039/c8ra03194k · Cited by 133 · see citing works
Gouge, M., Denlinger, E. R., Irwin, J., et al. (2019) · Additive manufacturing · doi:10.1016/j.addma.2019.06.022 · Cited by 122 · see citing works
Monte Carlo studies of polymer chain dimensions in the melt
Mansfield, M. L. (1982) · The Journal of Chemical Physics · doi:10.1063/1.443937 · Cited by 93 · see citing works
Lee, Y., Kirka, M. M., Ferguson, J., et al. (2020) · Additive manufacturing · doi:10.1016/j.addma.2019.101031 · Cited by 80 · see citing works
The Finite Element Method for the Thermo-Mechanical Modeling of Additive Manufacturing Processes
Gouge, M., Michaleris, P., Denlinger, E. R., et al. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-811820-7.00003-3 · Cited by 73 · see citing works
Jin, Z., Zhang, Z., Ott, J., & Gu, G. X. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101696 · Cited by 70 · see citing works
Self-citationCarraturo, M., Jomo, J., Kollmannsberger, S., et al. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101498 · Cited by 59 · see citing works
Modeling and Simulation of Laser Processing of Particulate-Functionalized Materials
Zohdi, T. I. (2015) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-015-9160-1 · Cited by 30 · see citing works
Zohdi, T. I. (2018) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2018.08.040 · Cited by 7 · see citing works
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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 · see citing works
- Authors
- Demir, K., Zhang, Z., Ben-Artzy, A., Hosemann, P., & Gu, G. X.
- Affiliations
- University of California, Berkeley (US)
- Ben-Gurion University of the Negev (IL)
- Corresponding author
- Gu, Grace X.
- Venue
- Journal of Manufacturing Processes
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 67
- Pages
- 628-634
- ISSN
- 1526-6125 (ISSN-L)
- OpenAlex
- W3160488492
- Cited by
- 15
- References
- 33
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 2 · 2022: 2 · 2021: 1
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Manufacturing Processes (acceptedVersion)
- Funders
- University of California Berkeley
- National Science Foundation
- National Science Foundation Directorate for Engineering (ACI-1548562)
- 3M
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Engineering › Mechanical Engineering › Welding Techniques and Residual Stresses
- Keywords
- metal additive manufacturing, powder bed fusion, residual stress prediction, microstructural properties, neural network prediction, in situ monitoring
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