Research and application of machine learning for additive manufacturing
Open accessAbstract
Additive manufacturing (AM) is poised to bring a revolution due to its unique production paradigm. It offers the prospect of mass customization, flexible production, on-demand and decentralized manufacturing. However, a number of challenges stem from not only the complexity of manufacturing systems but the demand for increasingly complex and high-quality products, in terms of design principles, standardization and quality control. These challenges build up barriers to the widespread adoption of AM in the industry and the in-depth research of AM in academia. To tackle the challenges, machine learning (ML) technologies rise to play a critical role as they are able to provide effective ways to quality control, process optimization, modelling of complex systems, and energy management. Hence, this paper employs a systematic literature review method as it is a defined and methodical way of identifying, assessing, and analysing published literature. Then, a keyword co-occurrence and cluster analysis are employed for analysing relevant literature. Several aspects of AM, including Design for AM (DfAM), material analytics, in situ monitoring and defect detection, property prediction and sustainability, have been clustered and summarized to present state-of-the-art research in the scope of ML for AM. Finally, the challenges and opportunities of ML for AM are uncovered and discussed.
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
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.
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?
The integration of machine learning in manufacturing has been well-established in recent years, aiming to enhance efficiency and precision in the manufacturing process.
“The integration of machine learning in manufacturing has been well-established in recent years, aiming to enhance efficiency and precision in the manufacturing process [415-419].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [416]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · cited 1× · see citing works
“Since ML technologies have been increasingly adopted for modelling and decision making in several crucial scenarios (e.g. medical use, defence, and precision machining), people have to make a trade-off between transparency and accuracy [217].”
Additive manufacturing (3D printing): A review of materials, methods, applications and challenges
Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T. Q., & Hui, D. (2018) · Composites Part B: Engineering · doi:10.1016/j.compositesb.2018.02.012 · Cited by 8,431 · see citing works
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 1× · see citing works
“With the advancement of data acquisition and storage technologies, data-driven approaches based on ML technologies have been increasingly adopted to discover hidden knowledge and build highly complex relationships in digital manufacturing systems [4].”
Wohlin, C. (2014) · Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering · doi:10.1145/2601248.2601268 · Cited by 4,234 · cited 1× · see citing works
“A manual search process is adopted to search for additional sources related to the review topic [27,28], where the exclusion and inclusion criteria were re-applied.”
From machine learning to deep learning: progress in machine intelligence for rational drug discovery
Zhang, L., Tan, J., Han, D., & Zhu, H. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.08.010 · Cited by 760 · see citing works
Herriott, C., & Spear, A. D. (2020) · Computational Materials Science · doi:10.1016/j.commatsci.2020.109599 · Cited by 187 · see citing works
Zhan, Z., & Li, H. (2021) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2020.106089 · Cited by 176 · see citing works
Muhammad, W., Brahme, A. P., Ibragimova, O., Kang, J., & Inal, K. (2021) · International Journal of Plasticity · doi:10.1016/j.ijplas.2020.102867 · Cited by 149 · see citing works
In-plane shape-deviation modeling and compensation for fused deposition modeling processes
Wang, A., Song, S., Huang, Q., & Tsung, F. (2017) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2544941 · Cited by 79 · see citing works
Survey on artificial intelligence for additive manufacturing
Yang, J., Chen, Y., Huang, W., & Li, Y. (2017) · 2017 23rd International Conference on Automation and Computing (ICAC) · doi:10.23919/iconac.2017.8082053 · Cited by 73 · see citing works
A statistical method for build orientation determination in additive manufacturing
Zhang, Y., Harik, R., Fadel, G., & Bernard, A. (2019) · Rapid Prototyping Journal · doi:10.1108/rpj-04-2018-0102 · Cited by 63 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
- Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., Simpson, T. W., Lu, Y., & Tang, Q.
- Affiliations
- Cardiff University (GB)
- National Institute of Standards and Technology (US)
- University of Manchester (GB)
- Georgia Institute of Technology (US)
- Pennsylvania State University (US)
- Chongqing University (CN)
- Corresponding author
- Liu, Ying
- Venue
- Additive Manufacturing
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 52
- Pages
- 102691
- ISSN
- 2214-7810 (ISSN-L)
- 2214-8604
- OpenAlex
- W4213262259
- Cited by
- 391
- References
- 12
- Citations by year
- 2026: 52 · 2025: 108 · 2024: 116 · 2023: 96 · 2022: 19
- FWCI
- 23.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Additive manufacturing (publishedVersion, cc-by)
- Research Explorer (The University of Manchester) (publishedVersion, other-oa)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- CERES (Cranfield University) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- machine learning applications, design for additive manufacturing, in situ monitoring, defect detection, property prediction, sustainability, process optimization, quality control, energy management, keyword co-occurrence, research trends
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
