Survey on artificial intelligence for additive manufacturing
Abstract
Additive manufacturing of three-dimensional objects are now more and more realised through 3D printing, known as an evolutional paradigm in the manufacturing industry. Artificial intelligence is currently finding wide applications to 3D printing for an intelligent, efficient, high quality, mass customised and service-oriented production process. This paper presents a comprehensive survey of artificial intelligence in 3D printing. Before a printing task begins, the printability of given 3D objects can be determined through a printability checker using machine learning. The prefabrication of slicing is accelerated through parallel slicing algorithms and the path planning is optimised intelligently. In the aspect of service and security, intelligent demand matching and resource allocation algorithms enable a Cloud service platform and evaluation model to provide clients with an on-demand service and access to a collection of shared resources. We also present three machine learning algorithms to detect product defects in the presence of cyber-attacks. Based on the reviews on various applications, printability with multi-indicators, reduction of complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of good opportunities for further research, especially in the era of Industry 4.0.
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Existing factory processes will be increasingly subject to analysis to ascertain whether they could be automated.
“Existing factory processes will be increasingly subject to analysis to ascertain whether they could be automated (Lee, 2002; Löffler & Tschiesner, 2013; Yang et al., 2017).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Yang et al., 2017
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- Authors
- Yang, J., Chen, Y., Huang, W., & Li, Y.
- Affiliations
- University of Glasgow (GB)
- Dongguan University of Technology (CN)
- Northwestern Polytechnical University (CN)
- Venue
- 2017 23rd International Conference on Automation and Computing (ICAC)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 1-6
- OpenAlex
- W2766462730
- Cited by
- 73
- References
- 34
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 7 · 2023: 14 · 2022: 11 · 2021: 13 · 2020: 8 · 2019: 2 · 2018: 4
- FWCI
- 4.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automation and Computing (ICAC) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Engineering › Building and Construction › Innovations in Concrete and Construction Materials
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Keywords
- artificial intelligence, 3D printing, additive manufacturing, printability prediction, slicing algorithm, path planning, cloud platform, resource allocation, demand matching, defect detection, cyberattacks, real-time control, Industry 4.0, mass customization
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 ›
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