Reverse engineering of additive manufactured composite part by toolpath reconstruction using imaging and machine learning
Open accessAbstract
Abstract Development of composite material parts requires significant research and development effort. The fiber size, volume fraction and direction are important in determining the properties of the part. Additive manufacturing (AM) methods are increasingly used for printing composite materials. Advancements in 3D scanning and imaging technology have raised a significant concern in reverse engineering of parts made by AM, which may result in counterfeiting and unauthorized production of high quality parts. This work is focused on using imaging methods and machine learning to reverse engineer a composite material part, where not only the geometry is captured but also the tool path of 3D printing is reconstructed using machine learning of microstructure. A dimensional accuracy with only 0.33% difference is achieved for the reverse engineered model.
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For printing composite materials, factors such as fiber size, volume fraction, and direction must be optimized to secure the desired properties for the printed part.
“For printing composite materials, factors such as fiber size, volume fraction, and direction must be optimized to secure the desired properties for the printed part [427].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [427]
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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
- Yanamandra, K., Chen, G. L., Xu, X., Mac, G., & Gupta, N.
- Affiliations
- New York University (US)
- Venue
- Composites Science and Technology
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 198
- Pages
- 108318
- ISSN
- 0266-3538 (ISSN-L)
- OpenAlex
- W3037035195
- Semantic Scholar
- ec2500477e1107f10e94790b51ac6d6783efa8fb
- Cited by
- 72
- References
- 43
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 11 · 2023: 16 · 2022: 13 · 2021: 9 · 2020: 3
- FWCI
- 3.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Composites Science and Technology (acceptedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
- Keywords
- reverse engineering, additive manufacturing, composite components, 3D printing, 3D scanning, imaging, machine learning, microstructure, fiber orientation, counterfeit parts, dimensional accuracy
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