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A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys
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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 [438-445].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [444]
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Metadata
- Authors
- Muhammad, W., Brahme, A. P., Ibragimova, O., Kang, J., & Inal, K.
- Affiliations
- University of Waterloo (CA)
- Corresponding author
- Inal, Kaan
- Venue
- International Journal of Plasticity
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 136
- Pages
- 102867
- ISSN
- 0749-6419 (ISSN-L)
- OpenAlex
- W3089021334
- Cited by
- 149
- References
- 72
- Citations by year
- 2026: 20 · 2025: 28 · 2024: 26 · 2023: 28 · 2022: 34 · 2021: 13
- FWCI
- 9.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Plasticity (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Metals and Alloys › Microstructure and mechanical properties
- Keywords
- local strain distribution, plastic anisotropy, fracture prediction, AlSi10Mg, laser powder bed fusion, additive manufacturing, porosity, computed tomography, digital image correlation, artificial neural networks, tensile deformation, shear bands, crack initiation, microstructure-property relationship
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