Mechanical properties prediction of composite laminate with FEA and machine learning coupled method
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Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Regression (SVR) have shown remarkable efficacy in different applications.
“Among the various ML algorithms, Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Regression (SVR) have shown remarkable efficacy in different applications.”
From Machine Learning Algorithms for Prediction and Characterization of Cohesive Zone Parameters for Mixed-Mode Fracture (2024) · cites this paper as [51]
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Ramian, A., & Elhajjar, R. (2024) · Journal of Composites Science · doi:10.3390/jcs8080326 · Cited by 8 · see citing works
“Zhang et al. [51] established a method coupling FEA with ML to predict the mechanical properties of composite laminates, including failure factors under random stress states and critical buckling eigenvalues.”
- Authors
- Zhang, C., Li, Y., Jiang, B., Wang, R., Liu, Y., & Jia, L.
- Affiliations
- Aviation Industry Corporation of China (China) (CN)
- Xi'an Jiaotong University (CN)
- Corresponding author
- Liu, Yilun
- Jia, Liyong
- Venue
- Composite Structures
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 299
- Pages
- 116086
- ISSN
- 0263-8223 (ISSN-L)
- OpenAlex
- W4291156125
- Cited by
- 102
- References
- 34
- Citations by year
- 2026: 19 · 2025: 35 · 2024: 30 · 2023: 18
- FWCI
- 9.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Composite Structures (publishedVersion)
- Topics
- Physical Sciences › Engineering › Mechanics of Materials › Mechanical Behavior of Composites
- Physical Sciences › Engineering › Mechanics of Materials › Composite Structure Analysis and Optimization
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- composite laminates, mechanical property prediction, Puck failure criterion, artificial neural networks, random forest, finite element analysis, ABAQUS scripting
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