Multiscale Analysis of Composite Structures with Artificial Neural Network Support for Micromodel Stress Determination
Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress ma...
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| Published in: | Materials Vol. 17; no. 1; p. 154 |
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| Abstract | Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress maps can be obtained. However, such stress results do not describe the actual behavior of the material and are often significantly different from the actual stresses in the heterogeneous microstructure. Finding high-accuracy stress results for such materials leads to time-consuming analyses in both scales. This paper focuses on the application of machine learning to multiscale analysis of structures made of composite materials, to substantially decrease the time of computations of such localization problems. The presented methodology was validated by a numerical example where a structure made of resin epoxy with randomly distributed short glass fibers was analyzed using a computational multiscale approach. Carefully prepared training data allowed artificial neural networks to learn relationships between two scales and significantly increased the efficiency of the multiscale approach. |
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| AbstractList | Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress maps can be obtained. However, such stress results do not describe the actual behavior of the material and are often significantly different from the actual stresses in the heterogeneous microstructure. Finding high-accuracy stress results for such materials leads to time-consuming analyses in both scales. This paper focuses on the application of machine learning to multiscale analysis of structures made of composite materials, to substantially decrease the time of computations of such localization problems. The presented methodology was validated by a numerical example where a structure made of resin epoxy with randomly distributed short glass fibers was analyzed using a computational multiscale approach. Carefully prepared training data allowed artificial neural networks to learn relationships between two scales and significantly increased the efficiency of the multiscale approach. |
| Audience | Academic |
| Author | Kuś, Wacław Jiregna, Iyasu Tafese Mucha, Waldemar |
| AuthorAffiliation | Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; waclaw.kus@polsl.pl (W.K.); iyasu.tafese.jiregna@polsl.pl (I.T.J.) |
| AuthorAffiliation_xml | – name: Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland; waclaw.kus@polsl.pl (W.K.); iyasu.tafese.jiregna@polsl.pl (I.T.J.) |
| Author_xml | – sequence: 1 givenname: Wacław orcidid: 0000-0001-7616-6881 surname: Kuś fullname: Kuś, Wacław organization: Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland – sequence: 2 givenname: Waldemar orcidid: 0000-0002-9724-1817 surname: Mucha fullname: Mucha, Waldemar organization: Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland – sequence: 3 givenname: Iyasu Tafese orcidid: 0000-0001-6610-8696 surname: Jiregna fullname: Jiregna, Iyasu Tafese organization: Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38204008$$D View this record in MEDLINE/PubMed |
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| Keywords | artificial neural network homogenization fiber-reinforced composite finite element method composite material multiscale modeling machine learning |
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| SubjectTerms | Artificial neural networks Boundary conditions Boundary value problems Composite materials Composite structures Efficiency Epoxy resins Finite element analysis Finite element method Fourier transforms Glass fiber reinforced plastics Glass-epoxy composites Homogenization Load Localization Machine learning Material properties Methods Microstructure Multiscale analysis Neural networks Numerical analysis Simulation |
| Title | Multiscale Analysis of Composite Structures with Artificial Neural Network Support for Micromodel Stress Determination |
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