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Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models
Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorpora...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371008/ https://www.ncbi.nlm.nih.gov/pubmed/35956742 http://dx.doi.org/10.3390/polym14153229 |
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author | Liao, Zhenhao Qiu, Cheng Yang, Jun Yang, Jinglei Yang, Lei |
author_facet | Liao, Zhenhao Qiu, Cheng Yang, Jun Yang, Jinglei Yang, Lei |
author_sort | Liao, Zhenhao |
collection | PubMed |
description | Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems. |
format | Online Article Text |
id | pubmed-9371008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93710082022-08-12 Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models Liao, Zhenhao Qiu, Cheng Yang, Jun Yang, Jinglei Yang, Lei Polymers (Basel) Article Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems. MDPI 2022-08-08 /pmc/articles/PMC9371008/ /pubmed/35956742 http://dx.doi.org/10.3390/polym14153229 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liao, Zhenhao Qiu, Cheng Yang, Jun Yang, Jinglei Yang, Lei Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models |
title | Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models |
title_full | Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models |
title_fullStr | Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models |
title_full_unstemmed | Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models |
title_short | Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models |
title_sort | accelerating the layup sequences design of composite laminates via theory-guided machine learning models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371008/ https://www.ncbi.nlm.nih.gov/pubmed/35956742 http://dx.doi.org/10.3390/polym14153229 |
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