Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity

Abstract Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and...

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Autores principales:	Adithya Challapalli, Guoqiang Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/97b1dee0cda7483388129d318b2344c5
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spelling	oai:doaj.org-article:97b1dee0cda7483388129d318b2344c52021-12-02T15:15:45ZMachine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity10.1038/s41598-021-98015-72045-2322https://doaj.org/article/97b1dee0cda7483388129d318b2344c52021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98015-7https://doaj.org/toc/2045-2322Abstract Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51–57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130–160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13–35% higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.Adithya ChallapalliGuoqiang LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Adithya Challapalli Guoqiang Li Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity
description	Abstract Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51–57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130–160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13–35% higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.
format	article
author	Adithya Challapalli Guoqiang Li
author_facet	Adithya Challapalli Guoqiang Li
author_sort	Adithya Challapalli
title	Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity
title_short	Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity
title_full	Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity
title_fullStr	Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity
title_full_unstemmed	Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity
title_sort	machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/97b1dee0cda7483388129d318b2344c5
work_keys_str_mv	AT adithyachallapalli machinelearningassisteddesignofnewlatticecoreforsandwichstructureswithsuperiorloadcarryingcapacity AT guoqiangli machinelearningassisteddesignofnewlatticecoreforsandwichstructureswithsuperiorloadcarryingcapacity
_version_	1718387522927591424

Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity

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