Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework

Cellular materials have been widely used in load carrying lightweight structures. Although lightweight increases natural frequency, low stiffness of cellular structures reduces natural frequency. Designing structures with higher natural frequency can usually avoid resonance. In addition, because of...

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Autores principales:	Adithya Challapalli, John Konlan, Dhrumil Patel, Guoqiang Li
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	cellular structures optimization machine learning Gans inverse design natural frequency Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/773967205b504a5499273fa0bcfa9583
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spelling	oai:doaj.org-article:773967205b504a5499273fa0bcfa95832021-12-01T18:49:14ZDiscovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework2297-307910.3389/fmech.2021.779098https://doaj.org/article/773967205b504a5499273fa0bcfa95832021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmech.2021.779098/fullhttps://doaj.org/toc/2297-3079Cellular materials have been widely used in load carrying lightweight structures. Although lightweight increases natural frequency, low stiffness of cellular structures reduces natural frequency. Designing structures with higher natural frequency can usually avoid resonance. In addition, because of the less amount of materials used in cellular structures, the energy absorption capability usually decreases such as under impact loading. Therefore, designing cellular structures with higher natural frequency and higher energy absorption capability is highly desired. In this study, machine learning and novel inverse design techniques enable to search a huge space of unexplored structural designs. In this study, machine learning regression and Generative Neural Networks (GANs) were used to form an inverse design framework. Optimal cellular unit cells that surpass the performance of biomimetic structures inspired from honeycomb, plant stems and trabecular bone in terms of natural frequency and impact resistance were discovered using machine learning. The discovered optimal cellular unit cells exhibited 30–100% higher natural frequency and 300% higher energy absorption than those of the biomimetic counterparts. The discovered optimal unit cells were validated through experimental and simulation comparisons. The machine learning framework in this study would help in designing load carrying engineering structures with increased natural frequency and enhanced energy absorption capability.Adithya ChallapalliJohn KonlanDhrumil PatelGuoqiang LiFrontiers Media S.A.articlecellular structuresoptimizationmachine learningGansinverse designnatural frequencyMechanical engineering and machineryTJ1-1570ENFrontiers in Mechanical Engineering, Vol 7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cellular structures optimization machine learning Gans inverse design natural frequency Mechanical engineering and machinery TJ1-1570
spellingShingle	cellular structures optimization machine learning Gans inverse design natural frequency Mechanical engineering and machinery TJ1-1570 Adithya Challapalli John Konlan Dhrumil Patel Guoqiang Li Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework
description	Cellular materials have been widely used in load carrying lightweight structures. Although lightweight increases natural frequency, low stiffness of cellular structures reduces natural frequency. Designing structures with higher natural frequency can usually avoid resonance. In addition, because of the less amount of materials used in cellular structures, the energy absorption capability usually decreases such as under impact loading. Therefore, designing cellular structures with higher natural frequency and higher energy absorption capability is highly desired. In this study, machine learning and novel inverse design techniques enable to search a huge space of unexplored structural designs. In this study, machine learning regression and Generative Neural Networks (GANs) were used to form an inverse design framework. Optimal cellular unit cells that surpass the performance of biomimetic structures inspired from honeycomb, plant stems and trabecular bone in terms of natural frequency and impact resistance were discovered using machine learning. The discovered optimal cellular unit cells exhibited 30–100% higher natural frequency and 300% higher energy absorption than those of the biomimetic counterparts. The discovered optimal unit cells were validated through experimental and simulation comparisons. The machine learning framework in this study would help in designing load carrying engineering structures with increased natural frequency and enhanced energy absorption capability.
format	article
author	Adithya Challapalli John Konlan Dhrumil Patel Guoqiang Li
author_facet	Adithya Challapalli John Konlan Dhrumil Patel Guoqiang Li
author_sort	Adithya Challapalli
title	Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework
title_short	Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework
title_full	Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework
title_fullStr	Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework
title_full_unstemmed	Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework
title_sort	discovery of cellular unit cells with high natural frequency and energy absorption capabilities by an inverse machine learning framework
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/773967205b504a5499273fa0bcfa9583
work_keys_str_mv	AT adithyachallapalli discoveryofcellularunitcellswithhighnaturalfrequencyandenergyabsorptioncapabilitiesbyaninversemachinelearningframework AT johnkonlan discoveryofcellularunitcellswithhighnaturalfrequencyandenergyabsorptioncapabilitiesbyaninversemachinelearningframework AT dhrumilpatel discoveryofcellularunitcellswithhighnaturalfrequencyandenergyabsorptioncapabilitiesbyaninversemachinelearningframework AT guoqiangli discoveryofcellularunitcellswithhighnaturalfrequencyandenergyabsorptioncapabilitiesbyaninversemachinelearningframework
_version_	1718404699609104384

Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework

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