[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123823-en":3,"doc-seo-123823-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123823,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Vibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning - Article","A machine learning framework predicts vibration suppression in graphene/fibre-reinforced laminate cantilever beams equipped with shunted piezoelectric systems. Parametric finite element simulations generate datasets capturing vibration response and vibration attenuation, with inputs including graphene and fibre reinforcement contents and fibre angles, and outputs representing the suppression level achieved by the piezoelectric shunt. Artificial Neural Networks are trained and validated using the simulated datasets. The methodology enables fast and accurate estimation of vibration response for nanocomposite laminates.","Central Lancashire Online Knowledge (CLoK)  \n\n| Title | Vibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning |\n| --- | --- |\n| Type | Article |\n| URL | [https://clok.uclan.ac.uk/51614/](https://clok.uclan.ac.uk/51614/) |\n| DOI | \\#\\#doi\\#\\# |\n| Date | 2024 |\n| Citation | Drosopoulos, Georgios orcid iconORCID: 0000-0002-4252-6321, Foutsitzi, Georgia, Daraki, Maria-Styliani and Stavroulakis, Georgios E. (2024) Vibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning. Signals, 5 (2) . pp. 326-342. |\n| Creators | Drosopoulos, Georgios, Foutsitzi, Georgia, Daraki, Maria-Styliani and Stavroulakis, Georgios E. |\n\nIt is advisable to refer to the publisher’s version if you intend to cite from the work. \\#\\#doi\\#\\# For information about Research at UCLan please go to [http://www.uclan.ac. uk/research/](http://www.uclan.ac. uk/research/)  \n[All outputs in CLoK are protected by Intellectual Property Rights law](All outputs in CLoK are protected by Intellectual Property Rights law), including Copyright law. Copyright, IPR and Moral Rights for the works on this site are retained by the individual authors and/or other copyright owners. Terms and conditions for use of this material are defined in the  \n[http://clok.uclan.ac.uk/policies/](http://clok.uclan.ac.uk/policies/)  \n signals  \nArticle  \nVibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning  \nGeorgios Drosopoulos 1,2,*, Georgia Foutsitzi 3, Maria-Styliani Daraki 4 and Georgios E. Stavroulakis 4  \nCitation: Drosopoulos, G.; Foutsitzi, G.; Daraki, M.-S.; Stavroulakis, G.E. Vibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning. Signals 2024, 5, 326–342 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)signals5020017  \nAcademic Editor: Zhong Liu  \nReceived: 5 April 2024  \nRevised: 7 May 2024  \nAccepted: 18 May 2024  \nPublished: 23 May 2024  \nCopyright: © 2024 by the authors. 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Discipline of Civil Engineering, University of Central Lancashire, Preston PR1 2HE, UK  \n2 Discipline of Civil Engineering, University of KwaZulu-Natal, Durban 4041, South Africa  \n3 Department of Informatics and Telecommunications, University of Ioannina, GR-47100 Arta, Greece; [gfoutsi@uoi.gr](gfoutsi@uoi.gr)  \n4 School of Production Engineering and Management, Technical University of Crete, GR-73100 Chania, Greece; [mdaraki1@tuc.gr](mdaraki1@tuc.gr) (M.-S.D.); [gestavroulakis@tuc.gr](gestavroulakis@tuc.gr) (G.E.S.)  \n* Correspondence: [gdrosopoulos@uclan.ac.uk](gdrosopoulos@uclan.ac.uk)  \nAbstract: The implementation of a machine learning approach to predict vibration suppression, as derived from nanocomposite laminates with piezoelectric shunted systems, is studied in this article. Datasets providing the vibration response and vibration attenuation are developed using parametric finite element simulations. A graphene/fibre-reinforced laminate cantilever beam is used in those simulations. Parameters, including the graphene and fibre reinforcements content, as well as the fibre angles, are among the inputs. Output is the vibration suppression achieved by the piezoelectric shunted system. Artificial Neural Networks are trained and tested using the derived datasets. The proposed methodology can be used for a fast and accurate prediction of the vibration response of nanocomposite laminates.  \nKeywords: artificial neural network; graphene nanoplatelets (GPLs); laminated nanocomposites; piezoelectric shunt circuit; single-mode damping; free v","cbCaijEyJsY8fhEf","https://ap.wps.com/l/cbCaijEyJsY8fhEf","pdf",1185921,1,18,"English","en",105,"# Introduction\n## Background on nanocomposites with graphene nanoplatelets\n## Vibration control using piezoelectric materials","[{\"question\":\"What problem does the article address?\",\"answer\":\"It addresses predicting vibration suppression of graphene-reinforced laminated beams using shunted piezoelectric systems with a machine learning approach.\"},{\"question\":\"How are the datasets for machine learning generated?\",\"answer\":\"Datasets are developed using parametric finite element simulations that provide vibration response and vibration attenuation for the cantilever beam models.\"},{\"question\":\"Which inputs and outputs are used in the neural network model?\",\"answer\":\"Inputs include graphene and fibre reinforcement contents and fibre angles, while the output is the vibration suppression achieved by the piezoelectric shunted system.\"}]","Vibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning - 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