Cargando…

A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, a...

Descripción completa

Detalles Bibliográficos
Autores principales: Vurtur Badarinath, Poojitha, Chierichetti, Maria, Davoudi Kakhki, Fatemeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7957535/
https://www.ncbi.nlm.nih.gov/pubmed/33673605
http://dx.doi.org/10.3390/s21051654
_version_ 1783664671037849600
author Vurtur Badarinath, Poojitha
Chierichetti, Maria
Davoudi Kakhki, Fatemeh
author_facet Vurtur Badarinath, Poojitha
Chierichetti, Maria
Davoudi Kakhki, Fatemeh
author_sort Vurtur Badarinath, Poojitha
collection PubMed
description Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.
format Online
Article
Text
id pubmed-7957535
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-79575352021-03-16 A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems Vurtur Badarinath, Poojitha Chierichetti, Maria Davoudi Kakhki, Fatemeh Sensors (Basel) Article Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results. MDPI 2021-02-27 /pmc/articles/PMC7957535/ /pubmed/33673605 http://dx.doi.org/10.3390/s21051654 Text en © 2021 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vurtur Badarinath, Poojitha
Chierichetti, Maria
Davoudi Kakhki, Fatemeh
A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems
title A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems
title_full A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems
title_fullStr A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems
title_full_unstemmed A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems
title_short A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems
title_sort machine learning approach as a surrogate for a finite element analysis: status of research and application to one dimensional systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7957535/
https://www.ncbi.nlm.nih.gov/pubmed/33673605
http://dx.doi.org/10.3390/s21051654
work_keys_str_mv AT vurturbadarinathpoojitha amachinelearningapproachasasurrogateforafiniteelementanalysisstatusofresearchandapplicationtoonedimensionalsystems
AT chierichettimaria amachinelearningapproachasasurrogateforafiniteelementanalysisstatusofresearchandapplicationtoonedimensionalsystems
AT davoudikakhkifatemeh amachinelearningapproachasasurrogateforafiniteelementanalysisstatusofresearchandapplicationtoonedimensionalsystems
AT vurturbadarinathpoojitha machinelearningapproachasasurrogateforafiniteelementanalysisstatusofresearchandapplicationtoonedimensionalsystems
AT chierichettimaria machinelearningapproachasasurrogateforafiniteelementanalysisstatusofresearchandapplicationtoonedimensionalsystems
AT davoudikakhkifatemeh machinelearningapproachasasurrogateforafiniteelementanalysisstatusofresearchandapplicationtoonedimensionalsystems