Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests
In this paper, computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously expl...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/3791543 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547551152177152 |
|---|---|
| author | Franck Nguyen Selim M. Barhli Daniel Pino Muñoz David Ryckelynck |
| author_facet | Franck Nguyen Selim M. Barhli Daniel Pino Muñoz David Ryckelynck |
| author_sort | Franck Nguyen |
| collection | DOAJ |
| description | In this paper, computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. The recognition of the suitable reduced order model is performed via a convolutional neural network (CNN) applied to a digital image of the mechanical test. The CNN recommend a convenient mechanical model available in a dictionary of reduced order models. The output of the convolutional neural network being a model, an error estimator, is proposed to assess the accuracy of this output. This article details simple algorithmic choices that allowed a realistic mechanical modeling via computer vision. |
| format | Article |
| id | doaj-art-152473761fe14d2794ccfc1409809d4b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-152473761fe14d2794ccfc1409809d4b2025-02-03T06:44:14ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/37915433791543Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical TestsFranck Nguyen0Selim M. Barhli1Daniel Pino Muñoz2David Ryckelynck3Centre des Matériaux, Mines ParisTech PSL Research University, Evry 91003, FranceSafran Analytics, rue des Jeunes Bois, Châteaufort, CS 80112, 78772 Magny les Hameaux Cedex, FranceCEMEF, Mines ParisTech PSL Research University, CS 10207, 06904 Sophia Antipolis Cedex, FranceCentre des Matériaux, Mines ParisTech PSL Research University, Evry 91003, FranceIn this paper, computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. The recognition of the suitable reduced order model is performed via a convolutional neural network (CNN) applied to a digital image of the mechanical test. The CNN recommend a convenient mechanical model available in a dictionary of reduced order models. The output of the convolutional neural network being a model, an error estimator, is proposed to assess the accuracy of this output. This article details simple algorithmic choices that allowed a realistic mechanical modeling via computer vision.http://dx.doi.org/10.1155/2018/3791543 |
| spellingShingle | Franck Nguyen Selim M. Barhli Daniel Pino Muñoz David Ryckelynck Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests Complexity |
| title | Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests |
| title_full | Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests |
| title_fullStr | Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests |
| title_full_unstemmed | Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests |
| title_short | Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests |
| title_sort | computer vision with error estimation for reduced order modeling of macroscopic mechanical tests |
| url | http://dx.doi.org/10.1155/2018/3791543 |
| work_keys_str_mv | AT francknguyen computervisionwitherrorestimationforreducedordermodelingofmacroscopicmechanicaltests AT selimmbarhli computervisionwitherrorestimationforreducedordermodelingofmacroscopicmechanicaltests AT danielpinomunoz computervisionwitherrorestimationforreducedordermodelingofmacroscopicmechanicaltests AT davidryckelynck computervisionwitherrorestimationforreducedordermodelingofmacroscopicmechanicaltests |