1. Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests
- Author
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Franck N'Guyen, Daniel Pino Muñoz, David Ryckelynck, S.M. Barhli, Centre de Mise en Forme des Matériaux (CEMEF), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), and Centre des Matériaux (MAT)
- Subjects
Multidisciplinary ,Article Subject ,General Computer Science ,Exploit ,business.industry ,Estimator ,02 engineering and technology ,Hybrid approach ,01 natural sciences ,Convolutional neural network ,lcsh:QA75.5-76.95 ,Reduced order ,010101 applied mathematics ,Digital image ,020303 mechanical engineering & transports ,0203 mechanical engineering ,Simple (abstract algebra) ,[PHYS.COND.CM-MS]Physics [physics]/Condensed Matter [cond-mat]/Materials Science [cond-mat.mtrl-sci] ,Computer vision ,lcsh:Electronic computers. Computer science ,Artificial intelligence ,0101 mathematics ,business ,Cluster analysis - Abstract
International audience; 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.
- Published
- 2018
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