Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests. (2nd December 2018)

• Record Type: Journal Article
• Authors: Nguyen, Franck; Barhli, Selim M.; Muñoz, Daniel Pino; Ryckelynck, David
• Other Names: Montáns Francisco J. Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-12-02
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/3791543
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.