Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests

Abstract EN

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.