Modélisation et validation expérimentale d'un modèle vibroacoustique d'un silencieux d'une motoneige

Ce mémoire traite la modélisation et la validation expérimentale du bruit d’un silencieux de motoneige. La première phase du projet consiste à modéliser numériquement le système d’échappement avec les méthodes numériques suivantes : éléments finis et éléments finis de frontière, afin d’évaluer ses performances acoustiques : perte par transmission, bruit de bouche et bruit de paroi. Une deuxième phase du projet consiste à valider expérimentalement les performances acoustiques calculées numériquement. La dernière phase du projet se consacrera à une étude paramétrique expérimentale d’un silencieux sur banc moteur. En conclusion, les résultats des modèles numériques mis en œuvre concordent bien avec les résultats expérimentaux. Cependant, les aspects non linéaires rencontrés à la dernière phase du projet n’ont pas été étudiés davantage

CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render.Comment: Accepted at the Workshop on Photorealistic Image and Environment Synthesis for Computer Vision (PIES-CV) at WACV2

Modélisation et validation expérimentale d'un modèle vibroacoustique d'un silencieux d'une motoneige

Ce mémoire traite la modélisation et la validation expérimentale du bruit d’un silencieux de motoneige. La première phase du projet consiste à modéliser numériquement le système d’échappement avec les méthodes numériques suivantes : éléments finis et éléments finis de frontière, afin d’évaluer ses performances acoustiques : perte par transmission, bruit de bouche et bruit de paroi. Une deuxième phase du projet consiste à valider expérimentalement les performances acoustiques calculées numériquement. La dernière phase du projet se consacrera à une étude paramétrique expérimentale d’un silencieux sur banc moteur. En conclusion, les résultats des modèles numériques mis en œuvre concordent bien avec les résultats expérimentaux. Cependant, les aspects non linéaires rencontrés à la dernière phase du projet n’ont pas été étudiés davantage

CenDerNet : center and curvature representations for render-and-compare 6D pose estimation

We introduce CenDerNet, a framework for 6D pose estimation from multi-view images based on center and curvature representations. Finding precise poses for reflective, textureless objects is a key challenge for industrial robotics. Our approach consists of three stages: First, a fully convolutional neural network predicts center and curvature heatmaps for each view; Second, center heatmaps are used to detect object instances and find their 3D centers; Third, 6D object poses are estimated using 3D centers and curvature heatmaps. By jointly optimizing poses across views using a render-and-compare approach, our method naturally handles occlusions and object symmetries. We show that CenDerNet outperforms previous methods on two industry-relevant datasets: DIMO and T-LESS

Inverse characterization method of viscoelastic materials using dispersion analysis

International audienceThis paper presents a dispersion-based method for identifying visco-elastic material properties by minimizing the residue between data of virtual experiments and data based on a viscoelastic model through the use of surrogate modeling. The dispersion data retrieved from a virtual experiment of a finite beam with constrained layer damping (the real and imaginary part of the wavenumber) is fitted with numerical dispersion data through an optimization scheme, which can be computationally expensive. In order to alleviate this issue, attention has been focused on the construction of a surrogate model that makes the optimization schemecheaper without loosing much accuracy in the prediction. This paper uses an interpolation method based on radial basis functions. Once the surrogate model is constructed, the viscoelastic parameters can then be identified and results are compared to the reference parameters