Fabrication and mechanical testing of a new sandwich structure with carbon fiber network core

The aim is the fabrication and mechanical testing of sandwich structures including a new core material known as fiber network sandwich materials. As fabrication norms for such a material do not exist as such, so the primary goal is to reproduce successfully fiber network sandwich specimens. Enhanced vibration testing diagnoses the quality of the fabrication process. These sandwich materials possess low structural strength as proved by the static tests (compression, bending), but the vibration test results give high damping values, making the material suitable for vibro-acoustic applications where structural strength is of secondary importance e.g., internal panelling of a helicopter

From Architectured Materials to Large-Scale Additive Manufacturing

The classical material-by-design approach has been extensively perfected by materials scientists, while engineers have been optimising structures geometrically for centuries. The purpose of architectured materials is to build bridges across themicroscale ofmaterials and themacroscale of engineering structures, to put some geometry in the microstructure. This is a paradigm shift. Materials cannot be considered monolithic anymore. Any set of materials functions, even antagonistic ones, can be envisaged in the future. In this paper, we intend to demonstrate the pertinence of computation for developing architectured materials, and the not-so-incidental outcome which led us to developing large-scale additive manufacturing for architectural applications

Computational Homogenization of Architectured Materials

Architectured materials involve geometrically engineered distributions of microstructural phases at a scale comparable to the scale of the component, thus calling for new models in order to determine the effective properties of materials. The present chapter aims at providing such models, in the case of mechanical properties. As a matter of fact, one engineering challenge is to predict the effective properties of such materials; computational homogenization using finite element analysis is a powerful tool to do so. Homogenized behavior of architectured materials can thus be used in large structural computations, hence enabling the dissemination of architectured materials in the industry. Furthermore, computational homogenization is the basis for computational topology optimization which will give rise to the next generation of architectured materials. This chapter covers the computational homogenization of periodic architectured materials in elasticity and plasticity, as well as the homogenization and representativity of random architectured materials

Method to apply and visualize physical models associated to a land cover performed by CNN: A case study of vegetation and water cooling effect in Bangkok Thailand

International audienceConvolutional Neural Networks (CNNs) are useful tools to perform land cover analysis, in particular when working on large areas. The information extracted from these land cover analyses is useful in many practical applications and can be used as input data for physical models capable of simulating any type of phenomenon of interest. Models results can be used to support policy making and can be visualized on the associated remote sensing image to aid interpretation. A method covering these aspects and illustrated on a practical use case is proposed in this paper. The use case focuses on the cooling effect of vegetation and water in Bangkok. For this use case, a dedicated dataset of approximately 680,000 64 × 64 pixels tiles with a resolution of 0.15 m/pixel is created. Three CNN models are built and optimized to classify each of the following classes: city, vegetation and water. The validation of the models shows that, with the exception of water where the accuracy is only 84%, the other two classes have an accuracy of over 90%. Post-processing is performed on each prediction before aggregating these results to obtain the land cover. Vegetation and water cooling models, given in the literature, are successfully applied using the land cover and their effects are plotted on the associated satellite images. Results show that small areas of trees close to water have little influence on the cooling effect and that it is not efficient to plant isolated trees near a larger vegetation site. Through this study, the proposed tool has demonstrated its usefulness

Land cover classification through Convolutional Neur-al Network model assembly: A case study of a local rural area in Thailand

International audienceRecent Convolutional Neural Network (CNN) has shown great potential in image classification, segmentation and object detection. Land cover takes advantage of CNN development for a large type of applications as water management and urban growing. However, to perform a land cover with numerous features - classes in classical CNN terminology, CNN models require a significant number of layers and neurons, resulting in high computational costs. To address this problem, a methodology is proposed in this paper to build a land cover using the aggregation of several CNN models. The overall process is based on 7 steps. The first two steps are the dataset creation and arrangement in smaller dataset fit for the specific features to detect. Then, a CNN architecture is built and validated on each sub-dataset corresponding to each class. Post-processing is conducted on each prediction before assembling the results. In the last step, a data cleaning is performed, giving the final land cover. The land cover of a rural area in Thailand is performed as a demonstration of the method, using satellite images with a resolution of 0.15 m/pixel. A 5-class (buildings, crops, forests, roads, and wastelands) dataset is created, consisting of a total of 1 million tiles of 64 x 64 pixels. The prediction results using the developed CNN model show an accuracy greater than 90% for each class, except for the road class where the accuracy only reaches 72%. Post-processing is performed on each of the 5 predictions. Only the 4 best results are retained and assembled to obtain the land cover, which generally corresponds to buildings, crops, forests, and wastelands. This method enables to identify by substitution with improved accuracy the last class whose prediction is the least accurate, and which generally corresponds to roads due to their small width relative to the tile size. The proposed methodology to perform a land cover by aggregating the prediction of different CNN models is found to predict correctly the land cover of two areas, especially roads can be classified, demonstrating the usefulness of the approach

Diagnosis of the Fabrication Process of Sandwich Structures with Fiber Network Core with the help of Vibration Testing

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POST-IMPACT BEHAVIOR OF INSERT IN SANDWICH STRUCTURES

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POST-IMPACT BEHAVIOR OF INSERT IN SANDWICH STRUCTURES

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Experimental analysis of impact and pull-through after impact in insert in sandwich structures

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Impact et tenue résiduelle des inserts de structures sandwichs

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