ParisTech

About: ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Residual stress. The organization has 1888 authors who have published 1965 publications receiving 55532 citations. The organization is also known as: Paris Institute of Technology & ParisTech Développement.

Direct Fabrication of a Ti-47Al-2Cr-2Nb Alloy by Selective Laser Melting and Direct Metal Deposition Processes

Abstract: A Ti-47Al-2Cr-2Nb (at.%) material was fabricated using two laser-based methods, “Selective Laser Melting” (SLM) and “Direct Metal Deposition” (DMD), for potential uses in aircraft jet engines. Experiments were conducted under controlled atmosphere by changing the processing parameters. Optimal parameters were searched for this relatively low ductility material to prevent cracking due to built-up residual stresses during fast cooling. It was observed that these non-equilibrium cooling conditions were fast enough to generate ultra fine and metastable structures exhibiting high microhardness values. Post heat-treatments were successfully used to restore homogeneous lamellar or duplex microstructures and to relieve the residual stresses. A comparison of these two methods is provided in terms of powder requirements and of process parameters to achieve noncracked structures and fully dense materials.

Morphological segmentation of FIB‐SEM data of highly porous media

Abstract: Summary Nanoporous materials play an important role in modern batteries as well as fuel cells. The materials microstructure needs to be analyzed as it determines the electrochemical properties. However, the microstructure is too fine to be resolved by microcomputed tomography. The method of choice to analyze the microstructure is focused ion beam nanotomography (FIB-SEM). However, the reconstruction of the porous 3D microstructure from FIB-SEM image data in general has been an unsolved problem so far. In this paper, we present a new method using morphological operations. First, features are extracted from the data. Subsequently, these features are combined to an initial segmentation, that is then refined by a constrained watershed transformation. We evaluate our method with synthetic data, generated by a simulation of the FIB-SEM imaging process. We compare the ground truth in the simulated data to the segmentation result. The new method is found to produce a much smaller error than existing techniques.

Laser-based vehicles tracking and classification using occlusion reasoning and confidence estimation

Abstract: In this paper, we present a robust approach for the detection, tracking and classification of multiple vehicles using a vehicle mounted laser scanner working independently in highways an urban centers. Our classification is based on different criteria: geometrical configuration, occlusion reasoning, sensor specifications and tracking information. The estimated confidence level is thus computed accounting the classification, the geometrical configuration and the tracking duration. Our system has been validated under various conditions (highways, urban centers) with three different laser scanners and proved is robustness on real data and with real time constraints.