Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Technological challenges >> Factory of the future incl. robotics and non destructive testing
2 proposition(s).

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Hybrid modeling for the simulation of ultrasonic inspection of laminate composite for the detection of inter-plies damages or weaknesses

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Acoustique



Factory of the future incl. robotics and non destructive testing (.pdf)

In the framework of the simulation of ultrasonic non-destructive techniques (UT), we consider to design specific simulation tools dedicated to laminate composites. These materials are nowadays widely used in the aeronautical field but show fragility under dynamic stresses such as impacts. Even for low-energy impacts, composite components can be weakened by localized damage, mainly by transverse cracking and delamination. Due to their anisotropic, heterogeneous and multi-layered properties, the development of UT methods for the inspection of such structures is very challenging. Numerical simulation is therefore useful, both for analysis and for the design and optimization of new UT techniques. Based on innovative numerical works, the aim of this study is to propose numerical methods dedicated to the simulation of new UT methods and in particular to the analysis of oblique incidence controls of realistic damage defects. For this, we will rely on existing developments recently done at CEA LIST based on the transient spectral element method, especially by using effective interface conditions to model a realistic delamination or local porosities in inter-plies matrix.

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Study of embedded prognostic strategies in wired networks based on temporal neural networks

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Fiabilité et Intégration Capteur



Factory of the future incl. robotics and non destructive testing (.pdf)

Whatever their fields of application, cables are very often victims of their operating environment. They often face aggressive conditions such as mechanical vibration, heat stress, moisture penetration, etc. These conditions favor the appearance of more or less serious defects ranging from a simple crack in the sheath to a cable break thus causing a malfunction of the system. In this context, the CEA LIST studies methods of diagnosis and prognosis of defects in cable networks based on the reflectometry method. The idea is to inject a test signal into the cable. Whenever it encounters an impedance discontinuity (i.e. a fault), some of its energy is returned to the injection point. The processing of the reflected signal subsequently makes it possible to detect and locate this defect. Despite the maturity of the reflectometry to detect a defect in a cable, it does not allow to determine the causes of the appearance of an incipient defect (ie damage of the shielding, radius of curvature, pinching, etc.) nor to predict its evolution in the future. The work of this thesis aims at developing new prognostic strategies for defects in wired networks. For this, the application of Machine Learning methods such as Artificial Neural Networks (ANN) on data from reflectometry sensors is a promising solution to solve this problem. It is in this context that the works of this thesis are inscribed.

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