Astrophysique et technologie aérospatiale

Several areas of technology and robotics research are being influenced by an increasing interest in AI. The space community has only recently begun to investigate artificial neural networks and deep learning techniques for space systems. The most important aspects of these topics for controlling, guiding, and navigating spacecraft dynamics will be discussed in this paper. In an effort to draw attention to the benefits and drawbacks of employing the most prevalent architectures of artificial neural networks and the training strategies that go along with them, we examine these components. Quantitative and qualitative metrics are used to compare and review particular system identification, control synthesis, and optical navigation applications of artificial neural networks. The end-to-end deep learning frameworks for spacecraft guidance, navigation, and control are presented in this overview, as are the hybrid approaches that combine neural techniques with conventional algorithms to boost their performance.

Aluminum lithium alloys (Al–Li) are gaining popularity for use in military applications and the aerospace industry due to the properties required by the presence of lithium, which provides a significant improvement in mechanical properties over conventional aluminum alloys. The departments of research and development are interested in making these alloys better, especially for the additive manufacturing process. As a result, the current focus is on the third generation of Al–Li because of its lower part quality than the first and second generations. This paper aims to provide an overview of the applications of Al–Li alloys, their carachetrization, precipitations, and the effects they have on mechanical properties and grain refinement. The various manufacturing procedures, approaches, and tests are then presented after thorough examination. This study also reviews the most recent Al–Li research conducted by scientists over the past few years on a variety of processes.