In Additive Manufacturing, Directed Energy Deposition (DED) is a promising technology that gains a growing interest in industry. An essential feature of this process its rapid fabrication capability, even for large-size parts. However, generating good material deposition trajectories remain a huge challenge that CAM software often fail to correctly deal with. The KAM4AM project aims at developing a software for DED manufacturing, based on the proven Artificial Intelligence technology of Reinforced Learning, to get a learning and adaptive CAM solution. A list of study cases from industry will help to collect the typologies of parts as well as technical and scientific issues related to DED technology. This data, combined with research cases, will enable to define the objectives and the functions of the learning environment that needs to be created. The main research challenges are (1) to design a problem-independent reward system, based on expert rules of the DED domain, (2) to develop a phenomenological model of the DED process, fast enough for allowing the numerous iterations required for the learning process. A last step consists in a thorough test of the generated trajectories, followed by the integration of these trajectories into Esprit Additive software.
